diff --git a/.virtual_documents/notebooks/final_Zina.ipynb b/.virtual_documents/notebooks/final_Zina.ipynb
new file mode 100644
index 00000000..a14c99ef
--- /dev/null
+++ b/.virtual_documents/notebooks/final_Zina.ipynb
@@ -0,0 +1,712 @@
+import pandas as pd
+star1_df = pd.read_csv("/Users/ZINA/Desktop/one-star-michelin-restaurants.csv")
+star2_df = pd.read_csv("/Users/ZINA/Desktop/two-stars-michelin-restaurants.csv")
+star3_df = pd.read_csv("/Users/ZINA/Desktop/three-stars-michelin-restaurants.csv")
+
+
+# Drop unwanted columns
+star1_df.drop(columns=['zipCode'], inplace=True)
+
+
+star2_df.drop(columns=['zipCode'], inplace=True)
+
+
+star3_df.drop(columns=['zipCode'], inplace=True)
+
+
+# Creating a star column
+star1_df['stars'] = '1 star'
+star2_df['stars'] = '2 stars'
+star3_df['stars'] = '3 stars'
+
+
+# Concatenating the 3 datasets 
+stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)
+
+
+# Replace $$$$$ with $$$$
+stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)
+
+
+# Clean weird characters
+stars_df['price'] = stars_df['price'].str.strip()
+stars_df['price'] = stars_df['price'].str.replace(r'\s+', '', regex=True)
+
+
+# Convert $ to ordinal numbers
+stars_df['price_ordinal'] = stars_df['price'].str.count(r'\$')
+
+
+# Compute median ordinal per star group (1 star, 2 stars, 3 stars)
+median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()
+
+
+# Replace missing ordinal values using matching star median
+stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)
+
+
+# Now convert back to $ string after filling
+stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)
+
+
+# Define the mapping
+price_mean_map = {
+    "$": 20,
+    "$$": 37.5,
+    "$$$": 62.5,
+    "$$$$": 100
+}
+
+
+# Create a new column with the mean price
+stars_df['price_mean'] = stars_df['price'].map(price_mean_map)
+
+
+# checking if there are no null values in all price columns 
+stars_df.isnull().sum()
+
+
+# Drop columns
+stars_df.drop(columns=['latitude', 'longitude'], inplace=True)
+stars_df.head()
+
+
+#put in lower for joining for example 'creative' with 'Creative'
+stars_df["cuisine"] = stars_df["cuisine"].str.strip().str.lower() 
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+international_types = ["modern cuisine", "classic cuisine",
+                       "street food", "meats and grills", "international", "innovative"]
+
+stars_df["cuisine_original"] = stars_df["cuisine"]
+
+stars_df["cuisine"] = stars_df["cuisine"].replace(
+    international_types, "international cuisine")
+
+international_sub = stars_df[
+    stars_df["cuisine_original"].isin(international_types)]
+
+ax = international_sub["cuisine_original"].value_counts().plot(kind="bar")
+
+plt.title("Distribution of subtypes within international cuisine")
+plt.ylabel("Number of restaurants")
+plt.xticks(rotation=45, ha="right")
+
+note = ("Modern cuisine combines global flavours with local and seasonal ingredients, ""while innovative refers to more experimental concepts such as molecular ""gastronomy or 3D‑printed food.")
+
+plt.subplots_adjust(bottom=0.3)
+
+plt.figtext(
+    0.5,
+    0.02,
+    note,
+    ha="center",
+    va="bottom",
+    wrap=True,
+    fontsize=9
+)
+
+plt.show()
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+#joining all the food subcategories into a main attribute, in this case "Chinese food" 
+chinese_type = ["chinese", 
+    "cantonese",
+    "cantonese roast meats",
+    "dim sum",
+    "shanghainese",
+    "sichuan",
+    "hunanese and sichuan",
+    "sichuan-huai yang",
+    "fujian",
+    "taizhou",
+    "hang zhou",
+    "noodles and congee"]
+
+stars_df["cuisine"] = stars_df["cuisine"].replace(chinese_type, "chinese")
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+#joining all the food subcategories into a main attribute
+korean_types = ['korean',
+    'korean contemporary',
+    'temple cuisine']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+thai_types = ['thai',
+    'thai contemporary',
+    'southern thai']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+american_types = ['american',
+    'californian',
+    'barbecue',
+    'steakhouse']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+french_types = ['french',
+    'classic french',
+    'french contemporary',
+    'modern french',
+    'creative french']
+
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+japanese_types = ['japanese',
+    'sushi',
+    'teppanyaki',
+    'japanese contemporary']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+other_asian_types = ['asian',
+    'asian influences',
+    'asian contemporary',
+    'fusion','taiwanese','peranakan','thai']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+british_types = ['modern british',
+    'traditional british',
+    'creative british']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+modern_types = ['modern cuisine',
+    'modern','modern food']
+stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].unique() #confirm if that values joined
+
+
+market_types = ['classic cuisine','market cuisine', 'regional cuisine']
+stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+mediterranean_types = ['mediterranean', 'mediterranean cuisine']
+stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')
+
+
+stars_df['cuisine'].unique() #confirm if that values joined
+
+
+other_european_types = ['european', 'european contemporary','mediterranean food']
+stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+italian_types = ['italian', 'italian contemporary']
+stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].unique()
+
+
+international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']
+stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')
+
+
+scandinavian_types = ['danish','finnish', 'scandinavian']
+stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')
+
+
+stars_df['cuisine'].unique()
+
+
+stars_df['cuisine'].nunique()
+
+
+stars_df = stars_df.sort_values(by="price")  
+stars_df['price'].unique()
+
+
+stars_df['name'].nunique()
+
+
+
+stars_df.columns
+
+
+# Restaurant name standardization - lower case
+
+stars_df['name']= stars_df['name'].str.lower()
+print (stars_df['name'])
+
+
+#Trim Excessive Whitespaces:
+
+stars_df['name'] = stars_df['name'].astype(str)
+stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))
+
+print(stars_df.sample(5)) 
+
+
+# year check
+
+stars_df['year'].nunique()
+
+
+
+
+print(stars_df['year'])
+
+
+# city names check
+stars_df['city'].nunique()
+
+
+stars_df['city'].unique()
+
+
+#Remove Leading/Trailing Spaces
+
+stars_df['city'] = stars_df['city'].str.strip()
+print(stars_df['city'])
+
+
+#convert to lower case
+
+stars_df['city'] = stars_df['city'].str.lower() 
+print(stars_df['city'])
+
+
+#check for duplicates
+
+duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]
+print(duplicates)
+
+
+# order A–Z
+
+#stars_df = stars_df.sort_values(by="city")  
+#stars_df['city'].unique()
+
+
+
+
+#remove numbers and zip codes
+
+import re
+
+def clean_city_name(city_name):
+    if isinstance(city_name, str):  # Check if the input is a string
+        # Use regex to remove " - numbers" at the end of the string
+        return re.sub(r'\s-\s\d+$', '', city_name).strip()
+    return city_name  # Return as is if it's not a string
+
+
+stars_df['city'] = stars_df['city'].apply(clean_city_name)
+
+
+#verify results
+
+print(stars_df['city'].unique())  # Display unique city names to verify the cleaning
+
+
+#remove special characters 
+stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)
+
+
+stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)
+
+
+stars_df['city'] = stars_df['city'].str.title()
+
+
+#the city column are stripped of accents and are presented in ASCII format.
+
+import unidecode
+stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))
+
+
+#verify the results
+print(stars_df['city'].unique()) 
+
+
+#grouping suburbs into major city and add info in a new column 
+
+#create a dictionary 
+
+location_map = {
+    # London + neighborhoods
+    'north kensington': 'London',
+    'kensington': 'London',
+    'westminster': 'London',
+    'soho': 'London',
+    'mayfair': 'London',
+    'marylebone': 'London',
+    'chelsea': 'London',
+    'clapham common': 'London',
+    "regent's park": 'London',
+    'shoreditch': 'London',
+    'spitalfields': 'London',
+    'belgravia': 'London',
+    'bloomsbury': 'London',
+    'finsbury': 'London',
+    'fulham': 'London',
+    'chiswick': 'London',
+    'city centre': 'London',
+    'city of london': 'London',
+    'hyde park': 'London',
+    # San Francisco
+    'south san francisco': 'San Francisco',
+    # Ireland
+    'baile mhic andáin/thomastown': 'Thomastown',
+    'gaillimh/galway': 'Galway',
+    'cill chainnigh/kilkenny': 'Kilkenny',
+    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',
+    'athína': 'Athens',
+    'ballydehob': 'Ballydehob',
+    # Finland
+    'helsingfors / helsinki': 'Helsinki',
+    # Czech Republic
+    'praha': 'Prague',
+    # Austria
+    'wien': 'Vienna',
+    'salzburg': 'Salzburg',
+    # Menai Bridge
+    'menai bridge/porthaethwy': 'Menai Bridge',
+    # USA cities
+    'los angeles': 'Los Angeles',
+    'san diego': 'San Diego',
+    'sacramento': 'Sacramento',
+    'new york': 'New York',
+    'chicago': 'Chicago',
+    'costa mesa': 'Costa Mesa',
+    'monterey': 'Monterey',
+    'washington, d.c.': 'Washington D.C.',
+    'south dalton': 'Dalton',
+    # Asia
+    'bangkok': 'Bangkok',
+    'phuket': 'Phuket',
+    'hong kong': 'Hong Kong',
+    'taipei': 'Taipei',
+    'seoul': 'Seoul',
+    'singapore': 'Singapore',
+    'macau': 'Macau',
+    # Croatia
+    'lovran': 'Lovran',
+    'rovinj': 'Rovinj',
+    'zagreb': 'Zagreb',
+    'šibenik': 'Sibenik',
+    # Norway / Scandinavia
+    'stavanger': 'Stavanger',
+    'trondheim': 'Trondheim',
+    'oslo': 'Oslo',
+    'göteborg': 'Gothenburg',
+    'växjö': 'Vaxjo',
+    'skåne-tranås': 'Skane-Tranas',
+    'vejle': 'Vejle',
+    # Denmark
+    'fredericia': 'Fredericia',
+    'pedersker': 'Pedersker',
+    'præstø': 'Praesto',
+    # Sweden
+    'malmö': 'Malmo',
+    'stockholm': 'Stockholm',
+    # Portugal / Ireland / UK misc
+    'bath': 'Bath',
+    'bristol': 'Bristol',
+    'cambridge': 'Cambridge',
+    'cheltenham': 'Cheltenham',
+    'chester': 'Chester',
+    'birmingham': 'Birmingham',
+    'edinburgh': 'Edinburgh',
+    'leeds': 'Leeds',
+    'oxford': 'Oxford',
+    'stratford-upon-avon': 'Stratford-Upon-Avon',
+    'padstow': 'Padstow',
+    'torquay': 'Torquay',
+    'newcastle upon tyne': 'Newcastle upon Tyne',
+    'nottingham': 'Nottingham',
+    'bray': 'Bray',
+    'bowness-on-windermere': 'Bowness-on-Windermere',
+    'cartmel': 'Cartmel',
+    'castle combe': 'Castle Combe',
+    'chagford': 'Chagford',
+    'chew magna': 'Chew Magna',
+    'dalry': 'Dalry',
+    'dorking': 'Dorking',
+    'egham': 'Egham',
+    'fence': 'Fence',
+    'fordwich': 'Fordwich',
+    'grasmere': 'Grasmere',
+    'gravetye': 'Gravetye',
+    'great milton': 'Great Milton',
+    'hallwang': 'Hallwang',
+    'hampton in arden': 'Hampton in Arden',
+    'harome': 'Harome',
+    'henne': 'Henne',
+    'horsham': 'Horsham',
+    'hunstanton': 'Hunstanton',
+    'ilfracombe': 'Ilfracombe',
+    'järpen': 'Jarpen',
+    'kenilworth': 'Kenilworth',
+    'kew': 'Kew',
+    'kleinwalsertal': 'Kleinwalsertal',
+    'knowstone': 'Knowstone',
+    'langho': 'Langho',
+    'leith': 'Leith',
+    'leynar': 'Leynar',
+    'little dunmow': 'Little Dunmow',
+    'llanddewi skirrid': 'Llanddewi Skirrid',
+    'llandrillo': 'Llandrillo',
+    'lovran': 'Lovran',
+    'lympstone': 'Lympstone',
+    'machynlleth': 'Machynlleth',
+    'malmesbury': 'Malmesbury',
+    'marlow': 'Marlow',
+    'morston': 'Morston',
+    'mountsorrel': 'Mountsorrel',
+    'murcott': 'Murcott',
+    'newbury': 'Newbury',
+    'oldstead': 'Oldstead',
+    'peat inn': 'Peat Inn',
+    'penarth': 'Penarth',
+    'port isaac': 'Port Isaac',
+    'portscatho': 'Portscatho',
+    'ripley': 'Ripley',
+    'saint helier/saint-hélier': 'Saint Helier',
+    "saint james's": 'Saint James',
+    'seasalter': 'Seasalter',
+    'shinfield': 'Shinfield',
+    'summerhouse': 'Summerhouse',
+    'upper hambleton': 'Hambleton',
+    'victoria': 'Victoria',
+    'wandsworth': 'London',
+    'whitebrook': 'Whitebrook',
+    'winchester': 'Winchester',
+    'winteringham': 'Winteringham'
+}
+
+stars_df['major_city'] = stars_df['city'].replace(location_map)
+
+
+print(stars_df[['city', 'major_city']].sample(10))  
+
+
+stars_df['name'].unique
+
+
+stars_df.tail(200)
+
+
+stars_df.shape
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_theme(style="whitegrid")
+
+
+stars_df["cuisine"].value_counts().head(10).plot(kind="bar")
+plt.title("Top 10 types of cuisine")
+plt.xlabel("Cuisine")
+plt.ylabel("Nº of restaurants")
+plt.xticks(rotation=45, ha="right")
+plt.tight_layout()
+
+
+
+top_cuisines = (
+    stars_df["cuisine"].value_counts()
+    .head(10)
+    .index
+)
+
+plt.figure(figsize=(8,4))
+sns.countplot(
+    data=stars_df[stars_df["cuisine"].isin(top_cuisines)],
+    y="cuisine",
+    order=top_cuisines
+)
+plt.title("Top 10 types of cuisine")
+plt.xlabel("Nº of restaurants")
+plt.ylabel("Cuisine")
+plt.tight_layout()
+
+
+stars_df["stars_n"] = stars_df["stars"].str[0].astype(int)
+
+stars_df.groupby("region")["stars_n"].mean().sort_values().plot(kind="bar")
+plt.title("Mean of Stars per region")
+plt.xlabel("Region")
+plt.ylabel("Mean of stars")
+plt.xticks(rotation=45, ha="right")
+plt.tight_layout()
+
+
+
+
+#visuals: 
+#which are the cities with more restaurants? 
+#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?
+# which are the cuisine dominating a city /region 
+# avg price point in a specific city based on restaurants?
+# time series 2018 vs 2019 any trend? any star restautant grew over past year? 
+# cheapest vs most expensive cuisine? 
+
+
+
+import requests
+import time
+import numpy as np
+
+API_KEY = "AIzaSyC8VCFpZ1WhNUegfd5ziwZPVRCe1pi35lo"
+
+def get_place_rating(name, city, api_key=API_KEY, sleep_sec=0.2):
+    query = f"{name}, {city}"
+    url_search = "https://maps.googleapis.com/maps/api/place/findplacefromtext/json"
+    params_search = {
+        "input": query,
+        "inputtype": "textquery",
+        "fields": "place_id",
+        "key": api_key
+    }
+    r = requests.get(url_search, params=params_search)
+    data = r.json()
+    status = data.get("status")
+    print("SEARCH:", query, "->", status)
+    if status != "OK":
+        return None, None
+
+    place_id = data["candidates"][0]["place_id"]
+
+    url_details = "https://maps.googleapis.com/maps/api/place/details/json"
+    params_details = {
+        "place_id": place_id,
+        "fields": "rating,user_ratings_total",
+        "key": api_key
+    }
+    d = requests.get(url_details, params=params_details)
+    det = d.json()
+    d_status = det.get("status")
+    print("DETAILS:", place_id, "->", d_status)
+    if d_status != "OK":
+        return None, None
+
+    time.sleep(sleep_sec)
+    result = det.get("result", {})
+    return result.get("rating"), result.get("user_ratings_total")
+
+
+# inicializar colunas (se ainda não existirem)
+if "Review_rating" not in stars_df.columns:
+    stars_df["Review_rating"] = np.nan
+if "Review_count" not in stars_df.columns:
+    stars_df["Review_count"] = np.nan
+
+# lista de restaurantes (ou partes do nome) que queres atualizar
+target_names = ["eleven madison park", "per se", "chef's table at brooklyn fare"]  # podes editar/expandir
+
+for idx, row in stars_df.iterrows():
+    name_lower = str(row["name"]).lower()
+
+    # verifica se algum dos nomes alvo aparece no name da linha
+    if any(tn.lower() in name_lower for tn in target_names):
+        rating, count = get_place_rating(row["name"], row["city"])
+        stars_df.at[idx, "Review_rating"] = rating
+        stars_df.at[idx, "Review_count"] = count
+    # os outros restaurantes ficam como estão (NaN ou valores antigos)
+
+
+
+
+
+stars_df = stars_df.sort_values(by="name", ascending=True).reset_index(drop=True)
+
+
+stars_df = stars_df.sort_values(by="name", ascending=True).reset_index(drop=True)
+stars_df['name'].unique()
+
+
+rest_names = ["per se", "eleven madison park","chef's table at brooklyn fare"]
+
+mask = stars_df["name"].str.strip().str.lower().isin(
+    [n.strip().lower() for n in rest_names]
+)
+linha_restaurante = stars_df.loc[mask, :]
+
+print(linha_restaurante)
+print(stars_df.loc[mask, "Review_rating"])
+
+
+
+
+stars_df.head(5)
+
+
+stars_df.columns
+
+
+import pandas as pd
+
+pd.set_option("display.max_columns", None)  # mostra todas as colunas
+stars_df.head()                            # ou stars_df.sample(5)
+
+
+
+
diff --git a/.virtual_documents/notebooks/final_pedro.ipynb b/.virtual_documents/notebooks/final_pedro.ipynb
new file mode 100644
index 00000000..7bffe64f
--- /dev/null
+++ b/.virtual_documents/notebooks/final_pedro.ipynb
@@ -0,0 +1,758 @@
+import pandas as pd
+star1_df = pd.read_csv(r'C:\Users\quint\Desktop\IronHack\WEEKS\WEEK4\project\one-star-michelin-restaurants.csv')
+star2_df = pd.read_csv(r'C:\Users\quint\Desktop\IronHack\WEEKS\WEEK4\project\two-stars-michelin-restaurants.csv')
+star3_df = pd.read_csv(r'C:\Users\quint\Desktop\IronHack\WEEKS\WEEK4\project\three-stars-michelin-restaurants.csv')
+
+
+
+# Drop unwanted columns
+star1_df.drop(columns=['zipCode'], inplace=True)
+
+
+star2_df.drop(columns=['zipCode'], inplace=True)
+
+
+star3_df.drop(columns=['zipCode'], inplace=True)
+
+
+# Creating a star column
+star1_df['stars'] = '1 star'
+star2_df['stars'] = '2 stars'
+star3_df['stars'] = '3 stars'
+
+
+# Concatenating the 3 datasets 
+stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)
+
+
+# Replace $$$$$ with $$$$
+stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)
+
+
+# Clean weird characters
+stars_df['price'] = stars_df['price'].str.strip()
+stars_df['price'] = stars_df['price'].str.replace(r'\s+', '', regex=True)
+
+
+# Convert $ to ordinal numbers
+stars_df['price_ordinal'] = stars_df['price'].str.count(r'\$')
+
+
+# Compute median ordinal per star group (1 star, 2 stars, 3 stars)
+median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()
+
+
+# Replace missing ordinal values using matching star median
+stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)
+
+
+# Now convert back to $ string after filling
+stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)
+
+
+# Define the mapping
+price_mean_map = {
+    "$": 20,
+    "$$": 37.5,
+    "$$$": 62.5,
+    "$$$$": 100
+}
+
+
+# Create a new column with the mean price
+stars_df['price_mean'] = stars_df['price'].map(price_mean_map)
+
+
+# checking if there are no null values in all price columns 
+stars_df.isnull().sum()
+
+
+# Drop columns
+stars_df.drop(columns=['latitude', 'longitude'], inplace=True)
+stars_df.head()
+
+
+#put in lower for joining for example 'creative' with 'Creative'
+stars_df["cuisine"] = stars_df["cuisine"].str.strip().str.lower() 
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+international_types = ["modern cuisine", "classic cuisine",
+                       "street food", "meats and grills", "international", "innovative"]
+
+stars_df["cuisine_original"] = stars_df["cuisine"]
+
+stars_df["cuisine"] = stars_df["cuisine"].replace(
+    international_types, "international cuisine")
+
+international_sub = stars_df[
+    stars_df["cuisine_original"].isin(international_types)]
+
+ax = international_sub["cuisine_original"].value_counts().plot(kind="bar")
+
+plt.title("Distribution of subtypes within international cuisine")
+plt.ylabel("Number of restaurants")
+plt.xticks(rotation=45, ha="right")
+
+note = ("Modern cuisine combines global flavours with local and seasonal ingredients, ""while innovative refers to more experimental concepts such as molecular ""gastronomy or 3D‑printed food.")
+
+plt.subplots_adjust(bottom=0.3)
+
+plt.figtext(
+    0.5,
+    0.02,
+    note,
+    ha="center",
+    va="bottom",
+    wrap=True,
+    fontsize=9
+)
+
+plt.show()
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+#joining all the food subcategories into a main attribute, in this case "Chinese food" 
+chinese_type = ["chinese", 
+    "cantonese",
+    "cantonese roast meats",
+    "dim sum",
+    "shanghainese",
+    "sichuan",
+    "hunanese and sichuan",
+    "sichuan-huai yang",
+    "fujian",
+    "taizhou",
+    "hang zhou",
+    "noodles and congee"]
+
+stars_df["cuisine"] = stars_df["cuisine"].replace(chinese_type, "chinese")
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+#joining all the food subcategories into a main attribute
+korean_types = ['korean',
+    'korean contemporary',
+    'temple cuisine']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+thai_types = ['thai',
+    'thai contemporary',
+    'southern thai']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+american_types = ['american',
+    'californian',
+    'barbecue',
+    'steakhouse']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+french_types = ['french',
+    'classic french',
+    'french contemporary',
+    'modern french',
+    'creative french']
+
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+japanese_types = ['japanese',
+    'sushi',
+    'teppanyaki',
+    'japanese contemporary']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+other_asian_types = ['asian',
+    'asian influences',
+    'asian contemporary',
+    'fusion','taiwanese','peranakan','thai']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+british_types = ['modern british',
+    'traditional british',
+    'creative british']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+modern_types = ['modern cuisine',
+    'modern','modern food']
+stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].unique() #confirm if that values joined
+
+
+market_types = ['classic cuisine','market cuisine', 'regional cuisine']
+stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+mediterranean_types = ['mediterranean', 'mediterranean cuisine']
+stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')
+
+
+stars_df['cuisine'].unique() #confirm if that values joined
+
+
+other_european_types = ['european', 'european contemporary','mediterranean food']
+stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+italian_types = ['italian', 'italian contemporary']
+stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].unique()
+
+
+international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']
+stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')
+
+
+scandinavian_types = ['danish','finnish', 'scandinavian']
+stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')
+
+
+stars_df['cuisine'].unique()
+
+
+stars_df['cuisine'].nunique()
+
+
+stars_df = stars_df.sort_values(by="price")  
+stars_df['price'].unique()
+
+
+stars_df['name'].nunique()
+
+
+
+stars_df.columns
+
+
+# Restaurant name standardization - lower case
+
+stars_df['name']= stars_df['name'].str.lower()
+print (stars_df['name'])
+
+
+#Trim Excessive Whitespaces:
+
+stars_df['name'] = stars_df['name'].astype(str)
+stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))
+
+print(stars_df.sample(5)) 
+
+
+# year check
+
+stars_df['year'].nunique()
+
+
+
+
+print(stars_df['year'])
+
+
+# city names check
+stars_df['city'].nunique()
+
+
+stars_df['city'].unique()
+
+
+#Remove Leading/Trailing Spaces
+
+stars_df['city'] = stars_df['city'].str.strip()
+print(stars_df['city'])
+
+
+#convert to lower case
+
+stars_df['city'] = stars_df['city'].str.lower() 
+print(stars_df['city'])
+
+
+#check for duplicates
+
+duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]
+print(duplicates)
+
+
+# order A–Z
+
+#stars_df = stars_df.sort_values(by="city")  
+#stars_df['city'].unique()
+
+
+
+
+#remove numbers and zip codes
+
+import re
+
+def clean_city_name(city_name):
+    if isinstance(city_name, str):  # Check if the input is a string
+        # Use regex to remove " - numbers" at the end of the string
+        return re.sub(r'\s-\s\d+$', '', city_name).strip()
+    return city_name  # Return as is if it's not a string
+
+
+stars_df['city'] = stars_df['city'].apply(clean_city_name)
+
+
+#verify results
+
+print(stars_df['city'].unique())  # Display unique city names to verify the cleaning
+
+
+#remove special characters 
+stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)
+
+
+stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)
+
+
+stars_df['city'] = stars_df['city'].str.title()
+
+
+#the city column are stripped of accents and are presented in ASCII format.
+
+import unidecode
+stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))
+
+
+#verify the results
+print(stars_df['city'].unique()) 
+
+
+#grouping suburbs into major city and add info in a new column 
+
+#create a dictionary 
+
+location_map = {
+    # London + neighborhoods
+    'north kensington': 'London',
+    'kensington': 'London',
+    'westminster': 'London',
+    'soho': 'London',
+    'mayfair': 'London',
+    'marylebone': 'London',
+    'chelsea': 'London',
+    'clapham common': 'London',
+    "regent's park": 'London',
+    'shoreditch': 'London',
+    'spitalfields': 'London',
+    'belgravia': 'London',
+    'bloomsbury': 'London',
+    'finsbury': 'London',
+    'fulham': 'London',
+    'chiswick': 'London',
+    'city centre': 'London',
+    'city of london': 'London',
+    'hyde park': 'London',
+    # San Francisco
+    'south san francisco': 'San Francisco',
+    # Ireland
+    'baile mhic andáin/thomastown': 'Thomastown',
+    'gaillimh/galway': 'Galway',
+    'cill chainnigh/kilkenny': 'Kilkenny',
+    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',
+    'athína': 'Athens',
+    'ballydehob': 'Ballydehob',
+    # Finland
+    'helsingfors / helsinki': 'Helsinki',
+    # Czech Republic
+    'praha': 'Prague',
+    # Austria
+    'wien': 'Vienna',
+    'salzburg': 'Salzburg',
+    # Menai Bridge
+    'menai bridge/porthaethwy': 'Menai Bridge',
+    # USA cities
+    'los angeles': 'Los Angeles',
+    'san diego': 'San Diego',
+    'sacramento': 'Sacramento',
+    'new york': 'New York',
+    'chicago': 'Chicago',
+    'costa mesa': 'Costa Mesa',
+    'monterey': 'Monterey',
+    'washington, d.c.': 'Washington D.C.',
+    'south dalton': 'Dalton',
+    # Asia
+    'bangkok': 'Bangkok',
+    'phuket': 'Phuket',
+    'hong kong': 'Hong Kong',
+    'taipei': 'Taipei',
+    'seoul': 'Seoul',
+    'singapore': 'Singapore',
+    'macau': 'Macau',
+    # Croatia
+    'lovran': 'Lovran',
+    'rovinj': 'Rovinj',
+    'zagreb': 'Zagreb',
+    'šibenik': 'Sibenik',
+    # Norway / Scandinavia
+    'stavanger': 'Stavanger',
+    'trondheim': 'Trondheim',
+    'oslo': 'Oslo',
+    'göteborg': 'Gothenburg',
+    'växjö': 'Vaxjo',
+    'skåne-tranås': 'Skane-Tranas',
+    'vejle': 'Vejle',
+    # Denmark
+    'fredericia': 'Fredericia',
+    'pedersker': 'Pedersker',
+    'præstø': 'Praesto',
+    # Sweden
+    'malmö': 'Malmo',
+    'stockholm': 'Stockholm',
+    # Portugal / Ireland / UK misc
+    'bath': 'Bath',
+    'bristol': 'Bristol',
+    'cambridge': 'Cambridge',
+    'cheltenham': 'Cheltenham',
+    'chester': 'Chester',
+    'birmingham': 'Birmingham',
+    'edinburgh': 'Edinburgh',
+    'leeds': 'Leeds',
+    'oxford': 'Oxford',
+    'stratford-upon-avon': 'Stratford-Upon-Avon',
+    'padstow': 'Padstow',
+    'torquay': 'Torquay',
+    'newcastle upon tyne': 'Newcastle upon Tyne',
+    'nottingham': 'Nottingham',
+    'bray': 'Bray',
+    'bowness-on-windermere': 'Bowness-on-Windermere',
+    'cartmel': 'Cartmel',
+    'castle combe': 'Castle Combe',
+    'chagford': 'Chagford',
+    'chew magna': 'Chew Magna',
+    'dalry': 'Dalry',
+    'dorking': 'Dorking',
+    'egham': 'Egham',
+    'fence': 'Fence',
+    'fordwich': 'Fordwich',
+    'grasmere': 'Grasmere',
+    'gravetye': 'Gravetye',
+    'great milton': 'Great Milton',
+    'hallwang': 'Hallwang',
+    'hampton in arden': 'Hampton in Arden',
+    'harome': 'Harome',
+    'henne': 'Henne',
+    'horsham': 'Horsham',
+    'hunstanton': 'Hunstanton',
+    'ilfracombe': 'Ilfracombe',
+    'järpen': 'Jarpen',
+    'kenilworth': 'Kenilworth',
+    'kew': 'Kew',
+    'kleinwalsertal': 'Kleinwalsertal',
+    'knowstone': 'Knowstone',
+    'langho': 'Langho',
+    'leith': 'Leith',
+    'leynar': 'Leynar',
+    'little dunmow': 'Little Dunmow',
+    'llanddewi skirrid': 'Llanddewi Skirrid',
+    'llandrillo': 'Llandrillo',
+    'lovran': 'Lovran',
+    'lympstone': 'Lympstone',
+    'machynlleth': 'Machynlleth',
+    'malmesbury': 'Malmesbury',
+    'marlow': 'Marlow',
+    'morston': 'Morston',
+    'mountsorrel': 'Mountsorrel',
+    'murcott': 'Murcott',
+    'newbury': 'Newbury',
+    'oldstead': 'Oldstead',
+    'peat inn': 'Peat Inn',
+    'penarth': 'Penarth',
+    'port isaac': 'Port Isaac',
+    'portscatho': 'Portscatho',
+    'ripley': 'Ripley',
+    'saint helier/saint-hélier': 'Saint Helier',
+    "saint james's": 'Saint James',
+    'seasalter': 'Seasalter',
+    'shinfield': 'Shinfield',
+    'summerhouse': 'Summerhouse',
+    'upper hambleton': 'Hambleton',
+    'victoria': 'Victoria',
+    'wandsworth': 'London',
+    'whitebrook': 'Whitebrook',
+    'winchester': 'Winchester',
+    'winteringham': 'Winteringham'
+}
+
+stars_df['major_city'] = stars_df['city'].replace(location_map)
+
+
+print(stars_df[['city', 'major_city']].sample(10))  
+
+
+stars_df['name'].unique
+
+
+stars_df.tail(200)
+
+
+stars_df.shape
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_theme(style="whitegrid")
+
+
+stars_df["cuisine"].value_counts().head(10).plot(kind="bar")
+plt.title("Top 10 types of cuisine")
+plt.xlabel("Cuisine")
+plt.ylabel("Nº of restaurants")
+plt.xticks(rotation=45, ha="right")
+plt.tight_layout()
+
+
+
+top_cuisines = (
+    stars_df["cuisine"].value_counts()
+    .head(10)
+    .index
+)
+
+plt.figure(figsize=(8,4))
+sns.countplot(
+    data=stars_df[stars_df["cuisine"].isin(top_cuisines)],
+    y="cuisine",
+    order=top_cuisines
+)
+plt.title("Top 10 types of cuisine")
+plt.xlabel("Nº of restaurants")
+plt.ylabel("Cuisine")
+plt.tight_layout()
+
+
+stars_df["stars_n"] = stars_df["stars"].str[0].astype(int)
+
+stars_df.groupby("region")["stars_n"].mean().sort_values().plot(kind="bar")
+plt.title("Mean of Stars per region")
+plt.xlabel("Region")
+plt.ylabel("Mean of stars")
+plt.xticks(rotation=45, ha="right")
+plt.tight_layout()
+
+
+
+
+#visuals: 
+#which are the cities with more restaurants? 
+#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?
+# which are the cuisine dominating a city /region 
+# avg price point in a specific city based on restaurants?
+# time series 2018 vs 2019 any trend? any star restautant grew over past year? 
+# cheapest vs most expensive cuisine? 
+
+
+
+import requests
+import time
+import numpy as np
+
+API_KEY = "AIzaSyC8VCFpZ1WhNUegfd5ziwZPVRCe1pi35lo"
+
+def get_place_rating(name, city, api_key=API_KEY, sleep_sec=0.2):
+    query = f"{name}, {city}"
+    url_search = "https://maps.googleapis.com/maps/api/place/findplacefromtext/json"
+    params_search = {
+        "input": query,
+        "inputtype": "textquery",
+        "fields": "place_id",
+        "key": api_key
+    }
+    r = requests.get(url_search, params=params_search)
+    data = r.json()
+    status = data.get("status")
+    print("SEARCH:", query, "->", status)
+    if status != "OK":
+        return None, None
+
+    place_id = data["candidates"][0]["place_id"]
+
+    url_details = "https://maps.googleapis.com/maps/api/place/details/json"
+    params_details = {
+        "place_id": place_id,
+        "fields": "rating,user_ratings_total",
+        "key": api_key
+    }
+    d = requests.get(url_details, params=params_details)
+    det = d.json()
+    d_status = det.get("status")
+    print("DETAILS:", place_id, "->", d_status)
+    if d_status != "OK":
+        return None, None
+
+    time.sleep(sleep_sec)
+    result = det.get("result", {})
+    return result.get("rating"), result.get("user_ratings_total")
+
+
+# inicializar colunas (se ainda não existirem)
+if "Review_rating" not in stars_df.columns:
+    stars_df["Review_rating"] = np.nan
+if "Review_count" not in stars_df.columns:
+    stars_df["Review_count"] = np.nan
+
+# lista de restaurantes (ou partes do nome) que queres atualizar
+target_names = ["eleven madison park", "per se", "chef's table at brooklyn fare"]  # podes editar/expandir
+
+for idx, row in stars_df.iterrows():
+    name_lower = str(row["name"]).lower()
+
+    # verifica se algum dos nomes alvo aparece no name da linha
+    if any(tn.lower() in name_lower for tn in target_names):
+        rating, count = get_place_rating(row["name"], row["city"])
+        stars_df.at[idx, "Review_rating"] = rating
+        stars_df.at[idx, "Review_count"] = count
+
+
+
+
+
+
+stars_df = stars_df.sort_values(by="name", ascending=True).reset_index(drop=True)
+
+
+stars_df = stars_df.sort_values(by="name", ascending=True).reset_index(drop=True)
+stars_df['name'].unique()
+
+
+rest_names = ["per se", "eleven madison park","chef's table at brooklyn fare"]
+
+mask = stars_df["name"].str.strip().str.lower().isin(
+    [n.strip().lower() for n in rest_names]
+)
+linha_restaurante = stars_df.loc[mask, :]
+
+print(linha_restaurante)
+print(stars_df.loc[mask, "Review_rating"])
+
+
+
+
+stars_df.head(5)
+
+
+stars_df.columns
+
+
+import pandas as pd
+
+pd.set_option("display.max_columns", None)  # mostra todas as colunas
+stars_df.head()                            # ou stars_df.sample(5)
+
+
+
+<<<<<<< HEAD
+import pandas as pd
+
+pd.set_option("display.max_columns", None)  # mostra todas as colunas
+stars_df.head()                            # ou stars_df.sample(5)
+
+
+
+names = [
+    "eleven madison park",
+    "per se",
+    "chef's table at brooklyn fare"
+]
+df_reviews = stars_df[stars_df["name"].isin(names)][["name", "Review_rating", "Review_count"]]
+df_reviews
+
+
+names = [
+    "eleven madison park",
+    "per se",
+    "chef's table at brooklyn fare"
+]
+df_reviews = stars_df[stars_df["name"].isin(names)][["name", "Review_rating", "Review_count"]]
+df_reviews
+
+
+
+=======
+import numpy as np
+import pymysql
+from sqlalchemy import create_engine
+import getpass  # To get the password without showing the input
+password = getpass.getpass()
+
+
+
+stars_df.to_csv("stars_df.csv", index=False, encoding="utf-8")
+
+
+
+stars_df.to_csv("stars_df.csv", index=False, encoding="utf-8")
+
+>>>>>>> Pedro
+
+
+
diff --git a/README.md b/README.md
index f637438f..3ab83df5 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,21 @@
 # Project overview
-...
+This project analyzes **global Michelin-starred restaurants** (1, 2, and 3 stars) to understand patterns in:
+- Cuisine type vs Michelin star level
+- Geographic distribution across regions and major cities
+- Price trends by star rating
+- Public reviews and their alignment with Michelin ratings
+
+The datasets were concatenated, cleaned, and enriched with:
+- Average price (`price_mean`)
+- Review rating and count metrics
 
 # Installation
 
 1. **Clone the repository**:
 
 ```bash
-git clone https://github.com/YourUsername/repository_name.git
+git clone https://github.com/ArrimachNasser/first_project.git
+
 ```
 
 2. **Install UV**
@@ -56,22 +65,47 @@ uv pip install -r requirements.txt
 ```
 
 # Questions 
-...
+1. Do certain cuisines have a higher probability of receiving 3 stars?
+2. Which regions host the most starred restaurants?
+3. How does the average price vary between 1, 2, and 3 stars?
+4. Do public reviews align with Michelin star ratings?
 
 # Dataset 
-...
+We used three primary datasets:
+- `one-star-michelin-restaurants.csv`
+- `two-stars-michelin-restaurants.csv`
+- `three-stars-michelin-restaurants.csv`
 
-## Main dataset issues
+**Additional sources / enrichment**:
+- MICHELIN Guide for The restaurants' price ranges index
+- Online Review information using API 
 
-- ...
-- ...
-- ...
+## Main dataset issues
+- Missing values in `price` column for some restaurants
+- Inconsistent cuisine naming (e.g., "French cuisine" vs "French")
+- Some restaurants incorrectly had five dollar symbols ($$$$$)
+- Special characters in restaurant names
 
 ## Solutions for the dataset issues
-...
-
-# Conclussions
-...
+- Imputed missing prices with the **median price** per star level
+- Normalized cuisine names by lowercase, stripping spaces, and mapping variants to a single category
+- Removed duplicates based on `name + city`
+- Created a `major_city` flag using top city populations
+- Special Characters: Used 'unidecode' to clean restaurant names of accents and special characters
+- Inconsistent cuisine naming: Reduced over 100 cuisine types into 10 coherent groups
+- Geographic Organization: Grouped and ordered suburbs by city and region for clarity
+- Symbol Inconsistencies: Fixed entries with too many $ symbols, removed extra spaces and hidden characters
+- Handeling Missing Prices: Filled missing values using per star category.
+
+# Conclusions
+- New York, Hong Kong, and San Francisco are major culinary and cultural hubs that attract Michelin-star.
+- New York leads in 1-star and 2-star Michelin restaurants, offering a diverse array of high-quality dining.
+- Hong Kong, excels in 3-star Michelin establishments, indicating a focus on elite dining experiences.
+- Among 3-star Michelin restaurants, the dominant cuisines are contemporary, French, and Chinese.
+- Average price increases with star level, most 3-star restaurants tend to be the most expensive
+- Among all cuisines, Austrian cuisine is the most expensive on average.
 
 # Next steps
-...
+- Predict Michelin star level using machine learning
+- Include chef profiles for deeper insights
+- Analyze yearly trends using the `year` column
diff --git a/README_Chiara.md b/README_Chiara.md
new file mode 100644
index 00000000..f637438f
--- /dev/null
+++ b/README_Chiara.md
@@ -0,0 +1,77 @@
+# Project overview
+...
+
+# Installation
+
+1. **Clone the repository**:
+
+```bash
+git clone https://github.com/YourUsername/repository_name.git
+```
+
+2. **Install UV**
+
+If you're a MacOS/Linux user type:
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+```
+
+If you're a Windows user open an Anaconda Powershell Prompt and type :
+
+```bash
+powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
+```
+
+3. **Create an environment**
+
+```bash
+uv venv 
+```
+
+3. **Activate the environment**
+
+If you're a MacOS/Linux user type (if you're using a bash shell):
+
+```bash
+source ./venv/bin/activate
+```
+
+If you're a MacOS/Linux user type (if you're using a csh/tcsh shell):
+
+```bash
+source ./venv/bin/activate.csh
+```
+
+If you're a Windows user type:
+
+```bash
+.\venv\Scripts\activate
+```
+
+4. **Install dependencies**:
+
+```bash
+uv pip install -r requirements.txt
+```
+
+# Questions 
+...
+
+# Dataset 
+...
+
+## Main dataset issues
+
+- ...
+- ...
+- ...
+
+## Solutions for the dataset issues
+...
+
+# Conclussions
+...
+
+# Next steps
+...
diff --git a/anaconda_projects/db/project_filebrowser.db b/anaconda_projects/db/project_filebrowser.db
new file mode 100644
index 00000000..e5ae8f7b
Binary files /dev/null and b/anaconda_projects/db/project_filebrowser.db differ
diff --git a/data/clean/.virtual_documents/allstars_pedro.ipynb b/data/clean/.virtual_documents/allstars_pedro.ipynb
new file mode 100644
index 00000000..49d928d7
--- /dev/null
+++ b/data/clean/.virtual_documents/allstars_pedro.ipynb
@@ -0,0 +1,322 @@
+import pandas as pd
+star1_df = pd.read_csv(r'C:\Users\quint\Desktop\IronHack\WEEKS\WEEK4\project\one-star-michelin-restaurants.csv')
+star2_df = pd.read_csv(r'C:\Users\quint\Desktop\IronHack\WEEKS\WEEK4\project\two-stars-michelin-restaurants.csv')
+star3_df = pd.read_csv(r'C:\Users\quint\Desktop\IronHack\WEEKS\WEEK4\project\three-stars-michelin-restaurants.csv')
+
+star3_df.shape
+
+
+
+
+
+star1_df.tail()
+
+
+star2_df.head()
+
+
+star1_df['stars'] = '1 star'
+star2_df['stars'] = '2 stars'
+star3_df['stars'] = '3 stars'
+
+
+#Drop unwanted columns
+star1_df.drop(columns=['zipCode'], inplace=True)
+star1_df.head()
+
+
+star2_df.drop(columns=['zipCode'], inplace=True)
+star2_df.head()
+
+
+
+star3_df.drop(columns=['zipCode'], inplace=True)
+star3_df.head()
+
+
+stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)
+
+
+stars_df.tail()
+
+
+stars_df.tail()
+
+
+stars_df['year'].nunique()
+
+
+stars_df.drop(columns=['latitude', 'longitude'], inplace=True)
+stars_df.head()
+
+
+stars_df['cuisine'].unique()
+
+
+#put in lower for joining for example 'creative' with 'Creative'
+stars_df["cuisine"] = stars_df["cuisine"].str.strip().str.lower() 
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+international_types = ["modern cuisine", "classic cuisine",
+                       "street food", "meats and grills", "international", "innovative"]
+
+stars_df["cuisine_original"] = stars_df["cuisine"]
+
+stars_df["cuisine"] = stars_df["cuisine"].replace(
+    international_types, "international cuisine")
+
+international_sub = stars_df[
+    stars_df["cuisine_original"].isin(international_types)]
+
+ax = international_sub["cuisine_original"].value_counts().plot(kind="bar")
+
+plt.title("Distribution of subtypes within international cuisine")
+plt.ylabel("Number of restaurants")
+plt.xticks(rotation=45, ha="right")
+
+note = ("Modern cuisine combines global flavours with local and seasonal ingredients, ""while innovative refers to more experimental concepts such as molecular ""gastronomy or 3D‑printed food.")
+
+plt.subplots_adjust(bottom=0.3)
+
+plt.figtext(
+    0.5,
+    0.02,
+    note,
+    ha="center",
+    va="bottom",
+    wrap=True,
+    fontsize=9
+)
+
+plt.show()
+
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+#joining all the food subcategories into a main attribute, in this case "Chinese food" 
+chinese_type = ["chinese", 
+    "cantonese",
+    "cantonese roast meats",
+    "dim sum",
+    "shanghainese",
+    "sichuan",
+    "hunanese and sichuan",
+    "sichuan-huai yang",
+    "fujian",
+    "taizhou",
+    "hang zhou",
+    "noodles and congee"]
+
+stars_df["cuisine"] = stars_df["cuisine"].replace(chinese_type, "chinese")
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+#joining all the food subcategories into a main attribute
+korean_types = ['korean',
+    'korean contemporary',
+    'temple cuisine']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+thai_types = ['thai',
+    'thai contemporary',
+    'southern thai']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+american_types = ['american',
+    'californian',
+    'barbecue',
+    'steakhouse']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+french_types = ['french',
+    'classic french',
+    'french contemporary',
+    'modern french',
+    'creative french']
+
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+japanese_types = ['japanese',
+    'sushi',
+    'teppanyaki',
+    'japanese contemporary']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+other_asian_types = ['asian',
+    'asian influences',
+    'asian contemporary',
+    'fusion','taiwanese','peranakan','thai']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+british_types = ['modern british',
+    'traditional british',
+    'creative british']
+
+stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')
+
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+modern_types = ['modern cuisine',
+    'modern','modern food']
+stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].unique() #confirm if that values joined
+
+
+market_types = ['classic cuisine','market cuisine', 'regional cuisine']
+stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+mediterranean_types = ['mediterranean', 'mediterranean cuisine']
+stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')
+
+
+stars_df['cuisine'].unique() #confirm if that values joined
+
+
+other_european_types = ['european', 'european contemporary','mediterranean food']
+stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+italian_types = ['italian', 'italian contemporary']
+stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].nunique() #confirm if that values joined
+
+
+stars_df['cuisine'].unique()
+
+
+international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']
+stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')
+
+
+scandinavian_types = ['danish','finnish', 'scandinavian']
+stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')
+
+
+stars_df['cuisine'].unique()
+
+
+stars_df['cuisine'].nunique()
+
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+sns.set_theme(style="whitegrid")
+
+
+
+stars_df["cuisine"].value_counts().head(10).plot(kind="bar")
+plt.title("Top 10 types of cuisine")
+plt.xlabel("Cuisine")
+plt.ylabel("Nº of restaurants")
+plt.xticks(rotation=45, ha="right")
+plt.tight_layout()
+
+
+
+top_cuisines = (
+    stars_df["cuisine"].value_counts()
+    .head(10)
+    .index
+)
+
+plt.figure(figsize=(8,4))
+sns.countplot(
+    data=stars_df[stars_df["cuisine"].isin(top_cuisines)],
+    y="cuisine",
+    order=top_cuisines
+)
+plt.title("Top 10 types of cuisine")
+plt.xlabel("Nº of restaurants")
+plt.ylabel("Cuisine")
+plt.tight_layout()
+
+
+
+stars_df["stars_n"] = stars_df["stars"].str[0].astype(int)
+
+stars_df.groupby("region")["stars_n"].mean().sort_values().plot(kind="bar")
+plt.title("Mean of Stars per region")
+plt.xlabel("Region")
+plt.ylabel("Mean of stars")
+plt.xticks(rotation=45, ha="right")
+plt.tight_layout()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/data/clean/C.ipynb b/data/clean/C.ipynb
new file mode 100644
index 00000000..7fd80873
--- /dev/null
+++ b/data/clean/C.ipynb
@@ -0,0 +1,1834 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            name  year   latitude  longitude            city   region zipCode  \\\n",
+      "0   Kilian Stuba  2019  47.348580   10.17114  Kleinwalsertal  Austria   87568   \n",
+      "1  Pfefferschiff  2019  47.837870   13.07917        Hallwang  Austria    5300   \n",
+      "2      Esszimmer  2019  47.806850   13.03409        Salzburg  Austria    5020   \n",
+      "3     Carpe Diem  2019  47.800010   13.04006        Salzburg  Austria    5020   \n",
+      "4         Edvard  2019  48.216503   16.36852            Wien  Austria    1010   \n",
+      "\n",
+      "           cuisine  price                                                url  \n",
+      "0         Creative  $$$$$  https://guide.michelin.com/at/en/vorarlberg/kl...  \n",
+      "1  Classic cuisine  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "2         Creative  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "3   Market cuisine  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "4   Modern cuisine   $$$$  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "                               name  year  latitude  longitude      city  \\\n",
+      "0                  SENNS.Restaurant  2019  47.83636   13.06389  Salzburg   \n",
+      "1                            Ikarus  2019  47.79536   13.00695  Salzburg   \n",
+      "2                       Mraz & Sohn  2019  48.23129   16.37637      Wien   \n",
+      "3               Konstantin Filippou  2019  48.21056   16.37996      Wien   \n",
+      "4  Silvio Nickol Gourmet Restaurant  2019  48.20558   16.37693      Wien   \n",
+      "\n",
+      "    region zipCode         cuisine  price  \\\n",
+      "0  Austria    5020        Creative  $$$$$   \n",
+      "1  Austria    5020        Creative  $$$$$   \n",
+      "2  Austria    1200        Creative  $$$$$   \n",
+      "3  Austria    1010  Modern cuisine  $$$$$   \n",
+      "4  Austria    1010  Modern cuisine  $$$$$   \n",
+      "\n",
+      "                                                 url  \n",
+      "0  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "1  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "2  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "3  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "4  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "            name  year  latitude  longitude                 city      region  \\\n",
+      "0         Amador  2019  48.25406   16.35915                 Wien     Austria   \n",
+      "1        Manresa  2019  37.22761 -121.98071  South San Francisco  California   \n",
+      "2           Benu  2019  37.78521 -122.39876        San Francisco  California   \n",
+      "3         Quince  2019  37.79762 -122.40337        San Francisco  California   \n",
+      "4  Atelier Crenn  2019  37.79835 -122.43586        San Francisco  California   \n",
+      "\n",
+      "  zipCode       cuisine  price  \\\n",
+      "0    1190      Creative  $$$$$   \n",
+      "1   95030  Contemporary   $$$$   \n",
+      "2   94105         Asian   $$$$   \n",
+      "3   94133  Contemporary   $$$$   \n",
+      "4   94123  Contemporary   $$$$   \n",
+      "\n",
+      "                                                 url  \n",
+      "0  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "1  https://guide.michelin.com/us/en/california/so...  \n",
+      "2  https://guide.michelin.com/us/en/california/sa...  \n",
+      "3  https://guide.michelin.com/us/en/california/sa...  \n",
+      "4  https://guide.michelin.com/us/en/california/sa...  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/raw/archive/one-star-michelin-restaurants.csv')\n",
+    "star2_df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/raw/archive/two-stars-michelin-restaurants.csv')\n",
+    "star3_df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/raw/archive/three-stars-michelin-restaurants.csv')\n",
+    "\n",
+    "\n",
+    "print(star1_df.head())\n",
+    "print(star2_df.head())\n",
+    "print(star3_df.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f79ac46f-854b-4337-9f7a-79f506585548",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(549, 10)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star1_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a17c895d-b8f9-444e-86e9-36b07d023eb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(110, 10)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star2_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "233d291f-a2c5-4c7a-ac8f-d6c30982bfcc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(36, 10)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star3_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f5b3ef80-0c45-4c07-99aa-748d31021f4c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['name', 'year', 'latitude', 'longitude', 'city', 'region', 'zipCode',\n",
+      "       'cuisine', 'price', 'url'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(star1_df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1192df3e-2126-4d21-8f94-6d262b0c0ac7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>zipCode</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kilian Stuba</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.348580</td>\n",
+       "      <td>10.17114</td>\n",
+       "      <td>Kleinwalsertal</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>87568</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vorarlberg/kl...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pfefferschiff</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.837870</td>\n",
+       "      <td>13.07917</td>\n",
+       "      <td>Hallwang</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>5300</td>\n",
+       "      <td>Classic cuisine</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esszimmer</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.806850</td>\n",
+       "      <td>13.03409</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>5020</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Carpe Diem</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.800010</td>\n",
+       "      <td>13.04006</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>5020</td>\n",
+       "      <td>Market cuisine</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Edvard</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.216503</td>\n",
+       "      <td>16.36852</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>1010</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vienna/wien/r...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year   latitude  longitude            city   region zipCode  \\\n",
+       "0   Kilian Stuba  2019  47.348580   10.17114  Kleinwalsertal  Austria   87568   \n",
+       "1  Pfefferschiff  2019  47.837870   13.07917        Hallwang  Austria    5300   \n",
+       "2      Esszimmer  2019  47.806850   13.03409        Salzburg  Austria    5020   \n",
+       "3     Carpe Diem  2019  47.800010   13.04006        Salzburg  Austria    5020   \n",
+       "4         Edvard  2019  48.216503   16.36852            Wien  Austria    1010   \n",
+       "\n",
+       "           cuisine  price                                                url  \n",
+       "0         Creative  $$$$$  https://guide.michelin.com/at/en/vorarlberg/kl...  \n",
+       "1  Classic cuisine  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+       "2         Creative  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+       "3   Market cuisine  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+       "4   Modern cuisine   $$$$  https://guide.michelin.com/at/en/vienna/wien/r...  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star1_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "84c801f9-7317-4c26-8501-821fa7fd6d8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      1 star\n",
+      "1      1 star\n",
+      "2      1 star\n",
+      "3      1 star\n",
+      "4      1 star\n",
+      "        ...  \n",
+      "544    1 star\n",
+      "545    1 star\n",
+      "546    1 star\n",
+      "547    1 star\n",
+      "548    1 star\n",
+      "Name: stars, Length: 549, dtype: object\n",
+      "0      2 stars\n",
+      "1      2 stars\n",
+      "2      2 stars\n",
+      "3      2 stars\n",
+      "4      2 stars\n",
+      "        ...   \n",
+      "105    2 stars\n",
+      "106    2 stars\n",
+      "107    2 stars\n",
+      "108    2 stars\n",
+      "109    2 stars\n",
+      "Name: stars, Length: 110, dtype: object\n",
+      "0     3 stars\n",
+      "1     3 stars\n",
+      "2     3 stars\n",
+      "3     3 stars\n",
+      "4     3 stars\n",
+      "5     3 stars\n",
+      "6     3 stars\n",
+      "7     3 stars\n",
+      "8     3 stars\n",
+      "9     3 stars\n",
+      "10    3 stars\n",
+      "11    3 stars\n",
+      "12    3 stars\n",
+      "13    3 stars\n",
+      "14    3 stars\n",
+      "15    3 stars\n",
+      "16    3 stars\n",
+      "17    3 stars\n",
+      "18    3 stars\n",
+      "19    3 stars\n",
+      "20    3 stars\n",
+      "21    3 stars\n",
+      "22    3 stars\n",
+      "23    3 stars\n",
+      "24    3 stars\n",
+      "25    3 stars\n",
+      "26    3 stars\n",
+      "27    3 stars\n",
+      "28    3 stars\n",
+      "29    3 stars\n",
+      "30    3 stars\n",
+      "31    3 stars\n",
+      "32    3 stars\n",
+      "33    3 stars\n",
+      "34    3 stars\n",
+      "35    3 stars\n",
+      "Name: stars, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'\n",
+    "\n",
+    "print (star1_df['stars'])\n",
+    "print (star2_df['stars'])\n",
+    "print (star3_df['stars'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0cac4c98-4990-4080-96d3-51a4c70d9536",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>zipCode</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Amador</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.25406</td>\n",
+       "      <td>16.35915</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>1190</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vienna/wien/r...</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Manresa</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.22761</td>\n",
+       "      <td>-121.98071</td>\n",
+       "      <td>South San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>95030</td>\n",
+       "      <td>Contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/california/so...</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Benu</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.78521</td>\n",
+       "      <td>-122.39876</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>94105</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/california/sa...</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Quince</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.79762</td>\n",
+       "      <td>-122.40337</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>94133</td>\n",
+       "      <td>Contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/california/sa...</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Atelier Crenn</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.79835</td>\n",
+       "      <td>-122.43586</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>94123</td>\n",
+       "      <td>Contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/california/sa...</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year  latitude  longitude                 city      region  \\\n",
+       "0         Amador  2019  48.25406   16.35915                 Wien     Austria   \n",
+       "1        Manresa  2019  37.22761 -121.98071  South San Francisco  California   \n",
+       "2           Benu  2019  37.78521 -122.39876        San Francisco  California   \n",
+       "3         Quince  2019  37.79762 -122.40337        San Francisco  California   \n",
+       "4  Atelier Crenn  2019  37.79835 -122.43586        San Francisco  California   \n",
+       "\n",
+       "  zipCode       cuisine  price  \\\n",
+       "0    1190      Creative  $$$$$   \n",
+       "1   95030  Contemporary   $$$$   \n",
+       "2   94105         Asian   $$$$   \n",
+       "3   94133  Contemporary   $$$$   \n",
+       "4   94123  Contemporary   $$$$   \n",
+       "\n",
+       "                                                 url    stars  \n",
+       "0  https://guide.michelin.com/at/en/vienna/wien/r...  3 stars  \n",
+       "1  https://guide.michelin.com/us/en/california/so...  3 stars  \n",
+       "2  https://guide.michelin.com/us/en/california/sa...  3 stars  \n",
+       "3  https://guide.michelin.com/us/en/california/sa...  3 stars  \n",
+       "4  https://guide.michelin.com/us/en/california/sa...  3 stars  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star1_df.head()\n",
+    "star2_df.head()\n",
+    "star3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b20ff0d1-1591-4915-8aa0-71daa52f795e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kilian Stuba</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.348580</td>\n",
+       "      <td>10.17114</td>\n",
+       "      <td>Kleinwalsertal</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>1 star</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pfefferschiff</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.837870</td>\n",
+       "      <td>13.07917</td>\n",
+       "      <td>Hallwang</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Classic cuisine</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>1 star</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esszimmer</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.806850</td>\n",
+       "      <td>13.03409</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>1 star</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Carpe Diem</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.800010</td>\n",
+       "      <td>13.04006</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Market cuisine</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>1 star</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Edvard</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.216503</td>\n",
+       "      <td>16.36852</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>1 star</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year   latitude  longitude            city   region  \\\n",
+       "0   Kilian Stuba  2019  47.348580   10.17114  Kleinwalsertal  Austria   \n",
+       "1  Pfefferschiff  2019  47.837870   13.07917        Hallwang  Austria   \n",
+       "2      Esszimmer  2019  47.806850   13.03409        Salzburg  Austria   \n",
+       "3     Carpe Diem  2019  47.800010   13.04006        Salzburg  Austria   \n",
+       "4         Edvard  2019  48.216503   16.36852            Wien  Austria   \n",
+       "\n",
+       "           cuisine  price   stars  \n",
+       "0         Creative  $$$$$  1 star  \n",
+       "1  Classic cuisine  $$$$$  1 star  \n",
+       "2         Creative  $$$$$  1 star  \n",
+       "3   Market cuisine  $$$$$  1 star  \n",
+       "4   Modern cuisine   $$$$  1 star  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Drop unwanted columns\n",
+    "star1_df.drop(columns=['url','zipCode'], inplace=True)\n",
+    "star1_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "35362075-61d9-456e-9d43-516305831f5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SENNS.Restaurant</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.83636</td>\n",
+       "      <td>13.06389</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>2 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ikarus</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>47.79536</td>\n",
+       "      <td>13.00695</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>2 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mraz &amp; Sohn</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.23129</td>\n",
+       "      <td>16.37637</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>2 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Konstantin Filippou</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.21056</td>\n",
+       "      <td>16.37996</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>2 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Silvio Nickol Gourmet Restaurant</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.20558</td>\n",
+       "      <td>16.37693</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>2 stars</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               name  year  latitude  longitude      city  \\\n",
+       "0                  SENNS.Restaurant  2019  47.83636   13.06389  Salzburg   \n",
+       "1                            Ikarus  2019  47.79536   13.00695  Salzburg   \n",
+       "2                       Mraz & Sohn  2019  48.23129   16.37637      Wien   \n",
+       "3               Konstantin Filippou  2019  48.21056   16.37996      Wien   \n",
+       "4  Silvio Nickol Gourmet Restaurant  2019  48.20558   16.37693      Wien   \n",
+       "\n",
+       "    region         cuisine  price    stars  \n",
+       "0  Austria        Creative  $$$$$  2 stars  \n",
+       "1  Austria        Creative  $$$$$  2 stars  \n",
+       "2  Austria        Creative  $$$$$  2 stars  \n",
+       "3  Austria  Modern cuisine  $$$$$  2 stars  \n",
+       "4  Austria  Modern cuisine  $$$$$  2 stars  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star2_df.drop(columns=['url', 'zipCode'], inplace=True)\n",
+    "star2_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a4947e7-fe2d-4547-8a87-4fda6f2177f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Amador</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>48.25406</td>\n",
+       "      <td>16.35915</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$$</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Manresa</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.22761</td>\n",
+       "      <td>-121.98071</td>\n",
+       "      <td>South San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>Contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Benu</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.78521</td>\n",
+       "      <td>-122.39876</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>Asian</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Quince</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.79762</td>\n",
+       "      <td>-122.40337</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>Contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Atelier Crenn</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>37.79835</td>\n",
+       "      <td>-122.43586</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>California</td>\n",
+       "      <td>Contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year  latitude  longitude                 city      region  \\\n",
+       "0         Amador  2019  48.25406   16.35915                 Wien     Austria   \n",
+       "1        Manresa  2019  37.22761 -121.98071  South San Francisco  California   \n",
+       "2           Benu  2019  37.78521 -122.39876        San Francisco  California   \n",
+       "3         Quince  2019  37.79762 -122.40337        San Francisco  California   \n",
+       "4  Atelier Crenn  2019  37.79835 -122.43586        San Francisco  California   \n",
+       "\n",
+       "        cuisine  price    stars  \n",
+       "0      Creative  $$$$$  3 stars  \n",
+       "1  Contemporary   $$$$  3 stars  \n",
+       "2         Asian   $$$$  3 stars  \n",
+       "3  Contemporary   $$$$  3 stars  \n",
+       "4  Contemporary   $$$$  3 stars  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "star3_df.drop(columns=['url', 'zipCode'], inplace=True)\n",
+    "star3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "cc8dc856-5819-4356-8d5c-cd74d6913b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#compiled df\n",
+    "\n",
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a12f0db9-0107-4d06-bfa3-070354d6add0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>stars</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>690</th>\n",
+       "      <td>Fat Duck</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>51.508280</td>\n",
+       "      <td>-0.702320</td>\n",
+       "      <td>Bray</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>691</th>\n",
+       "      <td>Waterside Inn</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>51.507730</td>\n",
+       "      <td>-0.701210</td>\n",
+       "      <td>Bray</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Classic French</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>692</th>\n",
+       "      <td>Alain Ducasse at The Dorchester</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>51.507120</td>\n",
+       "      <td>-0.152520</td>\n",
+       "      <td>Mayfair</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>French</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>693</th>\n",
+       "      <td>The Araki</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>51.511826</td>\n",
+       "      <td>-0.140389</td>\n",
+       "      <td>Mayfair</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>Japanese</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>Gordon Ramsay</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>51.485460</td>\n",
+       "      <td>-0.162020</td>\n",
+       "      <td>Chelsea</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>French</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3 stars</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                name  year   latitude  longitude     city  \\\n",
+       "690                         Fat Duck  2019  51.508280  -0.702320     Bray   \n",
+       "691                    Waterside Inn  2019  51.507730  -0.701210     Bray   \n",
+       "692  Alain Ducasse at The Dorchester  2019  51.507120  -0.152520  Mayfair   \n",
+       "693                        The Araki  2019  51.511826  -0.140389  Mayfair   \n",
+       "694                    Gordon Ramsay  2019  51.485460  -0.162020  Chelsea   \n",
+       "\n",
+       "             region         cuisine price    stars  \n",
+       "690  United Kingdom        Creative   NaN  3 stars  \n",
+       "691  United Kingdom  Classic French   NaN  3 stars  \n",
+       "692  United Kingdom          French   NaN  3 stars  \n",
+       "693  United Kingdom        Japanese   NaN  3 stars  \n",
+       "694  United Kingdom          French   NaN  3 stars  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "6e4047ce-07a6-49e8-a80d-2e04670bda00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "684"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['name'].nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "38e6aaff-350c-4e70-af2a-9deec6da9360",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'latitude', 'longitude', 'city', 'region', 'cuisine',\n",
+       "       'price', 'stars'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0                         kilian stuba\n",
+      "1                        pfefferschiff\n",
+      "2                            esszimmer\n",
+      "3                           carpe diem\n",
+      "4                               edvard\n",
+      "                    ...               \n",
+      "690                           fat duck\n",
+      "691                      waterside inn\n",
+      "692    alain ducasse at the dorchester\n",
+      "693                          the araki\n",
+      "694                      gordon ramsay\n",
+      "Name: name, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Restaurant name standardization - lower case\n",
+    "\n",
+    "stars_df['name']= stars_df['name'].str.lower()\n",
+    "print (stars_df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ce87746b-6e4b-41ad-a858-99c38a990e91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                name  year   latitude  longitude                    city  \\\n",
+      "119             clou  2019  55.683420  12.584970               København   \n",
+      "276           oteque  2019 -22.957470 -43.194424  Rio de Janeiro - 22271   \n",
+      "127  kadeau bornholm  2019  55.004570  14.969216               Pedersker   \n",
+      "509            sabor  2019  51.511368  -0.139677                 Mayfair   \n",
+      "538            story  2019  51.502680  -0.077740              Bermondsey   \n",
+      "\n",
+      "             region         cuisine  price   stars  \n",
+      "119         Denmark  Modern cuisine   $$$$  1 star  \n",
+      "276  Rio de Janeiro          modern  $$$$$  1 star  \n",
+      "127         Denmark        Creative   $$$$  1 star  \n",
+      "509  United Kingdom         Spanish    NaN  1 star  \n",
+      "538  United Kingdom  Modern cuisine    NaN  1 star  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Trim Excessive Whitespaces:\n",
+    "\n",
+    "stars_df['name'] = stars_df['name'].astype(str)\n",
+    "stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))\n",
+    "\n",
+    "print(stars_df.sample(5)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# year check\n",
+    "\n",
+    "stars_df['year'].nunique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "667cc635-829c-4ecd-b8ad-fe99e5da22d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      2019\n",
+      "1      2019\n",
+      "2      2019\n",
+      "3      2019\n",
+      "4      2019\n",
+      "       ... \n",
+      "690    2019\n",
+      "691    2019\n",
+      "692    2019\n",
+      "693    2019\n",
+      "694    2019\n",
+      "Name: year, Length: 695, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stars_df['year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "90157338-8f0d-4a73-bf0e-ec6f9de6df96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "179"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# city names check\n",
+    "stars_df['city'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ffe95673-21a1-4df4-9bd6-386d4f8ccfff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Kleinwalsertal', 'Hallwang', 'Salzburg', 'Wien',\n",
+       "       'South San Francisco', 'San Francisco', 'Monterey', 'Sacramento',\n",
+       "       'Los Angeles', 'Costa Mesa', 'San Diego', 'Chicago', 'Rovinj',\n",
+       "       'Lovran', 'Zagreb', 'Šibenik', 'Dubrovnik', 'Praha', 'Aarhus',\n",
+       "       'Vejle', 'Fredericia', 'Hørve', 'København', 'Præstø', 'Pedersker',\n",
+       "       'Helsingfors / Helsinki', 'Athína', 'Hong Kong', nan, 'Budapest',\n",
+       "       'Macau', 'Trondheim', 'Stavanger', 'Oslo', 'New York', 'Warszawa',\n",
+       "       'Gaillimh/Galway', 'Lios Dúin Bhearna/Lisdoonvarna', 'Ballydehob',\n",
+       "       'Baltimore', 'Corcaigh/Cork', 'City Centre',\n",
+       "       'Cill Chainnigh/Kilkenny', 'Blackrock',\n",
+       "       'Baile Mhic Andáin/Thomastown', 'Aird Mhór/Ardmore',\n",
+       "       'Rio de Janeiro - 22470', 'Rio de Janeiro - 22271',\n",
+       "       'Rio de Janeiro - 22021', 'São Paulo - 05413', 'São Paulo - 05415',\n",
+       "       'São Paulo - 01426', 'São Paulo - 04538', 'São Paulo - 04531',\n",
+       "       'São Paulo - 05706', 'São Paulo - 04509', 'São Paulo - 01401',\n",
+       "       'São Paulo - 04080', 'Seoul', 'Singapore', 'Göteborg', 'Stockholm',\n",
+       "       'Växjö', 'Malmö', 'Taipei', 'Bangkok', 'Phuket',\n",
+       "       'Washington, D.C.', 'Waternish', 'Dalry', 'Belfast', 'Peat Inn',\n",
+       "       'Leith', 'Edinburgh', 'Anstruther', 'Grasmere',\n",
+       "       'Bowness-on-Windermere', 'Cartmel', 'Newcastle upon Tyne',\n",
+       "       'Menai Bridge/Porthaethwy', 'Langho', 'Pateley Bridge',\n",
+       "       'Birkenhead', 'Fence', 'Llandrillo', 'Machynlleth', 'Oldstead',\n",
+       "       'Chester', 'Harome', 'Leeds', 'Montgomery/Trefaldwyn',\n",
+       "       'South Dalton', 'Baslow', 'Winteringham', 'Ilfracombe',\n",
+       "       'Llanddewi Skirrid', 'Padstow', 'Birmingham', 'Port Isaac',\n",
+       "       'Hampton in Arden', 'Mountsorrel', 'Whitebrook', 'Penarth',\n",
+       "       'Portscatho', 'Kenilworth', 'Knowstone', 'Stratford-upon-Avon',\n",
+       "       'Cheltenham', 'Chagford', 'Upper Hambleton', 'Bristol',\n",
+       "       'Chew Magna', 'Malmesbury', 'Castle Combe', 'Colerne', 'Bath',\n",
+       "       'Lympstone', 'Hunstanton', 'Torquay', 'Oxford', 'Murcott',\n",
+       "       'Morston', 'East Chisenbury', 'Newbury', 'Marlow',\n",
+       "       \"Burchett's Green\", 'Shinfield', 'Bray', 'Winchester', 'Bagshot',\n",
+       "       'Ascot', 'Egham', 'Kew', 'Chiswick', 'Little Dunmow',\n",
+       "       'Hammersmith', 'Kensington', 'Marylebone', \"Regent's Park\",\n",
+       "       'Fulham', 'Bloomsbury', 'Mayfair', 'Soho', 'Belgravia', 'Ripley',\n",
+       "       'Westminster', 'Chelsea', \"Saint James's\", 'Victoria',\n",
+       "       'Clerkenwell', 'London', 'Shoreditch', 'Finsbury', 'Spitalfields',\n",
+       "       'City of London', 'Bermondsey', 'Clapham Common', 'Wandsworth',\n",
+       "       'Dorking', 'Horsham', 'Gravetye', 'Seasalter', 'Biddenden',\n",
+       "       'Fordwich', 'Saint Helier/Saint-Hélier', 'Leynar', 'Henne',\n",
+       "       'Rio de Janeiro - 22441', 'São Paulo - 05416', 'São Paulo - 01411',\n",
+       "       'Järpen', 'Skåne-Tranås', 'Auchterarder', 'Summerhouse', 'Aughton',\n",
+       "       'Nottingham', 'Great Milton', 'Cambridge', 'North Kensington',\n",
+       "       'Hyde Park'], dtype=object)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "be8a74a5-127d-4992-adaf-8c0baf1ee4e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      Kleinwalsertal\n",
+      "1            Hallwang\n",
+      "2            Salzburg\n",
+      "3            Salzburg\n",
+      "4                Wien\n",
+      "            ...      \n",
+      "690              Bray\n",
+      "691              Bray\n",
+      "692           Mayfair\n",
+      "693           Mayfair\n",
+      "694           Chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Remove Leading/Trailing Spaces\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.strip()\n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9130d410-d12e-4ca7-9e1a-10bcd5df92a1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      kleinwalsertal\n",
+      "1            hallwang\n",
+      "2            salzburg\n",
+      "3            salzburg\n",
+      "4                wien\n",
+      "            ...      \n",
+      "690              bray\n",
+      "691              bray\n",
+      "692           mayfair\n",
+      "693           mayfair\n",
+      "694           chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#convert to lower case\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.lower() \n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "b93bb263-5025-4a48-bbcc-05bd1883896c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                name  year   latitude  longitude      city  \\\n",
+      "2                          esszimmer  2019  47.806850  13.034090  salzburg   \n",
+      "3                         carpe diem  2019  47.800010  13.040060  salzburg   \n",
+      "4                             edvard  2019  48.216503  16.368520      wien   \n",
+      "5                           das loft  2019  48.212720  16.379310      wien   \n",
+      "6                 pramerl & the wolf  2019  48.209450  16.371740      wien   \n",
+      "..                               ...   ...        ...        ...       ...   \n",
+      "690                         fat duck  2019  51.508280  -0.702320      bray   \n",
+      "691                    waterside inn  2019  51.507730  -0.701210      bray   \n",
+      "692  alain ducasse at the dorchester  2019  51.507120  -0.152520   mayfair   \n",
+      "693                        the araki  2019  51.511826  -0.140389   mayfair   \n",
+      "694                    gordon ramsay  2019  51.485460  -0.162020   chelsea   \n",
+      "\n",
+      "             region         cuisine  price    stars  \n",
+      "2           Austria        Creative  $$$$$   1 star  \n",
+      "3           Austria  Market cuisine  $$$$$   1 star  \n",
+      "4           Austria  Modern cuisine   $$$$   1 star  \n",
+      "5           Austria  Modern cuisine  $$$$$   1 star  \n",
+      "6           Austria        Creative  $$$$$   1 star  \n",
+      "..              ...             ...    ...      ...  \n",
+      "690  United Kingdom        Creative    NaN  3 stars  \n",
+      "691  United Kingdom  Classic French    NaN  3 stars  \n",
+      "692  United Kingdom          French    NaN  3 stars  \n",
+      "693  United Kingdom        Japanese    NaN  3 stars  \n",
+      "694  United Kingdom          French    NaN  3 stars  \n",
+      "\n",
+      "[571 rows x 9 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#check for duplicates\n",
+    "\n",
+    "duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4ece9b9e-aad7-4934-9d62-096f90825dc2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# order A–Z\n",
+    "\n",
+    "#stars_df = stars_df.sort_values(by=\"city\")  \n",
+    "#stars_df['city'].unique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "f136d339-877b-4fae-98a2-c157bed9be14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove numbers and zip codes\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def clean_city_name(city_name):\n",
+    "    if isinstance(city_name, str):  # Check if the input is a string\n",
+    "        # Use regex to remove \" - numbers\" at the end of the string\n",
+    "        return re.sub(r'\\s-\\s\\d+$', '', city_name).strip()\n",
+    "    return city_name  # Return as is if it's not a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7a49b8b8-6f7a-400b-b667-f6dd58dbe1b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].apply(clean_city_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1f7c26ac-f517-4ada-b0f3-845573c95de5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['kleinwalsertal' 'hallwang' 'salzburg' 'wien' 'south san francisco'\n",
+      " 'san francisco' 'monterey' 'sacramento' 'los angeles' 'costa mesa'\n",
+      " 'san diego' 'chicago' 'rovinj' 'lovran' 'zagreb' 'šibenik' 'dubrovnik'\n",
+      " 'praha' 'aarhus' 'vejle' 'fredericia' 'hørve' 'københavn' 'præstø'\n",
+      " 'pedersker' 'helsingfors / helsinki' 'athína' 'hong kong' nan 'budapest'\n",
+      " 'macau' 'trondheim' 'stavanger' 'oslo' 'new york' 'warszawa'\n",
+      " 'gaillimh/galway' 'lios dúin bhearna/lisdoonvarna' 'ballydehob'\n",
+      " 'baltimore' 'corcaigh/cork' 'city centre' 'cill chainnigh/kilkenny'\n",
+      " 'blackrock' 'baile mhic andáin/thomastown' 'aird mhór/ardmore'\n",
+      " 'rio de janeiro' 'são paulo' 'seoul' 'singapore' 'göteborg' 'stockholm'\n",
+      " 'växjö' 'malmö' 'taipei' 'bangkok' 'phuket' 'washington, d.c.'\n",
+      " 'waternish' 'dalry' 'belfast' 'peat inn' 'leith' 'edinburgh' 'anstruther'\n",
+      " 'grasmere' 'bowness-on-windermere' 'cartmel' 'newcastle upon tyne'\n",
+      " 'menai bridge/porthaethwy' 'langho' 'pateley bridge' 'birkenhead' 'fence'\n",
+      " 'llandrillo' 'machynlleth' 'oldstead' 'chester' 'harome' 'leeds'\n",
+      " 'montgomery/trefaldwyn' 'south dalton' 'baslow' 'winteringham'\n",
+      " 'ilfracombe' 'llanddewi skirrid' 'padstow' 'birmingham' 'port isaac'\n",
+      " 'hampton in arden' 'mountsorrel' 'whitebrook' 'penarth' 'portscatho'\n",
+      " 'kenilworth' 'knowstone' 'stratford-upon-avon' 'cheltenham' 'chagford'\n",
+      " 'upper hambleton' 'bristol' 'chew magna' 'malmesbury' 'castle combe'\n",
+      " 'colerne' 'bath' 'lympstone' 'hunstanton' 'torquay' 'oxford' 'murcott'\n",
+      " 'morston' 'east chisenbury' 'newbury' 'marlow' \"burchett's green\"\n",
+      " 'shinfield' 'bray' 'winchester' 'bagshot' 'ascot' 'egham' 'kew'\n",
+      " 'chiswick' 'little dunmow' 'hammersmith' 'kensington' 'marylebone'\n",
+      " \"regent's park\" 'fulham' 'bloomsbury' 'mayfair' 'soho' 'belgravia'\n",
+      " 'ripley' 'westminster' 'chelsea' \"saint james's\" 'victoria' 'clerkenwell'\n",
+      " 'london' 'shoreditch' 'finsbury' 'spitalfields' 'city of london'\n",
+      " 'bermondsey' 'clapham common' 'wandsworth' 'dorking' 'horsham' 'gravetye'\n",
+      " 'seasalter' 'biddenden' 'fordwich' 'saint helier/saint-hélier' 'leynar'\n",
+      " 'henne' 'järpen' 'skåne-tranås' 'auchterarder' 'summerhouse' 'aughton'\n",
+      " 'nottingham' 'great milton' 'cambridge' 'north kensington' 'hyde park']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verify results\n",
+    "\n",
+    "print(stars_df['city'].unique())  # Display unique city names to verify the cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "b1811b7b-8c9f-41f8-800f-35af07d5f14c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove special characters \n",
+    "\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "e4537f15-c284-42f9-b182-8f3f014732c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "5d3e0999-6260-438c-bbf9-4cbf0c594e22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2bd9b2b8-50e4-4266-957f-be1afad036d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'unidecode'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m#the city column are stripped of accents and are presented in ASCII format.\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01munidecode\u001b[39;00m\n\u001b[32m      4\u001b[39m stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m] = stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m].astype(\u001b[38;5;28mstr\u001b[39m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: unidecode.unidecode(x))\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'unidecode'"
+     ]
+    }
+   ],
+   "source": [
+    "#the city column are stripped of accents and are presented in ASCII format.\n",
+    "\n",
+    "import unidecode\n",
+    "stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a499442a-2cab-4c59-8f86-45e331d632e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#verify the results\n",
+    "print(stars_df['city'].unique()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33334128-de67-40e5-b1c5-e561b7dcbb15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grouping suburbs into major city and add info in a new column \n",
+    "\n",
+    "#create a dictionary \n",
+    "\n",
+    "location_map = {\n",
+    "    # London + neighborhoods\n",
+    "    'north kensington': 'London',\n",
+    "    'kensington': 'London',\n",
+    "    'westminster': 'London',\n",
+    "    'soho': 'London',\n",
+    "    'mayfair': 'London',\n",
+    "    'marylebone': 'London',\n",
+    "    'chelsea': 'London',\n",
+    "    'clapham common': 'London',\n",
+    "    \"regent's park\": 'London',\n",
+    "    'shoreditch': 'London',\n",
+    "    'spitalfields': 'London',\n",
+    "    'belgravia': 'London',\n",
+    "    'bloomsbury': 'London',\n",
+    "    'finsbury': 'London',\n",
+    "    'fulham': 'London',\n",
+    "    'chiswick': 'London',\n",
+    "    'city centre': 'London',\n",
+    "    'city of london': 'London',\n",
+    "    'hyde park': 'London',\n",
+    "    # San Francisco\n",
+    "    'south san francisco': 'San Francisco',\n",
+    "    # Ireland\n",
+    "    'baile mhic andáin/thomastown': 'Thomastown',\n",
+    "    'gaillimh/galway': 'Galway',\n",
+    "    'cill chainnigh/kilkenny': 'Kilkenny',\n",
+    "    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',\n",
+    "    'athína': 'Athens',\n",
+    "    'ballydehob': 'Ballydehob',\n",
+    "    # Finland\n",
+    "    'helsingfors / helsinki': 'Helsinki',\n",
+    "    # Czech Republic\n",
+    "    'praha': 'Prague',\n",
+    "    # Austria\n",
+    "    'wien': 'Vienna',\n",
+    "    'salzburg': 'Salzburg',\n",
+    "    # Menai Bridge\n",
+    "    'menai bridge/porthaethwy': 'Menai Bridge',\n",
+    "    # USA cities\n",
+    "    'los angeles': 'Los Angeles',\n",
+    "    'san diego': 'San Diego',\n",
+    "    'sacramento': 'Sacramento',\n",
+    "    'new york': 'New York',\n",
+    "    'chicago': 'Chicago',\n",
+    "    'costa mesa': 'Costa Mesa',\n",
+    "    'monterey': 'Monterey',\n",
+    "    'washington, d.c.': 'Washington D.C.',\n",
+    "    'south dalton': 'Dalton',\n",
+    "    # Asia\n",
+    "    'bangkok': 'Bangkok',\n",
+    "    'phuket': 'Phuket',\n",
+    "    'hong kong': 'Hong Kong',\n",
+    "    'taipei': 'Taipei',\n",
+    "    'seoul': 'Seoul',\n",
+    "    'singapore': 'Singapore',\n",
+    "    'macau': 'Macau',\n",
+    "    # Croatia\n",
+    "    'lovran': 'Lovran',\n",
+    "    'rovinj': 'Rovinj',\n",
+    "    'zagreb': 'Zagreb',\n",
+    "    'šibenik': 'Sibenik',\n",
+    "    # Norway / Scandinavia\n",
+    "    'stavanger': 'Stavanger',\n",
+    "    'trondheim': 'Trondheim',\n",
+    "    'oslo': 'Oslo',\n",
+    "    'göteborg': 'Gothenburg',\n",
+    "    'växjö': 'Vaxjo',\n",
+    "    'skåne-tranås': 'Skane-Tranas',\n",
+    "    'vejle': 'Vejle',\n",
+    "    # Denmark\n",
+    "    'fredericia': 'Fredericia',\n",
+    "    'pedersker': 'Pedersker',\n",
+    "    'præstø': 'Praesto',\n",
+    "    # Sweden\n",
+    "    'malmö': 'Malmo',\n",
+    "    'stockholm': 'Stockholm',\n",
+    "    # Portugal / Ireland / UK misc\n",
+    "    'bath': 'Bath',\n",
+    "    'bristol': 'Bristol',\n",
+    "    'cambridge': 'Cambridge',\n",
+    "    'cheltenham': 'Cheltenham',\n",
+    "    'chester': 'Chester',\n",
+    "    'birmingham': 'Birmingham',\n",
+    "    'edinburgh': 'Edinburgh',\n",
+    "    'leeds': 'Leeds',\n",
+    "    'oxford': 'Oxford',\n",
+    "    'stratford-upon-avon': 'Stratford-Upon-Avon',\n",
+    "    'padstow': 'Padstow',\n",
+    "    'torquay': 'Torquay',\n",
+    "    'newcastle upon tyne': 'Newcastle upon Tyne',\n",
+    "    'nottingham': 'Nottingham',\n",
+    "    'bray': 'Bray',\n",
+    "    'bowness-on-windermere': 'Bowness-on-Windermere',\n",
+    "    'cartmel': 'Cartmel',\n",
+    "    'castle combe': 'Castle Combe',\n",
+    "    'chagford': 'Chagford',\n",
+    "    'chew magna': 'Chew Magna',\n",
+    "    'dalry': 'Dalry',\n",
+    "    'dorking': 'Dorking',\n",
+    "    'egham': 'Egham',\n",
+    "    'fence': 'Fence',\n",
+    "    'fordwich': 'Fordwich',\n",
+    "    'grasmere': 'Grasmere',\n",
+    "    'gravetye': 'Gravetye',\n",
+    "    'great milton': 'Great Milton',\n",
+    "    'hallwang': 'Hallwang',\n",
+    "    'hampton in arden': 'Hampton in Arden',\n",
+    "    'harome': 'Harome',\n",
+    "    'henne': 'Henne',\n",
+    "    'horsham': 'Horsham',\n",
+    "    'hunstanton': 'Hunstanton',\n",
+    "    'ilfracombe': 'Ilfracombe',\n",
+    "    'järpen': 'Jarpen',\n",
+    "    'kenilworth': 'Kenilworth',\n",
+    "    'kew': 'Kew',\n",
+    "    'kleinwalsertal': 'Kleinwalsertal',\n",
+    "    'knowstone': 'Knowstone',\n",
+    "    'langho': 'Langho',\n",
+    "    'leith': 'Leith',\n",
+    "    'leynar': 'Leynar',\n",
+    "    'little dunmow': 'Little Dunmow',\n",
+    "    'llanddewi skirrid': 'Llanddewi Skirrid',\n",
+    "    'llandrillo': 'Llandrillo',\n",
+    "    'lovran': 'Lovran',\n",
+    "    'lympstone': 'Lympstone',\n",
+    "    'machynlleth': 'Machynlleth',\n",
+    "    'malmesbury': 'Malmesbury',\n",
+    "    'marlow': 'Marlow',\n",
+    "    'morston': 'Morston',\n",
+    "    'mountsorrel': 'Mountsorrel',\n",
+    "    'murcott': 'Murcott',\n",
+    "    'newbury': 'Newbury',\n",
+    "    'oldstead': 'Oldstead',\n",
+    "    'peat inn': 'Peat Inn',\n",
+    "    'penarth': 'Penarth',\n",
+    "    'port isaac': 'Port Isaac',\n",
+    "    'portscatho': 'Portscatho',\n",
+    "    'ripley': 'Ripley',\n",
+    "    'saint helier/saint-hélier': 'Saint Helier',\n",
+    "    \"saint james's\": 'Saint James',\n",
+    "    'seasalter': 'Seasalter',\n",
+    "    'shinfield': 'Shinfield',\n",
+    "    'summerhouse': 'Summerhouse',\n",
+    "    'upper hambleton': 'Hambleton',\n",
+    "    'victoria': 'Victoria',\n",
+    "    'wandsworth': 'London',\n",
+    "    'whitebrook': 'Whitebrook',\n",
+    "    'winchester': 'Winchester',\n",
+    "    'winteringham': 'Winteringham'\n",
+    "}\n",
+    "\n",
+    "stars_df['major_city'] = stars_df['city'].replace(location_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "698ea909-9354-4430-934e-c11a87b46503",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stars_df[['city', 'major_city']].sample(10))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d16e51a-d647-49d1-8209-e6f2e42720b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "270ab78b-4d81-442e-b00d-7800153f2d80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b858a728-00a6-48e0-81e9-d6263c7af9ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02d47a24-bcc7-4aab-878a-826549e0f9bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visuals: \n",
+    "#which are the cities with more restaurants? \n",
+    "#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?\n",
+    "# which are the cuisine dominating a city /region \n",
+    "# avg price point in a specific city based on restaurants?\n",
+    "# time series 2018 vs 2019 any trend? any star restautant grew over past year? \n",
+    "# cheapest vs most expensive cuisine? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3828f110-7152-4971-b01e-52b05aa51637",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/clean/CleanStars.csv b/data/clean/CleanStars.csv
new file mode 100644
index 00000000..4a8e5f66
--- /dev/null
+++ b/data/clean/CleanStars.csv
@@ -0,0 +1,1395 @@
+<<<<<<< Updated upstream
+name,year,city,region,cuisine,price,url,stars,major_city
+ho hung kee,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee,1 star,Hong Kong
+feng wei ju,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju,2 stars,Macau
+imperial treasure fine teochew cuisine (orchard),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard,1 star,Singapore
+shisen hanten,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten,2 stars,Singapore
+ma cuisine,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine,1 star,Singapore
+alma,2018,Singapore,Singapore,other european,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma,1 star,Singapore
+lei garden,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509,1 star,Singapore
+the song of india,2018,Singapore,Singapore,indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india,1 star,Singapore
+putien (kitchener road),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road,1 star,Singapore
+hill street tai hwa pork noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle,1 star,Singapore
+jin jin,2019,Seoul,South Korea,chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin,1 star,Seoul
+summer palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658,1 star,Singapore
+cheek by jowl,2018,Singapore,Singapore,australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl,1 star,Singapore
+nouri,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri,1 star,Singapore
+liao fan hong kong soya sauce chicken rice & noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle,1 star,Singapore
+garibaldi,2018,Singapore,Singapore,italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi,1 star,Singapore
+candlenut,2018,Singapore,Singapore,other asian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut,1 star,Singapore
+rhubarb,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb,1 star,Singapore
+tim ho wan (sham shui po),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po,1 star,Hong Kong
+lei garden (mong kok),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok,1 star,Hong Kong
+yè shanghai (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui,1 star,Hong Kong
+crystal jade golden palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace,1 star,Singapore
+pang's kitchen,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen,1 star,Hong Kong
+qi (wan chai),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai,1 star,Hong Kong
+kam's roast goose,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose,1 star,Hong Kong
+yat lok,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok,1 star,Hong Kong
+king,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226,1 star,Macau
+the golden peacock,2019,Macau,Macau,indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock,1 star,Macau
+suan thip,2019,Bangkok,Thailand,other asian,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip,1 star,Bangkok
+da san yuan,2019,Taipei,Taipei,chinese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan,1 star,Taipei
+lei garden (kwun tong),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong,1 star,Hong Kong
+tainan tan tsu mien seafood,2019,Taipei,Taipei,seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood,1 star,Taipei
+parachute,2019,Chicago,Chicago,other asian,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute,1 star,Chicago
+beefbar,2019,Hong Kong,Hong Kong,american,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar,1 star,Hong Kong
+claro,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro,1 star,New York
+yee tung heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen,1 star,Hong Kong
+the guest house,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house,2 stars,Taipei
+lai heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen,1 star,Macau
+ying,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying,1 star,Macau
+fu ho (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui,1 star,Hong Kong
+im teppanyaki & wine,2019,Hong Kong,Hong Kong,japanese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine,1 star,Hong Kong
+ah yat harbour view (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui,1 star,Hong Kong
+methavalai sorndaeng,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng,1 star,Bangkok
+imperial treasure fine chinese cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine,1 star,Hong Kong
+zi yat heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen,1 star,Macau
+corner house,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house,1 star,Singapore
+yat tung heen (jordan),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan,1 star,Hong Kong
+jade dragon,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon,3 stars,Macau
+balwoo gongyang,2019,Seoul,South Korea,korean,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang,1 star,Seoul
+le palais,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais,3 stars,Taipei
+bistro na's,2019,Los Angeles,California,chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s,1 star,Los Angeles
+kin khao,2019,San Francisco,California,other asian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao,1 star,San Francisco
+commonwealth,2019,San Francisco,California,contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth,1 star,San Francisco
+béni,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni,1 star,Singapore
+forum,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum,2 stars,Hong Kong
+labyrinth,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth,1 star,Singapore
+braci,2018,Singapore,Singapore,italian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci,1 star,Singapore
+sun tung lok,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok,2 stars,Hong Kong
+casa enríque,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique,1 star,New York
+meta,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta,1 star,Singapore
+bacchanalia,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia,1 star,Singapore
+shinji (bras basah road),2018,Singapore,Singapore,japanese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road,1 star,Singapore
+summer pavilion,2018,Singapore,Singapore,chinese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion,1 star,Singapore
+state bird provisions,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions,1 star,San Francisco
+faro,2019,New York,New York City,american,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro,1 star,New York
+saint pierre,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre,1 star,Singapore
+whitegrass,2018,Singapore,Singapore,australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass,1 star,Singapore
+rose's luxury,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury,1 star,"Washington, D.C."
+burnt ends,2018,Singapore,Singapore,american,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends,1 star,Singapore
+zhejiang heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen,1 star,Hong Kong
+uncle boons,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons,1 star,New York
+bloom in the park,2019,Malmo,Sweden,creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park,1 star,Malmo
+golden formosa,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa,1 star,Taipei
+ruean panya,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya,1 star,Bangkok
+søllerød kro,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro,1 star,Kobenhavn
+relæ,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae,1 star,Kobenhavn
+kiin kiin,2019,Kobenhavn,Denmark,other asian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin,1 star,Kobenhavn
+formel b,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b,1 star,Kobenhavn
+danny's steakhouse,2019,Taipei,Taipei,american,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse,1 star,Taipei
+marchal,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal,1 star,Kobenhavn
+tien hsiang lo,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo,1 star,Taipei
+era ora,2019,Kobenhavn,Denmark,italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora,1 star,Kobenhavn
+ya ge,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge,1 star,Taipei
+ying jee club,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club,2 stars,Hong Kong
+jardin de jade (wan chai),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai,1 star,Hong Kong
+tail up goat,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat,1 star,"Washington, D.C."
+mathias dahlgren-matbaren,2019,Stockholm,Sweden,international cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren,1 star,Stockholm
+jeju noodle bar,2019,New York,New York City,korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar,1 star,New York
+saneh jaan,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan,1 star,Bangkok
+mizumi (macau),2019,Macau,Macau,japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau,2 stars,Macau
+al's place,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place,1 star,San Francisco
+wing lei,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei,1 star,Macau
+golden flower,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower,2 stars,Macau
+tim's kitchen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen,1 star,Macau
+celebrity cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine,1 star,Hong Kong
+mountain and sea house,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house,1 star,Taipei
+rasa,2019,San Francisco,California,indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa,1 star,San Francisco
+jay fai,2019,Bangkok,Thailand,international cuisine,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai,1 star,Bangkok
+longtail,2019,Taipei,Taipei,international cuisine,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail,1 star,Taipei
+tuome,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome,1 star,New York
+dusek's (board & beer),2019,Chicago,Chicago,gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer,1 star,Chicago
+café china,2019,New York,New York City,chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china,1 star,New York
+the dabney,2019,"Washington, D.C.",Washington DC,american,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney,1 star,"Washington, D.C."
+bresca,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca,1 star,"Washington, D.C."
+loaf on,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on,1 star,Hong Kong
+new punjab club,2019,Hong Kong,Hong Kong,indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club,1 star,Hong Kong
+kwonsooksoo,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo,2 stars,Seoul
+shin sushi,2019,Los Angeles,California,japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi,1 star,Los Angeles
+osteria mozza,2019,Los Angeles,California,italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza,1 star,Los Angeles
+kali,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali,1 star,Los Angeles
+bouchon,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon,1 star,San Francisco
+taco maría,2019,Costa Mesa,California,mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria,1 star,Costa Mesa
+bar crenn,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn,1 star,San Francisco
+alla prima,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima,2 stars,Seoul
+henne kirkeby kro,2019,Henne,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro,2 stars,Henne
+band of bohemia,2019,Chicago,Chicago,gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia,1 star,Chicago
+masseria,2019,"Washington, D.C.",Washington DC,italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria,1 star,"Washington, D.C."
+entente,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente,1 star,Chicago
+kinship,2019,"Washington, D.C.",Washington DC,contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship,1 star,"Washington, D.C."
+rustic canyon,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon,1 star,Los Angeles
+mingles,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles,2 stars,Seoul
+duddell's,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s,1 star,Hong Kong
+mourad,2019,San Francisco,California,moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad,1 star,San Francisco
+boka,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka,1 star,Chicago
+roister,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister,1 star,Chicago
+jungsik,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965,2 stars,Seoul
+sorrel,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel,1 star,San Francisco
+lord stanley,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley,1 star,San Francisco
+north pond,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond,1 star,Chicago
+blue duck tavern,2019,"Washington, D.C.",Washington DC,american,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern,1 star,"Washington, D.C."
+kato,2019,Los Angeles,California,other asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato,1 star,Los Angeles
+domestic,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic,1 star,Aarhus
+me‚mu,2019,Vejle,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu,1 star,Vejle
+jordnær,2019,Kobenhavn,Denmark,danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer,1 star,Kobenhavn
+kokkeriet,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet,1 star,Kobenhavn
+gastromé,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome,1 star,Aarhus
+sepia,2019,Chicago,Chicago,american,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia,1 star,Chicago
+blackbird,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird,1 star,Chicago
+substans,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans,1 star,Aarhus
+stud!o at the standard,2019,Kobenhavn,Denmark,creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard,1 star,Kobenhavn
+the progress,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress,1 star,San Francisco
+ti trin ned,2019,Fredericia,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned,1 star,Fredericia
+olo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo,1 star,Helsingfors   Helsinki
+rich table,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table,1 star,San Francisco
+octavia,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia,1 star,San Francisco
+demo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo,1 star,Helsingfors   Helsinki
+grön,2019,Helsingfors   Helsinki,Finland,finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron,1 star,Helsingfors   Helsinki
+écriture,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture,2 stars,Hong Kong
+palace,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace,1 star,Helsingfors   Helsinki
+mister jiu's,2019,San Francisco,California,chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius,1 star,San Francisco
+sushi wadatsumi,2019,Hong Kong,Hong Kong,japanese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi,1 star,Hong Kong
+j'aime by jean-michel lorain,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain,1 star,Bangkok
+frederiksminde,2019,Praesto,Denmark,creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde,1 star,Praesto
+t'ang court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court,3 stars,Hong Kong
+savelberg,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg,1 star,Bangkok
+the eight,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight,3 stars,Macau
+ask,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask,1 star,Helsingfors   Helsinki
+ora,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora,1 star,Helsingfors   Helsinki
+nahm,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm,1 star,Bangkok
+xin rong ji,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji,1 star,Hong Kong
+tin lung heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen,2 stars,Hong Kong
+les amis,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis,2 stars,Singapore
+aster,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster,1 star,San Francisco
+épure,2019,nan,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure,1 star,nan
+summer palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace,1 star,Hong Kong
+alouette,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette,1 star,Kobenhavn
+tosca,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519,1 star,Hong Kong
+octavium,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium,1 star,Hong Kong
+spring moon,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon,1 star,Hong Kong
+shang palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace,1 star,Hong Kong
+ming court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court,1 star,Hong Kong
+nico,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico,1 star,San Francisco
+arbor,2019,nan,Hong Kong,international cuisine,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor,1 star,nan
+luce,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce,1 star,San Francisco
+yan toh heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen,2 stars,Hong Kong
+108,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108,1 star,Kobenhavn
+spruce,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce,1 star,San Francisco
+spqr,2019,San Francisco,California,italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr,1 star,San Francisco
+arcane,2019,Hong Kong,Hong Kong,other european,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane,1 star,Hong Kong
+the kitchen,2019,Macau,Macau,american,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810,1 star,Macau
+chim by siam wisdom,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom,1 star,Bangkok
+stand,2019,Budapest,Hungary,international cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand,1 star,Budapest
+sra bua by kiin kiin,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin,1 star,Bangkok
+belon,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon,1 star,Hong Kong
+paste,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste,1 star,Bangkok
+cut,2018,Singapore,Singapore,american,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut,1 star,Singapore
+8 1/2 otto e mezzo - bombana,2019,Macau,Macau,italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana,1 star,Macau
+sushi nomura,2019,Taipei,Taipei,japanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura,1 star,Taipei
+impromptu by paul lee,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee,1 star,Taipei
+the finch,2019,New York,New York City,american,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch,1 star,New York
+oxomoco,2019,New York,New York City,mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco,1 star,New York
+jewel bako,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako,1 star,New York
+ekstedt,2019,Stockholm,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt,1 star,Stockholm
+kontrast,2019,Oslo,Norway,scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast,1 star,Oslo
+jaan,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan,1 star,Singapore
+chef kang's,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s,1 star,Singapore
+galt,2019,Oslo,Norway,international cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt,1 star,Oslo
+siren by rw,2019,"Washington, D.C.",Washington DC,seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw,1 star,"Washington, D.C."
+sushi ichi,2018,Singapore,Singapore,japanese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi,1 star,Singapore
+exquisine,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine,1 star,Seoul
+pearl dragon,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon,1 star,Macau
+joo ok,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok,1 star,Seoul
+the village pub,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub,1 star,San Francisco
+muoki,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki,1 star,Seoul
+yu yuan,2019,Seoul,South Korea,chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan,1 star,Seoul
+l'amitié,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie,1 star,Seoul
+dosa,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871,1 star,Seoul
+poom,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom,1 star,Seoul
+zero complex,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex,1 star,Seoul
+soigné,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne,1 star,Seoul
+table for four,2019,Seoul,South Korea,other european,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four,1 star,Seoul
+the tasting room,2019,Macau,Macau,french,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room,2 stars,Macau
+kanoyama,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama,1 star,New York
+volt,2019,Stockholm,Sweden,creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt,1 star,Stockholm
+bicena,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena,1 star,Seoul
+the clocktower,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower,1 star,New York
+sushi sho,2019,Stockholm,Sweden,japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho,1 star,Stockholm
+casa mono,2019,New York,New York City,spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono,1 star,New York
+nix,2019,New York,New York City,vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix,1 star,New York
+bouley at home,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home,1 star,New York
+kosushi,2019,Sao Paulo,Sao Paulo,japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi,1 star,Sao Paulo
+taïrroir,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir,2 stars,Taipei
+cote,2019,New York,New York City,korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote,1 star,New York
+jiang-nan chun,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun,1 star,Singapore
+raw,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw,2 stars,Taipei
+le coucou,2019,New York,New York City,french,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou,1 star,New York
+l'atelier de joël robuchon,2019,Taipei,Taipei,french,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759,1 star,Taipei
+iggy's,2018,Singapore,Singapore,other european,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s,1 star,Singapore
+stay,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay,1 star,Seoul
+hirohisa,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa,1 star,New York
+da-wan,2019,Taipei,Taipei,american,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan,1 star,Taipei
+dining in space,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space,1 star,Seoul
+hansikgonggan,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan,1 star,Seoul
+kyo ya,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya,1 star,New York
+bhoga,2019,Goteborg,Sweden,creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga,1 star,Goteborg
+meadowsweet,2019,New York,New York City,other european,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet,1 star,New York
+protégé,2019,South San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege,1 star,South San Francisco
+thörnströms kök,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok,1 star,Goteborg
+sk mat & människor,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor,1 star,Goteborg
+28+,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28,1 star,Goteborg
+koka,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka,1 star,Goteborg
+guo fu lou,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828,1 star,Hong Kong
+kajitsu,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu,1 star,New York
+mume,2019,Taipei,Taipei,other european,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume,1 star,Taipei
+madera,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera,1 star,San Francisco
+sav,2019,Malmo,Sweden,creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav,1 star,Malmo
+ming fu,2019,Taipei,Taipei,other asian,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu,1 star,Taipei
+the musket room,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room,1 star,New York
+pm & vänner,2019,Vaxjo,Sweden,creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner,1 star,Vaxjo
+quince,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince,3 stars,San Francisco
+man wah,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah,1 star,Hong Kong
+costes downtown,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown,1 star,Budapest
+singlethread,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread,3 stars,San Francisco
+borkonyha winekitchen,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen,1 star,Budapest
+zz's clam bar,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar,1 star,New York
+manresa,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa,3 stars,South San Francisco
+shinji by kanesaka,2019,Macau,Macau,japanese,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka,1 star,Macau
+alinea,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea,3 stars,Chicago
+rech,2019,Hong Kong,Hong Kong,seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech,1 star,Hong Kong
+mandarin grill + bar,2019,Hong Kong,Hong Kong,other european,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar,1 star,Hong Kong
+fagn,2019,Trondheim,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn,1 star,Trondheim
+atelier crenn,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn,3 stars,San Francisco
+benu,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu,3 stars,San Francisco
+re-naa,2019,Stavanger,Norway,creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa,1 star,Stavanger
+credo,2019,Trondheim,Norway,creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo,1 star,Trondheim
+tuju,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju,2 stars,Sao Paulo
+gaon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon,3 stars,Seoul
+the french laundry,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry,3 stars,San Francisco
+gaggan,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan,2 stars,Bangkok
+the restaurant at meadowood,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood,3 stars,San Francisco
+bâtard,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard,1 star,New York
+geranium,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium,3 stars,Kobenhavn
+kosaka,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka,1 star,New York
+wallsé,2019,New York,New York City,austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse,1 star,New York
+okuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda,1 star,New York
+le grill de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon,1 star,New York
+del posto,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto,1 star,New York
+statholdergaarden,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden,1 star,Oslo
+sabi omakase,2019,Stavanger,Norway,japanese,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase,1 star,Stavanger
+sushi nakazawa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa,1 star,New York
+l'appart,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart,1 star,New York
+daniel berlin,2019,Skane Tranas,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin,2 stars,Skane Tranas
+aska,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska,2 stars,New York
+nomad,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad,1 star,New York
+sushi shikon,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon,3 stars,Hong Kong
+daniel,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel,2 stars,New York
+gramercy tavern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern,1 star,New York
+ai fiori,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori,1 star,New York
+the river café,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe,1 star,New York
+bar uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu,1 star,New York
+contra,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra,1 star,New York
+atomix,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix,1 star,New York
+agern,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern,1 star,New York
+caviar russe,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe,1 star,New York
+tempura matsui,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui,1 star,New York
+sushi yasuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda,1 star,New York
+sushi amane,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane,1 star,New York
+blue hill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill,1 star,New York
+café boulud,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud,1 star,New York
+vollmers,2019,Malmo,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers,2 stars,Malmo
+shoun ryugin,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin,2 stars,Taipei
+sushi amamoto,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto,2 stars,Taipei
+sühring,2019,Bangkok,Thailand,other european,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring,2 stars,Bangkok
+carbone,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone,1 star,New York
+le normandie,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie,2 stars,Bangkok
+pineapple and pearls,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls,2 stars,"Washington, D.C."
+atelier amaro,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro,1 star,Warszawa
+peter luger,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger,1 star,New York
+sushi inoue,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue,1 star,New York
+sushi noz,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz,1 star,New York
+satsuki,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki,1 star,New York
+minibar,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar,2 stars,"Washington, D.C."
+babbo,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo,1 star,New York
+lee jong kuk 104,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104,1 star,Seoul
+amador,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador,3 stars,Wien
+pfefferschiff,2019,Hallwang,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff,1 star,Hallwang
+shoukouwa,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa,2 stars,Singapore
+olympe,2019,Rio De Janeiro,Rio de Janeiro,french,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe,1 star,Rio De Janeiro
+lasai,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai,1 star,Rio De Janeiro
+ta vie,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie,2 stars,Hong Kong
+sushi saito,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito,2 stars,Hong Kong
+tenku ryugin,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin,2 stars,Hong Kong
+spondi,2019,Athina,Greece,french,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi,2 stars,Athina
+steirereck im stadtpark,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark,2 stars,Wien
+silvio nickol gourmet restaurant,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant,2 stars,Wien
+konstantin filippou,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou,2 stars,Wien
+mraz & sohn,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn,2 stars,Wien
+ikarus,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus,2 stars,Salzburg
+senns.restaurant,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant,2 stars,Salzburg
+pru,2019,Phuket,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru,1 star,Phuket
+sorn,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn,1 star,Bangkok
+costes,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes,1 star,Budapest
+bo.lan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan,1 star,Bangkok
+mosu,2019,Seoul,South Korea,international cuisine,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu,1 star,Seoul
+huto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto,1 star,Sao Paulo
+kan suke,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke,1 star,Sao Paulo
+kinoshita,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita,1 star,Sao Paulo
+tangará jean-georges,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges,1 star,Sao Paulo
+ryo gastronomia,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia,1 star,Sao Paulo
+picchi,2019,Sao Paulo,Sao Paulo,italian,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi,1 star,Sao Paulo
+evvai,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai,1 star,Sao Paulo
+maní,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani,1 star,Sao Paulo
+jun sakamoto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto,1 star,Sao Paulo
+cipriani,2019,Rio De Janeiro,Rio de Janeiro,italian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani,1 star,Rio De Janeiro
+mee,2019,Rio De Janeiro,Rio de Janeiro,other asian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee,1 star,Rio De Janeiro
+oteque,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque,1 star,Rio De Janeiro
+kadeau bornholm,2019,Pedersker,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm,1 star,Pedersker
+upstairs at mikkeller,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller,1 star,Bangkok
+oro,2019,Rio De Janeiro,Rio de Janeiro,creative,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro,2 stars,Rio De Janeiro
+noel,2019,Zagreb,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel,1 star,Zagreb
+carpe diem,2019,Salzburg,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem,1 star,Salzburg
+d.o.m.,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m,2 stars,Sao Paulo
+kojima,2019,Seoul,South Korea,japanese,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima,2 stars,Seoul
+waku ghin,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin,2 stars,Singapore
+mezzaluna,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna,2 stars,Bangkok
+bo innovation,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation,3 stars,Hong Kong
+kilian stuba,2019,Kleinwalsertal,Austria,creative,$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba,1 star,Kleinwalsertal
+onyx,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx,2 stars,Budapest
+the ocean,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean,1 star,Hong Kong
+tate,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate,1 star,Hong Kong
+kaiseki den by saotome,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome,1 star,Hong Kong
+vea,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea,1 star,Hong Kong
+takumi by daisuke mori,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori,1 star,Hong Kong
+sushi tokami,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami,1 star,Hong Kong
+hytra,2019,Athina,Greece,international cuisine,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra,1 star,Athina
+esszimmer,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer,1 star,Salzburg
+varoulko seaside,2019,Athina,Greece,seafood,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside,1 star,Athina
+la degustation bohême bourgeoise,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise,1 star,Praha
+field,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field,1 star,Praha
+360º,2019,Dubrovnik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA,1 star,Dubrovnik
+babel,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel,1 star,Budapest
+pelegrini,2019,Sibenik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini,1 star,Sibenik
+draga di lovrana,2019,Lovran,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana,1 star,Lovran
+monte,2019,Rovinj,Croatia,creative,$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte,1 star,Rovinj
+le ciel by toni mörwald,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald,1 star,Wien
+aend,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend,1 star,Wien
+tian,2019,Wien,Austria,vegetarian,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian,1 star,Wien
+shiki,2019,Wien,Austria,japanese,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki,1 star,Wien
+walter bauer,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer,1 star,Wien
+pramerl & the wolf,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf,1 star,Wien
+das loft,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft,1 star,Wien
+botrini's,2019,Athina,Greece,other european,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s,1 star,Athina
+gotham bar and grill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill,1 star,New York
+a‚o‚c,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac,2 stars,Kobenhavn
+junoon,2019,New York,New York City,indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon,1 star,New York
+r-haan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan,1 star,Bangkok
+canvas,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292,1 star,Bangkok
+fäviken magasinet,2019,Jarpen,Sweden,creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet,2 stars,Jarpen
+elements,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements,1 star,Bangkok
+ginza sushi ichi,2019,Bangkok,Thailand,japanese,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi,1 star,Bangkok
+blanca,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca,2 stars,New York
+komi,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi,1 star,"Washington, D.C."
+sushi taro,2019,"Washington, D.C.",Washington DC,japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro,1 star,"Washington, D.C."
+plume,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume,1 star,"Washington, D.C."
+shinji (tanglin road),2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road,1 star,Singapore
+coi,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi,2 stars,San Francisco
+jean-georges,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges,2 stars,New York
+atera,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera,2 stars,New York
+jungsik,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik,2 stars,New York
+gastrologik,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik,2 stars,Stockholm
+campton place,2019,San Francisco,California,indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place,2 stars,San Francisco
+saison,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison,2 stars,San Francisco
+lazy bear,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear,2 stars,San Francisco
+californios,2019,San Francisco,California,mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios,2 stars,San Francisco
+commis,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis,2 stars,San Francisco
+baumé,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume,2 stars,South San Francisco
+odette,2018,Singapore,Singapore,french,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette,2 stars,Singapore
+fiola,2019,"Washington, D.C.",Washington DC,italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola,1 star,"Washington, D.C."
+upper house,2019,Goteborg,Sweden,creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house,1 star,Goteborg
+métier,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier,1 star,"Washington, D.C."
+operakällaren,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren,1 star,Stockholm
+urasawa,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa,2 stars,Los Angeles
+the inn at little washington,2019,"Washington, D.C.",Washington DC,american,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington,3 stars,"Washington, D.C."
+noda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda,1 star,New York
+oaxen krog,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog,2 stars,Stockholm
+l'atelier de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505,2 stars,New York
+dialogue,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue,1 star,Los Angeles
+gaa,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa,1 star,Bangkok
+aloë,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe,1 star,Stockholm
+n/naka,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka,2 stars,Los Angeles
+aquavit,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit,2 stars,New York
+sushi kimura,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura,1 star,Singapore
+smyth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth,2 stars,Chicago
+providence,2019,Los Angeles,California,seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence,2 stars,Los Angeles
+vespertine,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine,2 stars,Los Angeles
+oriole,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole,2 stars,Chicago
+gabriel kreuther,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther,2 stars,New York
+pierre,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre,2 stars,Hong Kong
+koks,2019,Leynar,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780,2 stars,Leynar
+alain ducasse at morpheus,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus,2 stars,Macau
+noma,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma,2 stars,Kobenhavn
+the modern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern,2 stars,New York
+kadeau copenhagen,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen,2 stars,Kobenhavn
+kashiwaya,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya,2 stars,Hong Kong
+ko,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko,2 stars,New York
+somni,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni,2 stars,Los Angeles
+ichimura at uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu,2 stars,New York
+marea,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea,2 stars,New York
+acadia,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia,2 stars,Chicago
+sushi ginza onodera,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850,2 stars,Los Angeles
+agrikultur,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur,1 star,Stockholm
+acquerello,2019,San Francisco,California,italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello,2 stars,San Francisco
+le du,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du,1 star,Bangkok
+saawaan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan,1 star,Bangkok
+kitcho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho,1 star,Taipei
+logy,2019,Taipei,Taipei,other asian,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy,1 star,Taipei
+ken an ho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho,1 star,Taipei
+sushi ryu,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu,1 star,Taipei
+amber,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032,2 stars,Hong Kong
+sushi ginza onodera,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera,2 stars,New York
+aldea,2019,New York,New York City,other european,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea,1 star,New York
+cut,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418,1 star,Los Angeles
+shunji,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji,1 star,Los Angeles
+eleven madison park,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park,3 stars,New York
+le bernardin,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin,3 stars,New York
+per se,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se,3 stars,New York
+masa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa,3 stars,New York
+maaemo,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo,3 stars,Oslo
+chez tj,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj,1 star,South San Francisco
+plumed horse,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse,1 star,South San Francisco
+wakuriya,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya,1 star,San Francisco
+sushi yoshizumi,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi,1 star,San Francisco
+maum,2019,South San Francisco,California,korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum,1 star,South San Francisco
+omakase,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase,1 star,San Francisco
+birdsong,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong,1 star,San Francisco
+in situ,2019,San Francisco,California,international cuisine,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ,1 star,San Francisco
+auberge du soleil,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil,1 star,San Francisco
+chef's table at brooklyn fare,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare,3 stars,New York
+la toque,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque,1 star,San Francisco
+aubergine,2019,Monterey,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine,1 star,Monterey
+madcap,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap,1 star,San Francisco
+wako,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako,1 star,San Francisco
+clou,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou,1 star,Kobenhavn
+gary danko,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko,1 star,San Francisco
+keiko à nob hill,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill,1 star,San Francisco
+jū-ni,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni,1 star,San Francisco
+sons & daughters,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters,1 star,San Francisco
+michael mina,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina,1 star,San Francisco
+angler,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler,1 star,San Francisco
+hashiri,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri,1 star,San Francisco
+kinjo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo,1 star,San Francisco
+kong hans kælder,2019,Kobenhavn,Denmark,french,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder,1 star,Kobenhavn
+frederikshøj,2019,Aarhus,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj,1 star,Aarhus
+kenzo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo,1 star,San Francisco
+nozawa bar,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar,1 star,Los Angeles
+edvard,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard,1 star,Wien
+frantzén,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen,3 stars,Stockholm
+maude,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude,1 star,Los Angeles
+mori sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi,1 star,Los Angeles
+trois mec,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec,1 star,Los Angeles
+le comptoir,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir,1 star,Los Angeles
+q sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi,1 star,Los Angeles
+shibumi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644,1 star,Los Angeles
+hayato,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato,1 star,Los Angeles
+orsa & winston,2019,Los Angeles,California,other asian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston,1 star,Los Angeles
+hana re,2019,Costa Mesa,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re,1 star,Costa Mesa
+addison,2019,San Diego,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison,1 star,San Diego
+harbor house,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house,1 star,San Francisco
+goosefoot,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot,1 star,Chicago
+el ideas,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas,1 star,Chicago
+schwa,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa,1 star,Chicago
+la yeon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon,3 stars,Seoul
+temporis,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis,1 star,Chicago
+everest,2019,Chicago,Chicago,french,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest,1 star,Chicago
+topolobampo,2019,Chicago,Chicago,mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo,1 star,Chicago
+spiaggia,2019,Chicago,Chicago,italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia,1 star,Chicago
+robuchon au dôme,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome,3 stars,Macau
+l'atelier de joël robuchon,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon,3 stars,Hong Kong
+8½ otto e mezzo - bombana,2019,Hong Kong,Hong Kong,italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana,3 stars,Hong Kong
+caprice,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice,3 stars,Hong Kong
+lung king heen,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen,3 stars,Hong Kong
+gotgan,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan,1 star,Seoul
+slotskøkkenet,2019,Horve,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet,1 star,Horve
+elizabeth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth,1 star,Chicago
+madrona manor,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor,1 star,San Francisco
+the kitchen,2019,Sacramento,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371,1 star,Sacramento
+farmhouse inn & restaurant,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant,1 star,San Francisco
+elske,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske,1 star,Chicago
+senses,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses,1 star,Warszawa
+aniar,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar,1 star,Gaillimh Galway
+loam,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam,1 star,Gaillimh Galway
+wild honey inn,2019,Lios Duin Bhearna Lisdoonvarna,Ireland,international cuisine,,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn,1 star,Lios Duin Bhearna Lisdoonvarna
+chestnut,2019,Ballydehob,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut,1 star,Ballydehob
+mews,2019,Baltimore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews,1 star,Baltimore
+ichigo ichie,2019,Corcaigh Cork,Ireland,japanese,,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie,1 star,Corcaigh Cork
+chapter one,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one,1 star,City Centre
+greenhouse,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse,1 star,City Centre
+l'ecrivain,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain,1 star,City Centre
+campagne,2019,Cill Chainnigh Kilkenny,Ireland,british,,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne,1 star,Cill Chainnigh Kilkenny
+heron & grey,2019,Blackrock,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey,1 star,Blackrock
+lady helen,2019,Baile Mhic Andain Thomastown,Ireland,international cuisine,,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen,1 star,Baile Mhic Andain Thomastown
+house,2019,Aird Mhor Ardmore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house,1 star,Aird Mhor Ardmore
+loch bay,2019,Waternish,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay,1 star,Waternish
+braidwoods,2019,Dalry,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods,1 star,Dalry
+eipic,2019,Belfast,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic,1 star,Belfast
+ox,2019,Belfast,United Kingdom,british,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109,1 star,Belfast
+the peat inn,2019,Peat Inn,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn,1 star,Peat Inn
+kitchin,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin,1 star,Leith
+martin wishart,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart,1 star,Leith
+number one,2019,Edinburgh,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one,1 star,Edinburgh
+21212,2019,Edinburgh,United Kingdom,creative,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212,1 star,Edinburgh
+the cellar,2019,Anstruther,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar,1 star,Anstruther
+forest side,2019,Grasmere,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side,1 star,Grasmere
+hrishi,2019,Bowness On Windermere,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi,1 star,Bowness On Windermere
+rogan & co,2019,Cartmel,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co,1 star,Cartmel
+house of tides,2019,Newcastle Upon Tyne,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides,1 star,Newcastle Upon Tyne
+sosban & the old butchers,2019,Menai Bridge Porthaethwy,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers,1 star,Menai Bridge Porthaethwy
+northcote,2019,Langho,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote,1 star,Langho
+yorke arms,2019,Pateley Bridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms,1 star,Pateley Bridge
+fraiche,2019,Birkenhead,United Kingdom,creative,,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche,1 star,Birkenhead
+white swan,2019,Fence,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan,1 star,Fence
+tyddyn llan,2019,Llandrillo,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan,1 star,Llandrillo
+ynyshir,2019,Machynlleth,United Kingdom,creative,,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir,1 star,Machynlleth
+black swan,2019,Oldstead,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan,1 star,Oldstead
+simon radley at chester grosvenor,2019,Chester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor,1 star,Chester
+star inn at harome,2019,Harome,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome,1 star,Harome
+the man behind the curtain,2019,Leeds,United Kingdom,creative,,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain,1 star,Leeds
+the checkers,2019,Montgomery Trefaldwyn,United Kingdom,french,,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers,1 star,Montgomery Trefaldwyn
+pipe and glass,2019,South Dalton,United Kingdom,british,,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass,1 star,South Dalton
+fischer's at baslow hall,2019,Baslow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall,1 star,Baslow
+winteringham fields,2019,Winteringham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields,1 star,Winteringham
+thomas carr @ the olive room,2019,Ilfracombe,United Kingdom,seafood,,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room,1 star,Ilfracombe
+walnut tree,2019,Llanddewi Skirrid,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree,1 star,Llanddewi Skirrid
+paul ainsworth at no.6,2019,Padstow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6,1 star,Padstow
+simpsons,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons,1 star,Birmingham
+purnell's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s,1 star,Birmingham
+adam's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s,1 star,Birmingham
+outlaw's fish kitchen,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen,1 star,Port Isaac
+carters of moseley,2019,Birmingham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley,1 star,Birmingham
+peel's,2019,Hampton In Arden,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s,1 star,Hampton In Arden
+john's house,2019,Mountsorrel,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house,1 star,Mountsorrel
+the whitebrook,2019,Whitebrook,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook,1 star,Whitebrook
+james sommerin,2019,Penarth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin,1 star,Penarth
+driftwood,2019,Portscatho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood,1 star,Portscatho
+the cross at kenilworth,2019,Kenilworth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth,1 star,Kenilworth
+masons arms,2019,Knowstone,United Kingdom,french,,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms,1 star,Knowstone
+salt,2019,Stratford Upon Avon,United Kingdom,british,,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869,1 star,Stratford Upon Avon
+le champignon sauvage,2019,Cheltenham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage,1 star,Cheltenham
+gidleigh park,2019,Chagford,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park,1 star,Chagford
+hambleton hall,2019,Upper Hambleton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall,1 star,Upper Hambleton
+wilks,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks,1 star,Bristol
+bulrush,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush,1 star,Bristol
+casamia,2019,Bristol,United Kingdom,creative,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia,1 star,Bristol
+paco tapas,2019,Bristol,United Kingdom,spanish,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas,1 star,Bristol
+pony & trap,2019,Chew Magna,United Kingdom,british,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap,1 star,Chew Magna
+the dining room,2019,Malmesbury,United Kingdom,other asian,,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454,1 star,Malmesbury
+bybrook,2019,Castle Combe,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook,1 star,Castle Combe
+restaurant hywel jones by lucknam park,2019,Colerne,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park,1 star,Colerne
+olive tree,2019,Bath,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree,1 star,Bath
+lympstone manor,2019,Lympstone,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor,1 star,Lympstone
+the neptune,2019,Hunstanton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune,1 star,Hunstanton
+elephant,2019,Torquay,United Kingdom,british,,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant,1 star,Torquay
+oxford kitchen,2019,Oxford,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen,1 star,Oxford
+nut tree,2019,Murcott,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree,1 star,Murcott
+morston hall,2019,Morston,United Kingdom,british,,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall,1 star,Morston
+red lion freehouse,2019,East Chisenbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse,1 star,East Chisenbury
+blackbird,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018,1 star,Newbury
+woodspeen,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen,1 star,Newbury
+the coach,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach,1 star,Marlow
+crown,2019,Burchett'S Green,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549,1 star,Burchett'S Green
+l'ortolan,2019,Shinfield,United Kingdom,french,,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan,1 star,Shinfield
+hinds head,2019,Bray,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head,1 star,Bray
+black rat,2019,Winchester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat,1 star,Winchester
+matt worswick at the latymer,2019,Bagshot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer,1 star,Bagshot
+coworth park,2019,Ascot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park,1 star,Ascot
+tudor room,2019,Egham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room,1 star,Egham
+the glasshouse,2019,Kew,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse,1 star,Kew
+la trompette,2019,Chiswick,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette,1 star,Chiswick
+tim allen's flitch of bacon,2019,Little Dunmow,United Kingdom,british,,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon,1 star,Little Dunmow
+river café,2019,Hammersmith,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe,1 star,Hammersmith
+kitchen w8,2019,Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8,1 star,Kensington
+trishna,2019,Marylebone,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna,1 star,Marylebone
+roganic,2019,Marylebone,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic,1 star,Marylebone
+locanda locatelli,2019,Marylebone,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli,1 star,Marylebone
+texture,2019,Marylebone,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture,1 star,Marylebone
+portland,2019,Regent'S Park,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland,1 star,Regent'S Park
+harwood arms,2019,Fulham,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms,1 star,Fulham
+pied à terre,2019,Bloomsbury,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre,1 star,Bloomsbury
+the ninth,2019,Bloomsbury,United Kingdom,other european,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth,1 star,Bloomsbury
+kai,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638,1 star,Mayfair
+pollen street social,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social,1 star,Mayfair
+hakkasan mayfair,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair,1 star,Mayfair
+the square,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391,1 star,Mayfair
+alyn williams at the westbury,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury,1 star,Mayfair
+benares,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares,1 star,Mayfair
+hakkasan hanway place,2019,Bloomsbury,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place,1 star,Bloomsbury
+social eating house,2019,Soho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house,1 star,Soho
+galvin at windows,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows,1 star,Mayfair
+murano,2019,Mayfair,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano,1 star,Mayfair
+marcus,2019,Belgravia,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus,1 star,Belgravia
+sabor,2019,Mayfair,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor,1 star,Mayfair
+clock house,2019,Ripley,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house,1 star,Ripley
+yauatcha soho,2019,Soho,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho,1 star,Soho
+céleste,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste,1 star,Belgravia
+gymkhana,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana,1 star,Mayfair
+amaya,2019,Belgravia,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya,1 star,Belgravia
+pétrus,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688,1 star,Belgravia
+veeraswamy,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy,1 star,Mayfair
+barrafina,2019,Soho,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina,1 star,Soho
+hide,2019,Mayfair,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide,1 star,Mayfair
+ritz restaurant,2019,Westminster,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant,1 star,Westminster
+elystan street,2019,Chelsea,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street,1 star,Chelsea
+seven park place,2019,Saint James'S,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place,1 star,Saint James'S
+ikoyi,2019,Saint James'S,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi,1 star,Saint James'S
+aquavit,2019,Saint James'S,United Kingdom,scandinavian,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082,1 star,Saint James'S
+five fields,2019,Chelsea,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields,1 star,Chelsea
+dining room at the goring,2019,Victoria,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring,1 star,Victoria
+st john,2019,Clerkenwell,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john,1 star,Clerkenwell
+quilon,2019,Victoria,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon,1 star,Victoria
+club gascon,2019,London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon,1 star,London
+a. wong,2019,Victoria,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong,1 star,Victoria
+the clove club,2019,Shoreditch,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club,1 star,Shoreditch
+leroy,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy,1 star,Shoreditch
+angler,2019,Finsbury,United Kingdom,seafood,,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235,1 star,Finsbury
+brat,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat,1 star,Shoreditch
+lyle's,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s,1 star,Shoreditch
+galvin la chapelle,2019,Spitalfields,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle,1 star,Spitalfields
+city social,2019,City Of London,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social,1 star,City Of London
+la dame de pic,2019,City Of London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic,1 star,City Of London
+story,2019,Bermondsey,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story,1 star,Bermondsey
+trinity,2019,Clapham Common,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity,1 star,Clapham Common
+chez bruce,2019,Wandsworth,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce,1 star,Wandsworth
+sorrel,2019,Dorking,United Kingdom,british,,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459,1 star,Dorking
+restaurant tristan,2019,Horsham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan,1 star,Horsham
+gravetye manor,2019,Gravetye,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor,1 star,Gravetye
+the sportsman,2019,Seasalter,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman,1 star,Seasalter
+west house,2019,Biddenden,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house,1 star,Biddenden
+fordwich arms,2019,Fordwich,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms,1 star,Fordwich
+samphire,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987,1 star,Saint Helier Saint Helier
+bohemia,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia,1 star,Saint Helier Saint Helier
+patrick guilbaud,2019,City Centre,Ireland,french,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud,2 stars,City Centre
+andrew fairlie at gleneagles,2019,Auchterarder,United Kingdom,french,,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles,2 stars,Auchterarder
+l'enclume,2019,Cartmel,United Kingdom,creative,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume,2 stars,Cartmel
+raby hunt,2019,Summerhouse,United Kingdom,british,,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt,2 stars,Summerhouse
+moor hall,2019,Aughton,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall,2 stars,Aughton
+restaurant sat bains,2019,Nottingham,United Kingdom,creative,,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains,2 stars,Nottingham
+restaurant nathan outlaw,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw,2 stars,Port Isaac
+belmond le manoir aux quat' saisons,2019,Great Milton,United Kingdom,french,,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons,2 stars,Great Milton
+midsummer house,2019,Cambridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house,2 stars,Cambridge
+hand and flowers,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers,2 stars,Marlow
+ledbury,2019,North Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury,2 stars,North Kensington
+core by clare smyth,2019,North Kensington,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth,2 stars,North Kensington
+le gavroche,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche,2 stars,Mayfair
+kitchen table at bubbledogs,2019,Bloomsbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs,2 stars,Bloomsbury
+hélène darroze at the connaught,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught,2 stars,Mayfair
+dinner by heston blumenthal,2019,Hyde Park,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal,2 stars,Hyde Park
+umu,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu,2 stars,Mayfair
+sketch (the lecture room & library),2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library,2 stars,Mayfair
+greenhouse,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393,2 stars,Mayfair
+claude bosi at bibendum,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum,2 stars,Chelsea
+fat duck,2019,Bray,United Kingdom,creative,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck,3 stars,Bray
+waterside inn,2019,Bray,United Kingdom,french,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn,3 stars,Bray
+alain ducasse at the dorchester,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester,3 stars,Mayfair
+the araki,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki,3 stars,Mayfair
+gordon ramsay,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay,3 stars,Chelsea
+=======
+name,year,city,region,cuisine,price,url,stars,major_city
+ho hung kee,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee,1 star,Hong Kong
+feng wei ju,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju,2 stars,Macau
+imperial treasure fine teochew cuisine (orchard),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard,1 star,Singapore
+shisen hanten,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten,2 stars,Singapore
+ma cuisine,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine,1 star,Singapore
+alma,2018,Singapore,Singapore,other european,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma,1 star,Singapore
+lei garden,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509,1 star,Singapore
+the song of india,2018,Singapore,Singapore,indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india,1 star,Singapore
+putien (kitchener road),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road,1 star,Singapore
+hill street tai hwa pork noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle,1 star,Singapore
+jin jin,2019,Seoul,South Korea,chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin,1 star,Seoul
+summer palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658,1 star,Singapore
+cheek by jowl,2018,Singapore,Singapore,australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl,1 star,Singapore
+nouri,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri,1 star,Singapore
+liao fan hong kong soya sauce chicken rice & noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle,1 star,Singapore
+garibaldi,2018,Singapore,Singapore,italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi,1 star,Singapore
+candlenut,2018,Singapore,Singapore,other asian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut,1 star,Singapore
+rhubarb,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb,1 star,Singapore
+tim ho wan (sham shui po),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po,1 star,Hong Kong
+lei garden (mong kok),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok,1 star,Hong Kong
+yè shanghai (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui,1 star,Hong Kong
+crystal jade golden palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace,1 star,Singapore
+pang's kitchen,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen,1 star,Hong Kong
+qi (wan chai),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai,1 star,Hong Kong
+kam's roast goose,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose,1 star,Hong Kong
+yat lok,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok,1 star,Hong Kong
+king,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226,1 star,Macau
+the golden peacock,2019,Macau,Macau,indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock,1 star,Macau
+suan thip,2019,Bangkok,Thailand,other asian,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip,1 star,Bangkok
+da san yuan,2019,Taipei,Taipei,chinese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan,1 star,Taipei
+lei garden (kwun tong),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong,1 star,Hong Kong
+tainan tan tsu mien seafood,2019,Taipei,Taipei,seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood,1 star,Taipei
+parachute,2019,Chicago,Chicago,other asian,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute,1 star,Chicago
+beefbar,2019,Hong Kong,Hong Kong,american,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar,1 star,Hong Kong
+claro,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro,1 star,New York
+yee tung heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen,1 star,Hong Kong
+the guest house,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house,2 stars,Taipei
+lai heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen,1 star,Macau
+ying,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying,1 star,Macau
+fu ho (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui,1 star,Hong Kong
+im teppanyaki & wine,2019,Hong Kong,Hong Kong,japanese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine,1 star,Hong Kong
+ah yat harbour view (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui,1 star,Hong Kong
+methavalai sorndaeng,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng,1 star,Bangkok
+imperial treasure fine chinese cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine,1 star,Hong Kong
+zi yat heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen,1 star,Macau
+corner house,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house,1 star,Singapore
+yat tung heen (jordan),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan,1 star,Hong Kong
+jade dragon,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon,3 stars,Macau
+balwoo gongyang,2019,Seoul,South Korea,korean,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang,1 star,Seoul
+le palais,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais,3 stars,Taipei
+bistro na's,2019,Los Angeles,California,chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s,1 star,Los Angeles
+kin khao,2019,San Francisco,California,other asian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao,1 star,San Francisco
+commonwealth,2019,San Francisco,California,contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth,1 star,San Francisco
+béni,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni,1 star,Singapore
+forum,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum,2 stars,Hong Kong
+labyrinth,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth,1 star,Singapore
+braci,2018,Singapore,Singapore,italian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci,1 star,Singapore
+sun tung lok,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok,2 stars,Hong Kong
+casa enríque,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique,1 star,New York
+meta,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta,1 star,Singapore
+bacchanalia,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia,1 star,Singapore
+shinji (bras basah road),2018,Singapore,Singapore,japanese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road,1 star,Singapore
+summer pavilion,2018,Singapore,Singapore,chinese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion,1 star,Singapore
+state bird provisions,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions,1 star,San Francisco
+faro,2019,New York,New York City,american,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro,1 star,New York
+saint pierre,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre,1 star,Singapore
+whitegrass,2018,Singapore,Singapore,australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass,1 star,Singapore
+rose's luxury,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury,1 star,"Washington, D.C."
+burnt ends,2018,Singapore,Singapore,american,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends,1 star,Singapore
+zhejiang heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen,1 star,Hong Kong
+uncle boons,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons,1 star,New York
+bloom in the park,2019,Malmo,Sweden,creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park,1 star,Malmo
+golden formosa,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa,1 star,Taipei
+ruean panya,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya,1 star,Bangkok
+søllerød kro,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro,1 star,Kobenhavn
+relæ,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae,1 star,Kobenhavn
+kiin kiin,2019,Kobenhavn,Denmark,other asian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin,1 star,Kobenhavn
+formel b,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b,1 star,Kobenhavn
+danny's steakhouse,2019,Taipei,Taipei,american,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse,1 star,Taipei
+marchal,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal,1 star,Kobenhavn
+tien hsiang lo,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo,1 star,Taipei
+era ora,2019,Kobenhavn,Denmark,italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora,1 star,Kobenhavn
+ya ge,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge,1 star,Taipei
+ying jee club,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club,2 stars,Hong Kong
+jardin de jade (wan chai),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai,1 star,Hong Kong
+tail up goat,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat,1 star,"Washington, D.C."
+mathias dahlgren-matbaren,2019,Stockholm,Sweden,international cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren,1 star,Stockholm
+jeju noodle bar,2019,New York,New York City,korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar,1 star,New York
+saneh jaan,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan,1 star,Bangkok
+mizumi (macau),2019,Macau,Macau,japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau,2 stars,Macau
+al's place,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place,1 star,San Francisco
+wing lei,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei,1 star,Macau
+golden flower,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower,2 stars,Macau
+tim's kitchen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen,1 star,Macau
+celebrity cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine,1 star,Hong Kong
+mountain and sea house,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house,1 star,Taipei
+rasa,2019,San Francisco,California,indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa,1 star,San Francisco
+jay fai,2019,Bangkok,Thailand,international cuisine,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai,1 star,Bangkok
+longtail,2019,Taipei,Taipei,international cuisine,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail,1 star,Taipei
+tuome,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome,1 star,New York
+dusek's (board & beer),2019,Chicago,Chicago,gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer,1 star,Chicago
+café china,2019,New York,New York City,chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china,1 star,New York
+the dabney,2019,"Washington, D.C.",Washington DC,american,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney,1 star,"Washington, D.C."
+bresca,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca,1 star,"Washington, D.C."
+loaf on,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on,1 star,Hong Kong
+new punjab club,2019,Hong Kong,Hong Kong,indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club,1 star,Hong Kong
+kwonsooksoo,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo,2 stars,Seoul
+shin sushi,2019,Los Angeles,California,japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi,1 star,Los Angeles
+osteria mozza,2019,Los Angeles,California,italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza,1 star,Los Angeles
+kali,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali,1 star,Los Angeles
+bouchon,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon,1 star,San Francisco
+taco maría,2019,Costa Mesa,California,mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria,1 star,Costa Mesa
+bar crenn,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn,1 star,San Francisco
+alla prima,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima,2 stars,Seoul
+henne kirkeby kro,2019,Henne,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro,2 stars,Henne
+band of bohemia,2019,Chicago,Chicago,gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia,1 star,Chicago
+masseria,2019,"Washington, D.C.",Washington DC,italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria,1 star,"Washington, D.C."
+entente,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente,1 star,Chicago
+kinship,2019,"Washington, D.C.",Washington DC,contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship,1 star,"Washington, D.C."
+rustic canyon,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon,1 star,Los Angeles
+mingles,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles,2 stars,Seoul
+duddell's,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s,1 star,Hong Kong
+mourad,2019,San Francisco,California,moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad,1 star,San Francisco
+boka,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka,1 star,Chicago
+roister,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister,1 star,Chicago
+jungsik,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965,2 stars,Seoul
+sorrel,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel,1 star,San Francisco
+lord stanley,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley,1 star,San Francisco
+north pond,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond,1 star,Chicago
+blue duck tavern,2019,"Washington, D.C.",Washington DC,american,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern,1 star,"Washington, D.C."
+kato,2019,Los Angeles,California,other asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato,1 star,Los Angeles
+domestic,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic,1 star,Aarhus
+me‚mu,2019,Vejle,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu,1 star,Vejle
+jordnær,2019,Kobenhavn,Denmark,danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer,1 star,Kobenhavn
+kokkeriet,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet,1 star,Kobenhavn
+gastromé,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome,1 star,Aarhus
+sepia,2019,Chicago,Chicago,american,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia,1 star,Chicago
+blackbird,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird,1 star,Chicago
+substans,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans,1 star,Aarhus
+stud!o at the standard,2019,Kobenhavn,Denmark,creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard,1 star,Kobenhavn
+the progress,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress,1 star,San Francisco
+ti trin ned,2019,Fredericia,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned,1 star,Fredericia
+olo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo,1 star,Helsingfors   Helsinki
+rich table,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table,1 star,San Francisco
+octavia,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia,1 star,San Francisco
+demo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo,1 star,Helsingfors   Helsinki
+grön,2019,Helsingfors   Helsinki,Finland,finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron,1 star,Helsingfors   Helsinki
+écriture,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture,2 stars,Hong Kong
+palace,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace,1 star,Helsingfors   Helsinki
+mister jiu's,2019,San Francisco,California,chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius,1 star,San Francisco
+sushi wadatsumi,2019,Hong Kong,Hong Kong,japanese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi,1 star,Hong Kong
+j'aime by jean-michel lorain,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain,1 star,Bangkok
+frederiksminde,2019,Praesto,Denmark,creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde,1 star,Praesto
+t'ang court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court,3 stars,Hong Kong
+savelberg,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg,1 star,Bangkok
+the eight,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight,3 stars,Macau
+ask,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask,1 star,Helsingfors   Helsinki
+ora,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora,1 star,Helsingfors   Helsinki
+nahm,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm,1 star,Bangkok
+xin rong ji,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji,1 star,Hong Kong
+tin lung heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen,2 stars,Hong Kong
+les amis,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis,2 stars,Singapore
+aster,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster,1 star,San Francisco
+épure,2019,nan,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure,1 star,nan
+summer palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace,1 star,Hong Kong
+alouette,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette,1 star,Kobenhavn
+tosca,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519,1 star,Hong Kong
+octavium,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium,1 star,Hong Kong
+spring moon,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon,1 star,Hong Kong
+shang palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace,1 star,Hong Kong
+ming court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court,1 star,Hong Kong
+nico,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico,1 star,San Francisco
+arbor,2019,nan,Hong Kong,international cuisine,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor,1 star,nan
+luce,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce,1 star,San Francisco
+yan toh heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen,2 stars,Hong Kong
+108,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108,1 star,Kobenhavn
+spruce,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce,1 star,San Francisco
+spqr,2019,San Francisco,California,italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr,1 star,San Francisco
+arcane,2019,Hong Kong,Hong Kong,other european,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane,1 star,Hong Kong
+the kitchen,2019,Macau,Macau,american,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810,1 star,Macau
+chim by siam wisdom,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom,1 star,Bangkok
+stand,2019,Budapest,Hungary,international cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand,1 star,Budapest
+sra bua by kiin kiin,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin,1 star,Bangkok
+belon,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon,1 star,Hong Kong
+paste,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste,1 star,Bangkok
+cut,2018,Singapore,Singapore,american,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut,1 star,Singapore
+8 1/2 otto e mezzo - bombana,2019,Macau,Macau,italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana,1 star,Macau
+sushi nomura,2019,Taipei,Taipei,japanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura,1 star,Taipei
+impromptu by paul lee,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee,1 star,Taipei
+the finch,2019,New York,New York City,american,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch,1 star,New York
+oxomoco,2019,New York,New York City,mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco,1 star,New York
+jewel bako,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako,1 star,New York
+ekstedt,2019,Stockholm,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt,1 star,Stockholm
+kontrast,2019,Oslo,Norway,scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast,1 star,Oslo
+jaan,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan,1 star,Singapore
+chef kang's,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s,1 star,Singapore
+galt,2019,Oslo,Norway,international cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt,1 star,Oslo
+siren by rw,2019,"Washington, D.C.",Washington DC,seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw,1 star,"Washington, D.C."
+sushi ichi,2018,Singapore,Singapore,japanese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi,1 star,Singapore
+exquisine,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine,1 star,Seoul
+pearl dragon,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon,1 star,Macau
+joo ok,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok,1 star,Seoul
+the village pub,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub,1 star,San Francisco
+muoki,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki,1 star,Seoul
+yu yuan,2019,Seoul,South Korea,chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan,1 star,Seoul
+l'amitié,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie,1 star,Seoul
+dosa,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871,1 star,Seoul
+poom,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom,1 star,Seoul
+zero complex,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex,1 star,Seoul
+soigné,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne,1 star,Seoul
+table for four,2019,Seoul,South Korea,other european,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four,1 star,Seoul
+the tasting room,2019,Macau,Macau,french,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room,2 stars,Macau
+kanoyama,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama,1 star,New York
+volt,2019,Stockholm,Sweden,creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt,1 star,Stockholm
+bicena,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena,1 star,Seoul
+the clocktower,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower,1 star,New York
+sushi sho,2019,Stockholm,Sweden,japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho,1 star,Stockholm
+casa mono,2019,New York,New York City,spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono,1 star,New York
+nix,2019,New York,New York City,vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix,1 star,New York
+bouley at home,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home,1 star,New York
+kosushi,2019,Sao Paulo,Sao Paulo,japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi,1 star,Sao Paulo
+taïrroir,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir,2 stars,Taipei
+cote,2019,New York,New York City,korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote,1 star,New York
+jiang-nan chun,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun,1 star,Singapore
+raw,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw,2 stars,Taipei
+le coucou,2019,New York,New York City,french,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou,1 star,New York
+l'atelier de joël robuchon,2019,Taipei,Taipei,french,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759,1 star,Taipei
+iggy's,2018,Singapore,Singapore,other european,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s,1 star,Singapore
+stay,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay,1 star,Seoul
+hirohisa,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa,1 star,New York
+da-wan,2019,Taipei,Taipei,american,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan,1 star,Taipei
+dining in space,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space,1 star,Seoul
+hansikgonggan,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan,1 star,Seoul
+kyo ya,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya,1 star,New York
+bhoga,2019,Goteborg,Sweden,creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga,1 star,Goteborg
+meadowsweet,2019,New York,New York City,other european,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet,1 star,New York
+protégé,2019,South San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege,1 star,South San Francisco
+thörnströms kök,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok,1 star,Goteborg
+sk mat & människor,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor,1 star,Goteborg
+28+,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28,1 star,Goteborg
+koka,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka,1 star,Goteborg
+guo fu lou,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828,1 star,Hong Kong
+kajitsu,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu,1 star,New York
+mume,2019,Taipei,Taipei,other european,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume,1 star,Taipei
+madera,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera,1 star,San Francisco
+sav,2019,Malmo,Sweden,creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav,1 star,Malmo
+ming fu,2019,Taipei,Taipei,other asian,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu,1 star,Taipei
+the musket room,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room,1 star,New York
+pm & vänner,2019,Vaxjo,Sweden,creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner,1 star,Vaxjo
+quince,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince,3 stars,San Francisco
+man wah,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah,1 star,Hong Kong
+costes downtown,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown,1 star,Budapest
+singlethread,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread,3 stars,San Francisco
+borkonyha winekitchen,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen,1 star,Budapest
+zz's clam bar,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar,1 star,New York
+manresa,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa,3 stars,South San Francisco
+shinji by kanesaka,2019,Macau,Macau,japanese,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka,1 star,Macau
+alinea,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea,3 stars,Chicago
+rech,2019,Hong Kong,Hong Kong,seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech,1 star,Hong Kong
+mandarin grill + bar,2019,Hong Kong,Hong Kong,other european,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar,1 star,Hong Kong
+fagn,2019,Trondheim,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn,1 star,Trondheim
+atelier crenn,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn,3 stars,San Francisco
+benu,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu,3 stars,San Francisco
+re-naa,2019,Stavanger,Norway,creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa,1 star,Stavanger
+credo,2019,Trondheim,Norway,creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo,1 star,Trondheim
+tuju,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju,2 stars,Sao Paulo
+gaon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon,3 stars,Seoul
+the french laundry,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry,3 stars,San Francisco
+gaggan,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan,2 stars,Bangkok
+the restaurant at meadowood,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood,3 stars,San Francisco
+bâtard,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard,1 star,New York
+geranium,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium,3 stars,Kobenhavn
+kosaka,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka,1 star,New York
+wallsé,2019,New York,New York City,austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse,1 star,New York
+okuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda,1 star,New York
+le grill de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon,1 star,New York
+del posto,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto,1 star,New York
+statholdergaarden,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden,1 star,Oslo
+sabi omakase,2019,Stavanger,Norway,japanese,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase,1 star,Stavanger
+sushi nakazawa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa,1 star,New York
+l'appart,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart,1 star,New York
+daniel berlin,2019,Skane Tranas,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin,2 stars,Skane Tranas
+aska,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska,2 stars,New York
+nomad,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad,1 star,New York
+sushi shikon,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon,3 stars,Hong Kong
+daniel,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel,2 stars,New York
+gramercy tavern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern,1 star,New York
+ai fiori,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori,1 star,New York
+the river café,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe,1 star,New York
+bar uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu,1 star,New York
+contra,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra,1 star,New York
+atomix,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix,1 star,New York
+agern,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern,1 star,New York
+caviar russe,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe,1 star,New York
+tempura matsui,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui,1 star,New York
+sushi yasuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda,1 star,New York
+sushi amane,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane,1 star,New York
+blue hill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill,1 star,New York
+café boulud,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud,1 star,New York
+vollmers,2019,Malmo,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers,2 stars,Malmo
+shoun ryugin,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin,2 stars,Taipei
+sushi amamoto,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto,2 stars,Taipei
+sühring,2019,Bangkok,Thailand,other european,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring,2 stars,Bangkok
+carbone,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone,1 star,New York
+le normandie,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie,2 stars,Bangkok
+pineapple and pearls,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls,2 stars,"Washington, D.C."
+atelier amaro,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro,1 star,Warszawa
+peter luger,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger,1 star,New York
+sushi inoue,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue,1 star,New York
+sushi noz,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz,1 star,New York
+satsuki,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki,1 star,New York
+minibar,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar,2 stars,"Washington, D.C."
+babbo,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo,1 star,New York
+lee jong kuk 104,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104,1 star,Seoul
+amador,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador,3 stars,Wien
+pfefferschiff,2019,Hallwang,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff,1 star,Hallwang
+shoukouwa,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa,2 stars,Singapore
+olympe,2019,Rio De Janeiro,Rio de Janeiro,french,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe,1 star,Rio De Janeiro
+lasai,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai,1 star,Rio De Janeiro
+ta vie,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie,2 stars,Hong Kong
+sushi saito,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito,2 stars,Hong Kong
+tenku ryugin,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin,2 stars,Hong Kong
+spondi,2019,Athina,Greece,french,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi,2 stars,Athina
+steirereck im stadtpark,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark,2 stars,Wien
+silvio nickol gourmet restaurant,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant,2 stars,Wien
+konstantin filippou,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou,2 stars,Wien
+mraz & sohn,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn,2 stars,Wien
+ikarus,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus,2 stars,Salzburg
+senns.restaurant,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant,2 stars,Salzburg
+pru,2019,Phuket,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru,1 star,Phuket
+sorn,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn,1 star,Bangkok
+costes,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes,1 star,Budapest
+bo.lan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan,1 star,Bangkok
+mosu,2019,Seoul,South Korea,international cuisine,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu,1 star,Seoul
+huto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto,1 star,Sao Paulo
+kan suke,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke,1 star,Sao Paulo
+kinoshita,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita,1 star,Sao Paulo
+tangará jean-georges,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges,1 star,Sao Paulo
+ryo gastronomia,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia,1 star,Sao Paulo
+picchi,2019,Sao Paulo,Sao Paulo,italian,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi,1 star,Sao Paulo
+evvai,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai,1 star,Sao Paulo
+maní,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani,1 star,Sao Paulo
+jun sakamoto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto,1 star,Sao Paulo
+cipriani,2019,Rio De Janeiro,Rio de Janeiro,italian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani,1 star,Rio De Janeiro
+mee,2019,Rio De Janeiro,Rio de Janeiro,other asian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee,1 star,Rio De Janeiro
+oteque,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque,1 star,Rio De Janeiro
+kadeau bornholm,2019,Pedersker,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm,1 star,Pedersker
+upstairs at mikkeller,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller,1 star,Bangkok
+oro,2019,Rio De Janeiro,Rio de Janeiro,creative,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro,2 stars,Rio De Janeiro
+noel,2019,Zagreb,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel,1 star,Zagreb
+carpe diem,2019,Salzburg,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem,1 star,Salzburg
+d.o.m.,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m,2 stars,Sao Paulo
+kojima,2019,Seoul,South Korea,japanese,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima,2 stars,Seoul
+waku ghin,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin,2 stars,Singapore
+mezzaluna,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna,2 stars,Bangkok
+bo innovation,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation,3 stars,Hong Kong
+kilian stuba,2019,Kleinwalsertal,Austria,creative,$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba,1 star,Kleinwalsertal
+onyx,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx,2 stars,Budapest
+the ocean,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean,1 star,Hong Kong
+tate,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate,1 star,Hong Kong
+kaiseki den by saotome,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome,1 star,Hong Kong
+vea,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea,1 star,Hong Kong
+takumi by daisuke mori,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori,1 star,Hong Kong
+sushi tokami,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami,1 star,Hong Kong
+hytra,2019,Athina,Greece,international cuisine,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra,1 star,Athina
+esszimmer,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer,1 star,Salzburg
+varoulko seaside,2019,Athina,Greece,seafood,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside,1 star,Athina
+la degustation bohême bourgeoise,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise,1 star,Praha
+field,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field,1 star,Praha
+360º,2019,Dubrovnik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA,1 star,Dubrovnik
+babel,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel,1 star,Budapest
+pelegrini,2019,Sibenik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini,1 star,Sibenik
+draga di lovrana,2019,Lovran,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana,1 star,Lovran
+monte,2019,Rovinj,Croatia,creative,$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte,1 star,Rovinj
+le ciel by toni mörwald,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald,1 star,Wien
+aend,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend,1 star,Wien
+tian,2019,Wien,Austria,vegetarian,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian,1 star,Wien
+shiki,2019,Wien,Austria,japanese,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki,1 star,Wien
+walter bauer,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer,1 star,Wien
+pramerl & the wolf,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf,1 star,Wien
+das loft,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft,1 star,Wien
+botrini's,2019,Athina,Greece,other european,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s,1 star,Athina
+gotham bar and grill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill,1 star,New York
+a‚o‚c,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac,2 stars,Kobenhavn
+junoon,2019,New York,New York City,indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon,1 star,New York
+r-haan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan,1 star,Bangkok
+canvas,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292,1 star,Bangkok
+fäviken magasinet,2019,Jarpen,Sweden,creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet,2 stars,Jarpen
+elements,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements,1 star,Bangkok
+ginza sushi ichi,2019,Bangkok,Thailand,japanese,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi,1 star,Bangkok
+blanca,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca,2 stars,New York
+komi,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi,1 star,"Washington, D.C."
+sushi taro,2019,"Washington, D.C.",Washington DC,japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro,1 star,"Washington, D.C."
+plume,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume,1 star,"Washington, D.C."
+shinji (tanglin road),2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road,1 star,Singapore
+coi,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi,2 stars,San Francisco
+jean-georges,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges,2 stars,New York
+atera,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera,2 stars,New York
+jungsik,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik,2 stars,New York
+gastrologik,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik,2 stars,Stockholm
+campton place,2019,San Francisco,California,indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place,2 stars,San Francisco
+saison,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison,2 stars,San Francisco
+lazy bear,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear,2 stars,San Francisco
+californios,2019,San Francisco,California,mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios,2 stars,San Francisco
+commis,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis,2 stars,San Francisco
+baumé,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume,2 stars,South San Francisco
+odette,2018,Singapore,Singapore,french,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette,2 stars,Singapore
+fiola,2019,"Washington, D.C.",Washington DC,italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola,1 star,"Washington, D.C."
+upper house,2019,Goteborg,Sweden,creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house,1 star,Goteborg
+métier,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier,1 star,"Washington, D.C."
+operakällaren,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren,1 star,Stockholm
+urasawa,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa,2 stars,Los Angeles
+the inn at little washington,2019,"Washington, D.C.",Washington DC,american,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington,3 stars,"Washington, D.C."
+noda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda,1 star,New York
+oaxen krog,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog,2 stars,Stockholm
+l'atelier de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505,2 stars,New York
+dialogue,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue,1 star,Los Angeles
+gaa,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa,1 star,Bangkok
+aloë,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe,1 star,Stockholm
+n/naka,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka,2 stars,Los Angeles
+aquavit,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit,2 stars,New York
+sushi kimura,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura,1 star,Singapore
+smyth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth,2 stars,Chicago
+providence,2019,Los Angeles,California,seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence,2 stars,Los Angeles
+vespertine,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine,2 stars,Los Angeles
+oriole,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole,2 stars,Chicago
+gabriel kreuther,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther,2 stars,New York
+pierre,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre,2 stars,Hong Kong
+koks,2019,Leynar,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780,2 stars,Leynar
+alain ducasse at morpheus,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus,2 stars,Macau
+noma,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma,2 stars,Kobenhavn
+the modern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern,2 stars,New York
+kadeau copenhagen,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen,2 stars,Kobenhavn
+kashiwaya,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya,2 stars,Hong Kong
+ko,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko,2 stars,New York
+somni,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni,2 stars,Los Angeles
+ichimura at uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu,2 stars,New York
+marea,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea,2 stars,New York
+acadia,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia,2 stars,Chicago
+sushi ginza onodera,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850,2 stars,Los Angeles
+agrikultur,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur,1 star,Stockholm
+acquerello,2019,San Francisco,California,italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello,2 stars,San Francisco
+le du,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du,1 star,Bangkok
+saawaan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan,1 star,Bangkok
+kitcho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho,1 star,Taipei
+logy,2019,Taipei,Taipei,other asian,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy,1 star,Taipei
+ken an ho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho,1 star,Taipei
+sushi ryu,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu,1 star,Taipei
+amber,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032,2 stars,Hong Kong
+sushi ginza onodera,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera,2 stars,New York
+aldea,2019,New York,New York City,other european,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea,1 star,New York
+cut,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418,1 star,Los Angeles
+shunji,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji,1 star,Los Angeles
+eleven madison park,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park,3 stars,New York
+le bernardin,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin,3 stars,New York
+per se,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se,3 stars,New York
+masa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa,3 stars,New York
+maaemo,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo,3 stars,Oslo
+chez tj,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj,1 star,South San Francisco
+plumed horse,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse,1 star,South San Francisco
+wakuriya,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya,1 star,San Francisco
+sushi yoshizumi,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi,1 star,San Francisco
+maum,2019,South San Francisco,California,korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum,1 star,South San Francisco
+omakase,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase,1 star,San Francisco
+birdsong,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong,1 star,San Francisco
+in situ,2019,San Francisco,California,international cuisine,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ,1 star,San Francisco
+auberge du soleil,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil,1 star,San Francisco
+chef's table at brooklyn fare,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare,3 stars,New York
+la toque,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque,1 star,San Francisco
+aubergine,2019,Monterey,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine,1 star,Monterey
+madcap,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap,1 star,San Francisco
+wako,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako,1 star,San Francisco
+clou,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou,1 star,Kobenhavn
+gary danko,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko,1 star,San Francisco
+keiko à nob hill,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill,1 star,San Francisco
+jū-ni,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni,1 star,San Francisco
+sons & daughters,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters,1 star,San Francisco
+michael mina,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina,1 star,San Francisco
+angler,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler,1 star,San Francisco
+hashiri,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri,1 star,San Francisco
+kinjo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo,1 star,San Francisco
+kong hans kælder,2019,Kobenhavn,Denmark,french,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder,1 star,Kobenhavn
+frederikshøj,2019,Aarhus,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj,1 star,Aarhus
+kenzo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo,1 star,San Francisco
+nozawa bar,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar,1 star,Los Angeles
+edvard,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard,1 star,Wien
+frantzén,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen,3 stars,Stockholm
+maude,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude,1 star,Los Angeles
+mori sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi,1 star,Los Angeles
+trois mec,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec,1 star,Los Angeles
+le comptoir,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir,1 star,Los Angeles
+q sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi,1 star,Los Angeles
+shibumi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644,1 star,Los Angeles
+hayato,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato,1 star,Los Angeles
+orsa & winston,2019,Los Angeles,California,other asian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston,1 star,Los Angeles
+hana re,2019,Costa Mesa,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re,1 star,Costa Mesa
+addison,2019,San Diego,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison,1 star,San Diego
+harbor house,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house,1 star,San Francisco
+goosefoot,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot,1 star,Chicago
+el ideas,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas,1 star,Chicago
+schwa,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa,1 star,Chicago
+la yeon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon,3 stars,Seoul
+temporis,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis,1 star,Chicago
+everest,2019,Chicago,Chicago,french,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest,1 star,Chicago
+topolobampo,2019,Chicago,Chicago,mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo,1 star,Chicago
+spiaggia,2019,Chicago,Chicago,italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia,1 star,Chicago
+robuchon au dôme,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome,3 stars,Macau
+l'atelier de joël robuchon,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon,3 stars,Hong Kong
+8½ otto e mezzo - bombana,2019,Hong Kong,Hong Kong,italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana,3 stars,Hong Kong
+caprice,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice,3 stars,Hong Kong
+lung king heen,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen,3 stars,Hong Kong
+gotgan,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan,1 star,Seoul
+slotskøkkenet,2019,Horve,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet,1 star,Horve
+elizabeth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth,1 star,Chicago
+madrona manor,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor,1 star,San Francisco
+the kitchen,2019,Sacramento,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371,1 star,Sacramento
+farmhouse inn & restaurant,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant,1 star,San Francisco
+elske,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske,1 star,Chicago
+senses,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses,1 star,Warszawa
+aniar,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar,1 star,Gaillimh Galway
+loam,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam,1 star,Gaillimh Galway
+wild honey inn,2019,Lios Duin Bhearna Lisdoonvarna,Ireland,international cuisine,,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn,1 star,Lios Duin Bhearna Lisdoonvarna
+chestnut,2019,Ballydehob,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut,1 star,Ballydehob
+mews,2019,Baltimore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews,1 star,Baltimore
+ichigo ichie,2019,Corcaigh Cork,Ireland,japanese,,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie,1 star,Corcaigh Cork
+chapter one,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one,1 star,City Centre
+greenhouse,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse,1 star,City Centre
+l'ecrivain,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain,1 star,City Centre
+campagne,2019,Cill Chainnigh Kilkenny,Ireland,british,,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne,1 star,Cill Chainnigh Kilkenny
+heron & grey,2019,Blackrock,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey,1 star,Blackrock
+lady helen,2019,Baile Mhic Andain Thomastown,Ireland,international cuisine,,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen,1 star,Baile Mhic Andain Thomastown
+house,2019,Aird Mhor Ardmore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house,1 star,Aird Mhor Ardmore
+loch bay,2019,Waternish,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay,1 star,Waternish
+braidwoods,2019,Dalry,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods,1 star,Dalry
+eipic,2019,Belfast,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic,1 star,Belfast
+ox,2019,Belfast,United Kingdom,british,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109,1 star,Belfast
+the peat inn,2019,Peat Inn,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn,1 star,Peat Inn
+kitchin,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin,1 star,Leith
+martin wishart,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart,1 star,Leith
+number one,2019,Edinburgh,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one,1 star,Edinburgh
+21212,2019,Edinburgh,United Kingdom,creative,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212,1 star,Edinburgh
+the cellar,2019,Anstruther,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar,1 star,Anstruther
+forest side,2019,Grasmere,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side,1 star,Grasmere
+hrishi,2019,Bowness On Windermere,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi,1 star,Bowness On Windermere
+rogan & co,2019,Cartmel,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co,1 star,Cartmel
+house of tides,2019,Newcastle Upon Tyne,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides,1 star,Newcastle Upon Tyne
+sosban & the old butchers,2019,Menai Bridge Porthaethwy,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers,1 star,Menai Bridge Porthaethwy
+northcote,2019,Langho,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote,1 star,Langho
+yorke arms,2019,Pateley Bridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms,1 star,Pateley Bridge
+fraiche,2019,Birkenhead,United Kingdom,creative,,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche,1 star,Birkenhead
+white swan,2019,Fence,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan,1 star,Fence
+tyddyn llan,2019,Llandrillo,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan,1 star,Llandrillo
+ynyshir,2019,Machynlleth,United Kingdom,creative,,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir,1 star,Machynlleth
+black swan,2019,Oldstead,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan,1 star,Oldstead
+simon radley at chester grosvenor,2019,Chester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor,1 star,Chester
+star inn at harome,2019,Harome,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome,1 star,Harome
+the man behind the curtain,2019,Leeds,United Kingdom,creative,,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain,1 star,Leeds
+the checkers,2019,Montgomery Trefaldwyn,United Kingdom,french,,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers,1 star,Montgomery Trefaldwyn
+pipe and glass,2019,South Dalton,United Kingdom,british,,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass,1 star,South Dalton
+fischer's at baslow hall,2019,Baslow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall,1 star,Baslow
+winteringham fields,2019,Winteringham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields,1 star,Winteringham
+thomas carr @ the olive room,2019,Ilfracombe,United Kingdom,seafood,,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room,1 star,Ilfracombe
+walnut tree,2019,Llanddewi Skirrid,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree,1 star,Llanddewi Skirrid
+paul ainsworth at no.6,2019,Padstow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6,1 star,Padstow
+simpsons,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons,1 star,Birmingham
+purnell's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s,1 star,Birmingham
+adam's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s,1 star,Birmingham
+outlaw's fish kitchen,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen,1 star,Port Isaac
+carters of moseley,2019,Birmingham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley,1 star,Birmingham
+peel's,2019,Hampton In Arden,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s,1 star,Hampton In Arden
+john's house,2019,Mountsorrel,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house,1 star,Mountsorrel
+the whitebrook,2019,Whitebrook,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook,1 star,Whitebrook
+james sommerin,2019,Penarth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin,1 star,Penarth
+driftwood,2019,Portscatho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood,1 star,Portscatho
+the cross at kenilworth,2019,Kenilworth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth,1 star,Kenilworth
+masons arms,2019,Knowstone,United Kingdom,french,,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms,1 star,Knowstone
+salt,2019,Stratford Upon Avon,United Kingdom,british,,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869,1 star,Stratford Upon Avon
+le champignon sauvage,2019,Cheltenham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage,1 star,Cheltenham
+gidleigh park,2019,Chagford,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park,1 star,Chagford
+hambleton hall,2019,Upper Hambleton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall,1 star,Upper Hambleton
+wilks,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks,1 star,Bristol
+bulrush,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush,1 star,Bristol
+casamia,2019,Bristol,United Kingdom,creative,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia,1 star,Bristol
+paco tapas,2019,Bristol,United Kingdom,spanish,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas,1 star,Bristol
+pony & trap,2019,Chew Magna,United Kingdom,british,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap,1 star,Chew Magna
+the dining room,2019,Malmesbury,United Kingdom,other asian,,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454,1 star,Malmesbury
+bybrook,2019,Castle Combe,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook,1 star,Castle Combe
+restaurant hywel jones by lucknam park,2019,Colerne,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park,1 star,Colerne
+olive tree,2019,Bath,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree,1 star,Bath
+lympstone manor,2019,Lympstone,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor,1 star,Lympstone
+the neptune,2019,Hunstanton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune,1 star,Hunstanton
+elephant,2019,Torquay,United Kingdom,british,,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant,1 star,Torquay
+oxford kitchen,2019,Oxford,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen,1 star,Oxford
+nut tree,2019,Murcott,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree,1 star,Murcott
+morston hall,2019,Morston,United Kingdom,british,,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall,1 star,Morston
+red lion freehouse,2019,East Chisenbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse,1 star,East Chisenbury
+blackbird,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018,1 star,Newbury
+woodspeen,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen,1 star,Newbury
+the coach,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach,1 star,Marlow
+crown,2019,Burchett'S Green,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549,1 star,Burchett'S Green
+l'ortolan,2019,Shinfield,United Kingdom,french,,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan,1 star,Shinfield
+hinds head,2019,Bray,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head,1 star,Bray
+black rat,2019,Winchester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat,1 star,Winchester
+matt worswick at the latymer,2019,Bagshot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer,1 star,Bagshot
+coworth park,2019,Ascot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park,1 star,Ascot
+tudor room,2019,Egham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room,1 star,Egham
+the glasshouse,2019,Kew,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse,1 star,Kew
+la trompette,2019,Chiswick,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette,1 star,Chiswick
+tim allen's flitch of bacon,2019,Little Dunmow,United Kingdom,british,,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon,1 star,Little Dunmow
+river café,2019,Hammersmith,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe,1 star,Hammersmith
+kitchen w8,2019,Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8,1 star,Kensington
+trishna,2019,Marylebone,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna,1 star,Marylebone
+roganic,2019,Marylebone,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic,1 star,Marylebone
+locanda locatelli,2019,Marylebone,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli,1 star,Marylebone
+texture,2019,Marylebone,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture,1 star,Marylebone
+portland,2019,Regent'S Park,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland,1 star,Regent'S Park
+harwood arms,2019,Fulham,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms,1 star,Fulham
+pied à terre,2019,Bloomsbury,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre,1 star,Bloomsbury
+the ninth,2019,Bloomsbury,United Kingdom,other european,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth,1 star,Bloomsbury
+kai,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638,1 star,Mayfair
+pollen street social,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social,1 star,Mayfair
+hakkasan mayfair,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair,1 star,Mayfair
+the square,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391,1 star,Mayfair
+alyn williams at the westbury,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury,1 star,Mayfair
+benares,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares,1 star,Mayfair
+hakkasan hanway place,2019,Bloomsbury,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place,1 star,Bloomsbury
+social eating house,2019,Soho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house,1 star,Soho
+galvin at windows,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows,1 star,Mayfair
+murano,2019,Mayfair,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano,1 star,Mayfair
+marcus,2019,Belgravia,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus,1 star,Belgravia
+sabor,2019,Mayfair,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor,1 star,Mayfair
+clock house,2019,Ripley,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house,1 star,Ripley
+yauatcha soho,2019,Soho,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho,1 star,Soho
+céleste,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste,1 star,Belgravia
+gymkhana,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana,1 star,Mayfair
+amaya,2019,Belgravia,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya,1 star,Belgravia
+pétrus,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688,1 star,Belgravia
+veeraswamy,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy,1 star,Mayfair
+barrafina,2019,Soho,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina,1 star,Soho
+hide,2019,Mayfair,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide,1 star,Mayfair
+ritz restaurant,2019,Westminster,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant,1 star,Westminster
+elystan street,2019,Chelsea,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street,1 star,Chelsea
+seven park place,2019,Saint James'S,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place,1 star,Saint James'S
+ikoyi,2019,Saint James'S,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi,1 star,Saint James'S
+aquavit,2019,Saint James'S,United Kingdom,scandinavian,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082,1 star,Saint James'S
+five fields,2019,Chelsea,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields,1 star,Chelsea
+dining room at the goring,2019,Victoria,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring,1 star,Victoria
+st john,2019,Clerkenwell,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john,1 star,Clerkenwell
+quilon,2019,Victoria,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon,1 star,Victoria
+club gascon,2019,London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon,1 star,London
+a. wong,2019,Victoria,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong,1 star,Victoria
+the clove club,2019,Shoreditch,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club,1 star,Shoreditch
+leroy,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy,1 star,Shoreditch
+angler,2019,Finsbury,United Kingdom,seafood,,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235,1 star,Finsbury
+brat,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat,1 star,Shoreditch
+lyle's,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s,1 star,Shoreditch
+galvin la chapelle,2019,Spitalfields,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle,1 star,Spitalfields
+city social,2019,City Of London,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social,1 star,City Of London
+la dame de pic,2019,City Of London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic,1 star,City Of London
+story,2019,Bermondsey,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story,1 star,Bermondsey
+trinity,2019,Clapham Common,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity,1 star,Clapham Common
+chez bruce,2019,Wandsworth,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce,1 star,Wandsworth
+sorrel,2019,Dorking,United Kingdom,british,,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459,1 star,Dorking
+restaurant tristan,2019,Horsham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan,1 star,Horsham
+gravetye manor,2019,Gravetye,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor,1 star,Gravetye
+the sportsman,2019,Seasalter,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman,1 star,Seasalter
+west house,2019,Biddenden,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house,1 star,Biddenden
+fordwich arms,2019,Fordwich,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms,1 star,Fordwich
+samphire,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987,1 star,Saint Helier Saint Helier
+bohemia,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia,1 star,Saint Helier Saint Helier
+patrick guilbaud,2019,City Centre,Ireland,french,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud,2 stars,City Centre
+andrew fairlie at gleneagles,2019,Auchterarder,United Kingdom,french,,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles,2 stars,Auchterarder
+l'enclume,2019,Cartmel,United Kingdom,creative,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume,2 stars,Cartmel
+raby hunt,2019,Summerhouse,United Kingdom,british,,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt,2 stars,Summerhouse
+moor hall,2019,Aughton,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall,2 stars,Aughton
+restaurant sat bains,2019,Nottingham,United Kingdom,creative,,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains,2 stars,Nottingham
+restaurant nathan outlaw,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw,2 stars,Port Isaac
+belmond le manoir aux quat' saisons,2019,Great Milton,United Kingdom,french,,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons,2 stars,Great Milton
+midsummer house,2019,Cambridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house,2 stars,Cambridge
+hand and flowers,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers,2 stars,Marlow
+ledbury,2019,North Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury,2 stars,North Kensington
+core by clare smyth,2019,North Kensington,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth,2 stars,North Kensington
+le gavroche,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche,2 stars,Mayfair
+kitchen table at bubbledogs,2019,Bloomsbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs,2 stars,Bloomsbury
+hélène darroze at the connaught,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught,2 stars,Mayfair
+dinner by heston blumenthal,2019,Hyde Park,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal,2 stars,Hyde Park
+umu,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu,2 stars,Mayfair
+sketch (the lecture room & library),2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library,2 stars,Mayfair
+greenhouse,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393,2 stars,Mayfair
+claude bosi at bibendum,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum,2 stars,Chelsea
+fat duck,2019,Bray,United Kingdom,creative,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck,3 stars,Bray
+waterside inn,2019,Bray,United Kingdom,french,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn,3 stars,Bray
+alain ducasse at the dorchester,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester,3 stars,Mayfair
+the araki,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki,3 stars,Mayfair
+gordon ramsay,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay,3 stars,Chelsea
+>>>>>>> Stashed changes
diff --git a/data/clean/CleanStars__.csv b/data/clean/CleanStars__.csv
new file mode 100644
index 00000000..a17e76e2
--- /dev/null
+++ b/data/clean/CleanStars__.csv
@@ -0,0 +1,696 @@
+name,year,city,region,cuisine,price,url,stars,major_city
+ho hung kee,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee,1 star,Hong Kong
+feng wei ju,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju,2 stars,Macau
+imperial treasure fine teochew cuisine (orchard),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard,1 star,Singapore
+shisen hanten,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten,2 stars,Singapore
+ma cuisine,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine,1 star,Singapore
+alma,2018,Singapore,Singapore,other european,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma,1 star,Singapore
+lei garden,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509,1 star,Singapore
+the song of india,2018,Singapore,Singapore,indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india,1 star,Singapore
+putien (kitchener road),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road,1 star,Singapore
+hill street tai hwa pork noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle,1 star,Singapore
+jin jin,2019,Seoul,South Korea,chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin,1 star,Seoul
+summer palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658,1 star,Singapore
+cheek by jowl,2018,Singapore,Singapore,australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl,1 star,Singapore
+nouri,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri,1 star,Singapore
+liao fan hong kong soya sauce chicken rice & noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle,1 star,Singapore
+garibaldi,2018,Singapore,Singapore,italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi,1 star,Singapore
+candlenut,2018,Singapore,Singapore,other asian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut,1 star,Singapore
+rhubarb,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb,1 star,Singapore
+tim ho wan (sham shui po),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po,1 star,Hong Kong
+lei garden (mong kok),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok,1 star,Hong Kong
+yè shanghai (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui,1 star,Hong Kong
+crystal jade golden palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace,1 star,Singapore
+pang's kitchen,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen,1 star,Hong Kong
+qi (wan chai),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai,1 star,Hong Kong
+kam's roast goose,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose,1 star,Hong Kong
+yat lok,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok,1 star,Hong Kong
+king,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226,1 star,Macau
+the golden peacock,2019,Macau,Macau,indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock,1 star,Macau
+suan thip,2019,Bangkok,Thailand,other asian,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip,1 star,Bangkok
+da san yuan,2019,Taipei,Taipei,chinese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan,1 star,Taipei
+lei garden (kwun tong),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong,1 star,Hong Kong
+tainan tan tsu mien seafood,2019,Taipei,Taipei,seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood,1 star,Taipei
+parachute,2019,Chicago,Chicago,other asian,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute,1 star,Chicago
+beefbar,2019,Hong Kong,Hong Kong,american,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar,1 star,Hong Kong
+claro,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro,1 star,New York
+yee tung heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen,1 star,Hong Kong
+the guest house,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house,2 stars,Taipei
+lai heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen,1 star,Macau
+ying,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying,1 star,Macau
+fu ho (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui,1 star,Hong Kong
+im teppanyaki & wine,2019,Hong Kong,Hong Kong,japanese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine,1 star,Hong Kong
+ah yat harbour view (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui,1 star,Hong Kong
+methavalai sorndaeng,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng,1 star,Bangkok
+imperial treasure fine chinese cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine,1 star,Hong Kong
+zi yat heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen,1 star,Macau
+corner house,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house,1 star,Singapore
+yat tung heen (jordan),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan,1 star,Hong Kong
+jade dragon,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon,3 stars,Macau
+balwoo gongyang,2019,Seoul,South Korea,korean,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang,1 star,Seoul
+le palais,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais,3 stars,Taipei
+bistro na's,2019,Los Angeles,California,chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s,1 star,Los Angeles
+kin khao,2019,San Francisco,California,other asian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao,1 star,San Francisco
+commonwealth,2019,San Francisco,California,contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth,1 star,San Francisco
+béni,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni,1 star,Singapore
+forum,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum,2 stars,Hong Kong
+labyrinth,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth,1 star,Singapore
+braci,2018,Singapore,Singapore,italian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci,1 star,Singapore
+sun tung lok,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok,2 stars,Hong Kong
+casa enríque,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique,1 star,New York
+meta,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta,1 star,Singapore
+bacchanalia,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia,1 star,Singapore
+shinji (bras basah road),2018,Singapore,Singapore,japanese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road,1 star,Singapore
+summer pavilion,2018,Singapore,Singapore,chinese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion,1 star,Singapore
+state bird provisions,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions,1 star,San Francisco
+faro,2019,New York,New York City,american,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro,1 star,New York
+saint pierre,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre,1 star,Singapore
+whitegrass,2018,Singapore,Singapore,australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass,1 star,Singapore
+rose's luxury,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury,1 star,"Washington, D.C."
+burnt ends,2018,Singapore,Singapore,american,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends,1 star,Singapore
+zhejiang heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen,1 star,Hong Kong
+uncle boons,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons,1 star,New York
+bloom in the park,2019,Malmo,Sweden,creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park,1 star,Malmo
+golden formosa,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa,1 star,Taipei
+ruean panya,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya,1 star,Bangkok
+søllerød kro,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro,1 star,Kobenhavn
+relæ,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae,1 star,Kobenhavn
+kiin kiin,2019,Kobenhavn,Denmark,other asian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin,1 star,Kobenhavn
+formel b,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b,1 star,Kobenhavn
+danny's steakhouse,2019,Taipei,Taipei,american,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse,1 star,Taipei
+marchal,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal,1 star,Kobenhavn
+tien hsiang lo,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo,1 star,Taipei
+era ora,2019,Kobenhavn,Denmark,italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora,1 star,Kobenhavn
+ya ge,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge,1 star,Taipei
+ying jee club,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club,2 stars,Hong Kong
+jardin de jade (wan chai),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai,1 star,Hong Kong
+tail up goat,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat,1 star,"Washington, D.C."
+mathias dahlgren-matbaren,2019,Stockholm,Sweden,international cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren,1 star,Stockholm
+jeju noodle bar,2019,New York,New York City,korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar,1 star,New York
+saneh jaan,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan,1 star,Bangkok
+mizumi (macau),2019,Macau,Macau,japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau,2 stars,Macau
+al's place,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place,1 star,San Francisco
+wing lei,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei,1 star,Macau
+golden flower,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower,2 stars,Macau
+tim's kitchen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen,1 star,Macau
+celebrity cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine,1 star,Hong Kong
+mountain and sea house,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house,1 star,Taipei
+rasa,2019,San Francisco,California,indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa,1 star,San Francisco
+jay fai,2019,Bangkok,Thailand,international cuisine,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai,1 star,Bangkok
+longtail,2019,Taipei,Taipei,international cuisine,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail,1 star,Taipei
+tuome,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome,1 star,New York
+dusek's (board & beer),2019,Chicago,Chicago,gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer,1 star,Chicago
+café china,2019,New York,New York City,chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china,1 star,New York
+the dabney,2019,"Washington, D.C.",Washington DC,american,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney,1 star,"Washington, D.C."
+bresca,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca,1 star,"Washington, D.C."
+loaf on,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on,1 star,Hong Kong
+new punjab club,2019,Hong Kong,Hong Kong,indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club,1 star,Hong Kong
+kwonsooksoo,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo,2 stars,Seoul
+shin sushi,2019,Los Angeles,California,japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi,1 star,Los Angeles
+osteria mozza,2019,Los Angeles,California,italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza,1 star,Los Angeles
+kali,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali,1 star,Los Angeles
+bouchon,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon,1 star,San Francisco
+taco maría,2019,Costa Mesa,California,mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria,1 star,Costa Mesa
+bar crenn,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn,1 star,San Francisco
+alla prima,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima,2 stars,Seoul
+henne kirkeby kro,2019,Henne,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro,2 stars,Henne
+band of bohemia,2019,Chicago,Chicago,gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia,1 star,Chicago
+masseria,2019,"Washington, D.C.",Washington DC,italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria,1 star,"Washington, D.C."
+entente,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente,1 star,Chicago
+kinship,2019,"Washington, D.C.",Washington DC,contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship,1 star,"Washington, D.C."
+rustic canyon,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon,1 star,Los Angeles
+mingles,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles,2 stars,Seoul
+duddell's,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s,1 star,Hong Kong
+mourad,2019,San Francisco,California,moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad,1 star,San Francisco
+boka,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka,1 star,Chicago
+roister,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister,1 star,Chicago
+jungsik,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965,2 stars,Seoul
+sorrel,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel,1 star,San Francisco
+lord stanley,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley,1 star,San Francisco
+north pond,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond,1 star,Chicago
+blue duck tavern,2019,"Washington, D.C.",Washington DC,american,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern,1 star,"Washington, D.C."
+kato,2019,Los Angeles,California,other asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato,1 star,Los Angeles
+domestic,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic,1 star,Aarhus
+me‚mu,2019,Vejle,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu,1 star,Vejle
+jordnær,2019,Kobenhavn,Denmark,danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer,1 star,Kobenhavn
+kokkeriet,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet,1 star,Kobenhavn
+gastromé,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome,1 star,Aarhus
+sepia,2019,Chicago,Chicago,american,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia,1 star,Chicago
+blackbird,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird,1 star,Chicago
+substans,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans,1 star,Aarhus
+stud!o at the standard,2019,Kobenhavn,Denmark,creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard,1 star,Kobenhavn
+the progress,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress,1 star,San Francisco
+ti trin ned,2019,Fredericia,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned,1 star,Fredericia
+olo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo,1 star,Helsingfors   Helsinki
+rich table,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table,1 star,San Francisco
+octavia,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia,1 star,San Francisco
+demo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo,1 star,Helsingfors   Helsinki
+grön,2019,Helsingfors   Helsinki,Finland,finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron,1 star,Helsingfors   Helsinki
+écriture,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture,2 stars,Hong Kong
+palace,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace,1 star,Helsingfors   Helsinki
+mister jiu's,2019,San Francisco,California,chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius,1 star,San Francisco
+sushi wadatsumi,2019,Hong Kong,Hong Kong,japanese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi,1 star,Hong Kong
+j'aime by jean-michel lorain,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain,1 star,Bangkok
+frederiksminde,2019,Praesto,Denmark,creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde,1 star,Praesto
+t'ang court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court,3 stars,Hong Kong
+savelberg,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg,1 star,Bangkok
+the eight,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight,3 stars,Macau
+ask,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask,1 star,Helsingfors   Helsinki
+ora,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora,1 star,Helsingfors   Helsinki
+nahm,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm,1 star,Bangkok
+xin rong ji,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji,1 star,Hong Kong
+tin lung heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen,2 stars,Hong Kong
+les amis,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis,2 stars,Singapore
+aster,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster,1 star,San Francisco
+épure,2019,nan,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure,1 star,nan
+summer palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace,1 star,Hong Kong
+alouette,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette,1 star,Kobenhavn
+tosca,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519,1 star,Hong Kong
+octavium,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium,1 star,Hong Kong
+spring moon,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon,1 star,Hong Kong
+shang palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace,1 star,Hong Kong
+ming court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court,1 star,Hong Kong
+nico,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico,1 star,San Francisco
+arbor,2019,nan,Hong Kong,international cuisine,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor,1 star,nan
+luce,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce,1 star,San Francisco
+yan toh heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen,2 stars,Hong Kong
+108,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108,1 star,Kobenhavn
+spruce,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce,1 star,San Francisco
+spqr,2019,San Francisco,California,italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr,1 star,San Francisco
+arcane,2019,Hong Kong,Hong Kong,other european,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane,1 star,Hong Kong
+the kitchen,2019,Macau,Macau,american,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810,1 star,Macau
+chim by siam wisdom,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom,1 star,Bangkok
+stand,2019,Budapest,Hungary,international cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand,1 star,Budapest
+sra bua by kiin kiin,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin,1 star,Bangkok
+belon,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon,1 star,Hong Kong
+paste,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste,1 star,Bangkok
+cut,2018,Singapore,Singapore,american,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut,1 star,Singapore
+8 1/2 otto e mezzo - bombana,2019,Macau,Macau,italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana,1 star,Macau
+sushi nomura,2019,Taipei,Taipei,japanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura,1 star,Taipei
+impromptu by paul lee,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee,1 star,Taipei
+the finch,2019,New York,New York City,american,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch,1 star,New York
+oxomoco,2019,New York,New York City,mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco,1 star,New York
+jewel bako,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako,1 star,New York
+ekstedt,2019,Stockholm,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt,1 star,Stockholm
+kontrast,2019,Oslo,Norway,scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast,1 star,Oslo
+jaan,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan,1 star,Singapore
+chef kang's,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s,1 star,Singapore
+galt,2019,Oslo,Norway,international cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt,1 star,Oslo
+siren by rw,2019,"Washington, D.C.",Washington DC,seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw,1 star,"Washington, D.C."
+sushi ichi,2018,Singapore,Singapore,japanese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi,1 star,Singapore
+exquisine,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine,1 star,Seoul
+pearl dragon,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon,1 star,Macau
+joo ok,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok,1 star,Seoul
+the village pub,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub,1 star,San Francisco
+muoki,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki,1 star,Seoul
+yu yuan,2019,Seoul,South Korea,chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan,1 star,Seoul
+l'amitié,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie,1 star,Seoul
+dosa,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871,1 star,Seoul
+poom,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom,1 star,Seoul
+zero complex,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex,1 star,Seoul
+soigné,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne,1 star,Seoul
+table for four,2019,Seoul,South Korea,other european,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four,1 star,Seoul
+the tasting room,2019,Macau,Macau,french,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room,2 stars,Macau
+kanoyama,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama,1 star,New York
+volt,2019,Stockholm,Sweden,creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt,1 star,Stockholm
+bicena,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena,1 star,Seoul
+the clocktower,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower,1 star,New York
+sushi sho,2019,Stockholm,Sweden,japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho,1 star,Stockholm
+casa mono,2019,New York,New York City,spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono,1 star,New York
+nix,2019,New York,New York City,vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix,1 star,New York
+bouley at home,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home,1 star,New York
+kosushi,2019,Sao Paulo,Sao Paulo,japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi,1 star,Sao Paulo
+taïrroir,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir,2 stars,Taipei
+cote,2019,New York,New York City,korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote,1 star,New York
+jiang-nan chun,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun,1 star,Singapore
+raw,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw,2 stars,Taipei
+le coucou,2019,New York,New York City,french,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou,1 star,New York
+l'atelier de joël robuchon,2019,Taipei,Taipei,french,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759,1 star,Taipei
+iggy's,2018,Singapore,Singapore,other european,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s,1 star,Singapore
+stay,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay,1 star,Seoul
+hirohisa,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa,1 star,New York
+da-wan,2019,Taipei,Taipei,american,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan,1 star,Taipei
+dining in space,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space,1 star,Seoul
+hansikgonggan,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan,1 star,Seoul
+kyo ya,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya,1 star,New York
+bhoga,2019,Goteborg,Sweden,creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga,1 star,Goteborg
+meadowsweet,2019,New York,New York City,other european,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet,1 star,New York
+protégé,2019,South San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege,1 star,South San Francisco
+thörnströms kök,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok,1 star,Goteborg
+sk mat & människor,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor,1 star,Goteborg
+28+,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28,1 star,Goteborg
+koka,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka,1 star,Goteborg
+guo fu lou,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828,1 star,Hong Kong
+kajitsu,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu,1 star,New York
+mume,2019,Taipei,Taipei,other european,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume,1 star,Taipei
+madera,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera,1 star,San Francisco
+sav,2019,Malmo,Sweden,creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav,1 star,Malmo
+ming fu,2019,Taipei,Taipei,other asian,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu,1 star,Taipei
+the musket room,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room,1 star,New York
+pm & vänner,2019,Vaxjo,Sweden,creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner,1 star,Vaxjo
+quince,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince,3 stars,San Francisco
+man wah,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah,1 star,Hong Kong
+costes downtown,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown,1 star,Budapest
+singlethread,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread,3 stars,San Francisco
+borkonyha winekitchen,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen,1 star,Budapest
+zz's clam bar,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar,1 star,New York
+manresa,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa,3 stars,South San Francisco
+shinji by kanesaka,2019,Macau,Macau,japanese,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka,1 star,Macau
+alinea,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea,3 stars,Chicago
+rech,2019,Hong Kong,Hong Kong,seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech,1 star,Hong Kong
+mandarin grill + bar,2019,Hong Kong,Hong Kong,other european,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar,1 star,Hong Kong
+fagn,2019,Trondheim,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn,1 star,Trondheim
+atelier crenn,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn,3 stars,San Francisco
+benu,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu,3 stars,San Francisco
+re-naa,2019,Stavanger,Norway,creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa,1 star,Stavanger
+credo,2019,Trondheim,Norway,creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo,1 star,Trondheim
+tuju,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju,2 stars,Sao Paulo
+gaon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon,3 stars,Seoul
+the french laundry,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry,3 stars,San Francisco
+gaggan,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan,2 stars,Bangkok
+the restaurant at meadowood,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood,3 stars,San Francisco
+bâtard,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard,1 star,New York
+geranium,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium,3 stars,Kobenhavn
+kosaka,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka,1 star,New York
+wallsé,2019,New York,New York City,austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse,1 star,New York
+okuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda,1 star,New York
+le grill de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon,1 star,New York
+del posto,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto,1 star,New York
+statholdergaarden,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden,1 star,Oslo
+sabi omakase,2019,Stavanger,Norway,japanese,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase,1 star,Stavanger
+sushi nakazawa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa,1 star,New York
+l'appart,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart,1 star,New York
+daniel berlin,2019,Skane Tranas,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin,2 stars,Skane Tranas
+aska,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska,2 stars,New York
+nomad,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad,1 star,New York
+sushi shikon,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon,3 stars,Hong Kong
+daniel,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel,2 stars,New York
+gramercy tavern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern,1 star,New York
+ai fiori,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori,1 star,New York
+the river café,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe,1 star,New York
+bar uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu,1 star,New York
+contra,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra,1 star,New York
+atomix,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix,1 star,New York
+agern,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern,1 star,New York
+caviar russe,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe,1 star,New York
+tempura matsui,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui,1 star,New York
+sushi yasuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda,1 star,New York
+sushi amane,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane,1 star,New York
+blue hill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill,1 star,New York
+café boulud,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud,1 star,New York
+vollmers,2019,Malmo,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers,2 stars,Malmo
+shoun ryugin,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin,2 stars,Taipei
+sushi amamoto,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto,2 stars,Taipei
+sühring,2019,Bangkok,Thailand,other european,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring,2 stars,Bangkok
+carbone,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone,1 star,New York
+le normandie,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie,2 stars,Bangkok
+pineapple and pearls,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls,2 stars,"Washington, D.C."
+atelier amaro,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro,1 star,Warszawa
+peter luger,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger,1 star,New York
+sushi inoue,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue,1 star,New York
+sushi noz,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz,1 star,New York
+satsuki,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki,1 star,New York
+minibar,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar,2 stars,"Washington, D.C."
+babbo,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo,1 star,New York
+lee jong kuk 104,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104,1 star,Seoul
+amador,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador,3 stars,Wien
+pfefferschiff,2019,Hallwang,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff,1 star,Hallwang
+shoukouwa,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa,2 stars,Singapore
+olympe,2019,Rio De Janeiro,Rio de Janeiro,french,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe,1 star,Rio De Janeiro
+lasai,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai,1 star,Rio De Janeiro
+ta vie,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie,2 stars,Hong Kong
+sushi saito,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito,2 stars,Hong Kong
+tenku ryugin,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin,2 stars,Hong Kong
+spondi,2019,Athina,Greece,french,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi,2 stars,Athina
+steirereck im stadtpark,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark,2 stars,Wien
+silvio nickol gourmet restaurant,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant,2 stars,Wien
+konstantin filippou,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou,2 stars,Wien
+mraz & sohn,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn,2 stars,Wien
+ikarus,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus,2 stars,Salzburg
+senns.restaurant,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant,2 stars,Salzburg
+pru,2019,Phuket,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru,1 star,Phuket
+sorn,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn,1 star,Bangkok
+costes,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes,1 star,Budapest
+bo.lan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan,1 star,Bangkok
+mosu,2019,Seoul,South Korea,international cuisine,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu,1 star,Seoul
+huto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto,1 star,Sao Paulo
+kan suke,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke,1 star,Sao Paulo
+kinoshita,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita,1 star,Sao Paulo
+tangará jean-georges,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges,1 star,Sao Paulo
+ryo gastronomia,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia,1 star,Sao Paulo
+picchi,2019,Sao Paulo,Sao Paulo,italian,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi,1 star,Sao Paulo
+evvai,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai,1 star,Sao Paulo
+maní,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani,1 star,Sao Paulo
+jun sakamoto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto,1 star,Sao Paulo
+cipriani,2019,Rio De Janeiro,Rio de Janeiro,italian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani,1 star,Rio De Janeiro
+mee,2019,Rio De Janeiro,Rio de Janeiro,other asian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee,1 star,Rio De Janeiro
+oteque,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque,1 star,Rio De Janeiro
+kadeau bornholm,2019,Pedersker,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm,1 star,Pedersker
+upstairs at mikkeller,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller,1 star,Bangkok
+oro,2019,Rio De Janeiro,Rio de Janeiro,creative,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro,2 stars,Rio De Janeiro
+noel,2019,Zagreb,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel,1 star,Zagreb
+carpe diem,2019,Salzburg,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem,1 star,Salzburg
+d.o.m.,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m,2 stars,Sao Paulo
+kojima,2019,Seoul,South Korea,japanese,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima,2 stars,Seoul
+waku ghin,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin,2 stars,Singapore
+mezzaluna,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna,2 stars,Bangkok
+bo innovation,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation,3 stars,Hong Kong
+kilian stuba,2019,Kleinwalsertal,Austria,creative,$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba,1 star,Kleinwalsertal
+onyx,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx,2 stars,Budapest
+the ocean,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean,1 star,Hong Kong
+tate,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate,1 star,Hong Kong
+kaiseki den by saotome,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome,1 star,Hong Kong
+vea,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea,1 star,Hong Kong
+takumi by daisuke mori,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori,1 star,Hong Kong
+sushi tokami,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami,1 star,Hong Kong
+hytra,2019,Athina,Greece,international cuisine,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra,1 star,Athina
+esszimmer,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer,1 star,Salzburg
+varoulko seaside,2019,Athina,Greece,seafood,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside,1 star,Athina
+la degustation bohême bourgeoise,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise,1 star,Praha
+field,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field,1 star,Praha
+360º,2019,Dubrovnik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA,1 star,Dubrovnik
+babel,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel,1 star,Budapest
+pelegrini,2019,Sibenik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini,1 star,Sibenik
+draga di lovrana,2019,Lovran,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana,1 star,Lovran
+monte,2019,Rovinj,Croatia,creative,$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte,1 star,Rovinj
+le ciel by toni mörwald,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald,1 star,Wien
+aend,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend,1 star,Wien
+tian,2019,Wien,Austria,vegetarian,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian,1 star,Wien
+shiki,2019,Wien,Austria,japanese,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki,1 star,Wien
+walter bauer,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer,1 star,Wien
+pramerl & the wolf,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf,1 star,Wien
+das loft,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft,1 star,Wien
+botrini's,2019,Athina,Greece,other european,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s,1 star,Athina
+gotham bar and grill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill,1 star,New York
+a‚o‚c,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac,2 stars,Kobenhavn
+junoon,2019,New York,New York City,indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon,1 star,New York
+r-haan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan,1 star,Bangkok
+canvas,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292,1 star,Bangkok
+fäviken magasinet,2019,Jarpen,Sweden,creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet,2 stars,Jarpen
+elements,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements,1 star,Bangkok
+ginza sushi ichi,2019,Bangkok,Thailand,japanese,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi,1 star,Bangkok
+blanca,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca,2 stars,New York
+komi,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi,1 star,"Washington, D.C."
+sushi taro,2019,"Washington, D.C.",Washington DC,japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro,1 star,"Washington, D.C."
+plume,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume,1 star,"Washington, D.C."
+shinji (tanglin road),2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road,1 star,Singapore
+coi,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi,2 stars,San Francisco
+jean-georges,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges,2 stars,New York
+atera,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera,2 stars,New York
+jungsik,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik,2 stars,New York
+gastrologik,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik,2 stars,Stockholm
+campton place,2019,San Francisco,California,indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place,2 stars,San Francisco
+saison,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison,2 stars,San Francisco
+lazy bear,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear,2 stars,San Francisco
+californios,2019,San Francisco,California,mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios,2 stars,San Francisco
+commis,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis,2 stars,San Francisco
+baumé,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume,2 stars,South San Francisco
+odette,2018,Singapore,Singapore,french,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette,2 stars,Singapore
+fiola,2019,"Washington, D.C.",Washington DC,italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola,1 star,"Washington, D.C."
+upper house,2019,Goteborg,Sweden,creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house,1 star,Goteborg
+métier,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier,1 star,"Washington, D.C."
+operakällaren,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren,1 star,Stockholm
+urasawa,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa,2 stars,Los Angeles
+the inn at little washington,2019,"Washington, D.C.",Washington DC,american,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington,3 stars,"Washington, D.C."
+noda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda,1 star,New York
+oaxen krog,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog,2 stars,Stockholm
+l'atelier de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505,2 stars,New York
+dialogue,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue,1 star,Los Angeles
+gaa,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa,1 star,Bangkok
+aloë,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe,1 star,Stockholm
+n/naka,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka,2 stars,Los Angeles
+aquavit,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit,2 stars,New York
+sushi kimura,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura,1 star,Singapore
+smyth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth,2 stars,Chicago
+providence,2019,Los Angeles,California,seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence,2 stars,Los Angeles
+vespertine,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine,2 stars,Los Angeles
+oriole,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole,2 stars,Chicago
+gabriel kreuther,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther,2 stars,New York
+pierre,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre,2 stars,Hong Kong
+koks,2019,Leynar,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780,2 stars,Leynar
+alain ducasse at morpheus,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus,2 stars,Macau
+noma,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma,2 stars,Kobenhavn
+the modern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern,2 stars,New York
+kadeau copenhagen,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen,2 stars,Kobenhavn
+kashiwaya,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya,2 stars,Hong Kong
+ko,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko,2 stars,New York
+somni,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni,2 stars,Los Angeles
+ichimura at uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu,2 stars,New York
+marea,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea,2 stars,New York
+acadia,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia,2 stars,Chicago
+sushi ginza onodera,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850,2 stars,Los Angeles
+agrikultur,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur,1 star,Stockholm
+acquerello,2019,San Francisco,California,italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello,2 stars,San Francisco
+le du,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du,1 star,Bangkok
+saawaan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan,1 star,Bangkok
+kitcho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho,1 star,Taipei
+logy,2019,Taipei,Taipei,other asian,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy,1 star,Taipei
+ken an ho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho,1 star,Taipei
+sushi ryu,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu,1 star,Taipei
+amber,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032,2 stars,Hong Kong
+sushi ginza onodera,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera,2 stars,New York
+aldea,2019,New York,New York City,other european,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea,1 star,New York
+cut,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418,1 star,Los Angeles
+shunji,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji,1 star,Los Angeles
+eleven madison park,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park,3 stars,New York
+le bernardin,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin,3 stars,New York
+per se,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se,3 stars,New York
+masa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa,3 stars,New York
+maaemo,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo,3 stars,Oslo
+chez tj,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj,1 star,South San Francisco
+plumed horse,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse,1 star,South San Francisco
+wakuriya,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya,1 star,San Francisco
+sushi yoshizumi,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi,1 star,San Francisco
+maum,2019,South San Francisco,California,korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum,1 star,South San Francisco
+omakase,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase,1 star,San Francisco
+birdsong,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong,1 star,San Francisco
+in situ,2019,San Francisco,California,international cuisine,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ,1 star,San Francisco
+auberge du soleil,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil,1 star,San Francisco
+chef's table at brooklyn fare,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare,3 stars,New York
+la toque,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque,1 star,San Francisco
+aubergine,2019,Monterey,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine,1 star,Monterey
+madcap,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap,1 star,San Francisco
+wako,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako,1 star,San Francisco
+clou,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou,1 star,Kobenhavn
+gary danko,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko,1 star,San Francisco
+keiko à nob hill,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill,1 star,San Francisco
+jū-ni,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni,1 star,San Francisco
+sons & daughters,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters,1 star,San Francisco
+michael mina,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina,1 star,San Francisco
+angler,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler,1 star,San Francisco
+hashiri,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri,1 star,San Francisco
+kinjo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo,1 star,San Francisco
+kong hans kælder,2019,Kobenhavn,Denmark,french,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder,1 star,Kobenhavn
+frederikshøj,2019,Aarhus,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj,1 star,Aarhus
+kenzo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo,1 star,San Francisco
+nozawa bar,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar,1 star,Los Angeles
+edvard,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard,1 star,Wien
+frantzén,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen,3 stars,Stockholm
+maude,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude,1 star,Los Angeles
+mori sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi,1 star,Los Angeles
+trois mec,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec,1 star,Los Angeles
+le comptoir,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir,1 star,Los Angeles
+q sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi,1 star,Los Angeles
+shibumi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644,1 star,Los Angeles
+hayato,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato,1 star,Los Angeles
+orsa & winston,2019,Los Angeles,California,other asian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston,1 star,Los Angeles
+hana re,2019,Costa Mesa,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re,1 star,Costa Mesa
+addison,2019,San Diego,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison,1 star,San Diego
+harbor house,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house,1 star,San Francisco
+goosefoot,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot,1 star,Chicago
+el ideas,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas,1 star,Chicago
+schwa,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa,1 star,Chicago
+la yeon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon,3 stars,Seoul
+temporis,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis,1 star,Chicago
+everest,2019,Chicago,Chicago,french,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest,1 star,Chicago
+topolobampo,2019,Chicago,Chicago,mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo,1 star,Chicago
+spiaggia,2019,Chicago,Chicago,italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia,1 star,Chicago
+robuchon au dôme,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome,3 stars,Macau
+l'atelier de joël robuchon,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon,3 stars,Hong Kong
+8½ otto e mezzo - bombana,2019,Hong Kong,Hong Kong,italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana,3 stars,Hong Kong
+caprice,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice,3 stars,Hong Kong
+lung king heen,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen,3 stars,Hong Kong
+gotgan,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan,1 star,Seoul
+slotskøkkenet,2019,Horve,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet,1 star,Horve
+elizabeth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth,1 star,Chicago
+madrona manor,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor,1 star,San Francisco
+the kitchen,2019,Sacramento,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371,1 star,Sacramento
+farmhouse inn & restaurant,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant,1 star,San Francisco
+elske,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske,1 star,Chicago
+senses,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses,1 star,Warszawa
+aniar,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar,1 star,Gaillimh Galway
+loam,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam,1 star,Gaillimh Galway
+wild honey inn,2019,Lios Duin Bhearna Lisdoonvarna,Ireland,international cuisine,,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn,1 star,Lios Duin Bhearna Lisdoonvarna
+chestnut,2019,Ballydehob,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut,1 star,Ballydehob
+mews,2019,Baltimore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews,1 star,Baltimore
+ichigo ichie,2019,Corcaigh Cork,Ireland,japanese,,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie,1 star,Corcaigh Cork
+chapter one,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one,1 star,City Centre
+greenhouse,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse,1 star,City Centre
+l'ecrivain,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain,1 star,City Centre
+campagne,2019,Cill Chainnigh Kilkenny,Ireland,british,,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne,1 star,Cill Chainnigh Kilkenny
+heron & grey,2019,Blackrock,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey,1 star,Blackrock
+lady helen,2019,Baile Mhic Andain Thomastown,Ireland,international cuisine,,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen,1 star,Baile Mhic Andain Thomastown
+house,2019,Aird Mhor Ardmore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house,1 star,Aird Mhor Ardmore
+loch bay,2019,Waternish,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay,1 star,Waternish
+braidwoods,2019,Dalry,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods,1 star,Dalry
+eipic,2019,Belfast,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic,1 star,Belfast
+ox,2019,Belfast,United Kingdom,british,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109,1 star,Belfast
+the peat inn,2019,Peat Inn,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn,1 star,Peat Inn
+kitchin,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin,1 star,Leith
+martin wishart,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart,1 star,Leith
+number one,2019,Edinburgh,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one,1 star,Edinburgh
+21212,2019,Edinburgh,United Kingdom,creative,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212,1 star,Edinburgh
+the cellar,2019,Anstruther,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar,1 star,Anstruther
+forest side,2019,Grasmere,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side,1 star,Grasmere
+hrishi,2019,Bowness On Windermere,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi,1 star,Bowness On Windermere
+rogan & co,2019,Cartmel,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co,1 star,Cartmel
+house of tides,2019,Newcastle Upon Tyne,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides,1 star,Newcastle Upon Tyne
+sosban & the old butchers,2019,Menai Bridge Porthaethwy,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers,1 star,Menai Bridge Porthaethwy
+northcote,2019,Langho,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote,1 star,Langho
+yorke arms,2019,Pateley Bridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms,1 star,Pateley Bridge
+fraiche,2019,Birkenhead,United Kingdom,creative,,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche,1 star,Birkenhead
+white swan,2019,Fence,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan,1 star,Fence
+tyddyn llan,2019,Llandrillo,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan,1 star,Llandrillo
+ynyshir,2019,Machynlleth,United Kingdom,creative,,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir,1 star,Machynlleth
+black swan,2019,Oldstead,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan,1 star,Oldstead
+simon radley at chester grosvenor,2019,Chester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor,1 star,Chester
+star inn at harome,2019,Harome,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome,1 star,Harome
+the man behind the curtain,2019,Leeds,United Kingdom,creative,,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain,1 star,Leeds
+the checkers,2019,Montgomery Trefaldwyn,United Kingdom,french,,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers,1 star,Montgomery Trefaldwyn
+pipe and glass,2019,South Dalton,United Kingdom,british,,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass,1 star,South Dalton
+fischer's at baslow hall,2019,Baslow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall,1 star,Baslow
+winteringham fields,2019,Winteringham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields,1 star,Winteringham
+thomas carr @ the olive room,2019,Ilfracombe,United Kingdom,seafood,,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room,1 star,Ilfracombe
+walnut tree,2019,Llanddewi Skirrid,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree,1 star,Llanddewi Skirrid
+paul ainsworth at no.6,2019,Padstow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6,1 star,Padstow
+simpsons,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons,1 star,Birmingham
+purnell's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s,1 star,Birmingham
+adam's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s,1 star,Birmingham
+outlaw's fish kitchen,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen,1 star,Port Isaac
+carters of moseley,2019,Birmingham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley,1 star,Birmingham
+peel's,2019,Hampton In Arden,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s,1 star,Hampton In Arden
+john's house,2019,Mountsorrel,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house,1 star,Mountsorrel
+the whitebrook,2019,Whitebrook,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook,1 star,Whitebrook
+james sommerin,2019,Penarth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin,1 star,Penarth
+driftwood,2019,Portscatho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood,1 star,Portscatho
+the cross at kenilworth,2019,Kenilworth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth,1 star,Kenilworth
+masons arms,2019,Knowstone,United Kingdom,french,,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms,1 star,Knowstone
+salt,2019,Stratford Upon Avon,United Kingdom,british,,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869,1 star,Stratford Upon Avon
+le champignon sauvage,2019,Cheltenham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage,1 star,Cheltenham
+gidleigh park,2019,Chagford,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park,1 star,Chagford
+hambleton hall,2019,Upper Hambleton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall,1 star,Upper Hambleton
+wilks,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks,1 star,Bristol
+bulrush,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush,1 star,Bristol
+casamia,2019,Bristol,United Kingdom,creative,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia,1 star,Bristol
+paco tapas,2019,Bristol,United Kingdom,spanish,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas,1 star,Bristol
+pony & trap,2019,Chew Magna,United Kingdom,british,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap,1 star,Chew Magna
+the dining room,2019,Malmesbury,United Kingdom,other asian,,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454,1 star,Malmesbury
+bybrook,2019,Castle Combe,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook,1 star,Castle Combe
+restaurant hywel jones by lucknam park,2019,Colerne,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park,1 star,Colerne
+olive tree,2019,Bath,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree,1 star,Bath
+lympstone manor,2019,Lympstone,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor,1 star,Lympstone
+the neptune,2019,Hunstanton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune,1 star,Hunstanton
+elephant,2019,Torquay,United Kingdom,british,,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant,1 star,Torquay
+oxford kitchen,2019,Oxford,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen,1 star,Oxford
+nut tree,2019,Murcott,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree,1 star,Murcott
+morston hall,2019,Morston,United Kingdom,british,,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall,1 star,Morston
+red lion freehouse,2019,East Chisenbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse,1 star,East Chisenbury
+blackbird,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018,1 star,Newbury
+woodspeen,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen,1 star,Newbury
+the coach,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach,1 star,Marlow
+crown,2019,Burchett'S Green,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549,1 star,Burchett'S Green
+l'ortolan,2019,Shinfield,United Kingdom,french,,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan,1 star,Shinfield
+hinds head,2019,Bray,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head,1 star,Bray
+black rat,2019,Winchester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat,1 star,Winchester
+matt worswick at the latymer,2019,Bagshot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer,1 star,Bagshot
+coworth park,2019,Ascot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park,1 star,Ascot
+tudor room,2019,Egham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room,1 star,Egham
+the glasshouse,2019,Kew,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse,1 star,Kew
+la trompette,2019,Chiswick,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette,1 star,Chiswick
+tim allen's flitch of bacon,2019,Little Dunmow,United Kingdom,british,,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon,1 star,Little Dunmow
+river café,2019,Hammersmith,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe,1 star,Hammersmith
+kitchen w8,2019,Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8,1 star,Kensington
+trishna,2019,Marylebone,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna,1 star,Marylebone
+roganic,2019,Marylebone,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic,1 star,Marylebone
+locanda locatelli,2019,Marylebone,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli,1 star,Marylebone
+texture,2019,Marylebone,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture,1 star,Marylebone
+portland,2019,Regent'S Park,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland,1 star,Regent'S Park
+harwood arms,2019,Fulham,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms,1 star,Fulham
+pied à terre,2019,Bloomsbury,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre,1 star,Bloomsbury
+the ninth,2019,Bloomsbury,United Kingdom,other european,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth,1 star,Bloomsbury
+kai,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638,1 star,Mayfair
+pollen street social,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social,1 star,Mayfair
+hakkasan mayfair,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair,1 star,Mayfair
+the square,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391,1 star,Mayfair
+alyn williams at the westbury,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury,1 star,Mayfair
+benares,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares,1 star,Mayfair
+hakkasan hanway place,2019,Bloomsbury,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place,1 star,Bloomsbury
+social eating house,2019,Soho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house,1 star,Soho
+galvin at windows,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows,1 star,Mayfair
+murano,2019,Mayfair,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano,1 star,Mayfair
+marcus,2019,Belgravia,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus,1 star,Belgravia
+sabor,2019,Mayfair,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor,1 star,Mayfair
+clock house,2019,Ripley,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house,1 star,Ripley
+yauatcha soho,2019,Soho,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho,1 star,Soho
+céleste,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste,1 star,Belgravia
+gymkhana,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana,1 star,Mayfair
+amaya,2019,Belgravia,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya,1 star,Belgravia
+pétrus,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688,1 star,Belgravia
+veeraswamy,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy,1 star,Mayfair
+barrafina,2019,Soho,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina,1 star,Soho
+hide,2019,Mayfair,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide,1 star,Mayfair
+ritz restaurant,2019,Westminster,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant,1 star,Westminster
+elystan street,2019,Chelsea,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street,1 star,Chelsea
+seven park place,2019,Saint James'S,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place,1 star,Saint James'S
+ikoyi,2019,Saint James'S,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi,1 star,Saint James'S
+aquavit,2019,Saint James'S,United Kingdom,scandinavian,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082,1 star,Saint James'S
+five fields,2019,Chelsea,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields,1 star,Chelsea
+dining room at the goring,2019,Victoria,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring,1 star,Victoria
+st john,2019,Clerkenwell,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john,1 star,Clerkenwell
+quilon,2019,Victoria,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon,1 star,Victoria
+club gascon,2019,London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon,1 star,London
+a. wong,2019,Victoria,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong,1 star,Victoria
+the clove club,2019,Shoreditch,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club,1 star,Shoreditch
+leroy,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy,1 star,Shoreditch
+angler,2019,Finsbury,United Kingdom,seafood,,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235,1 star,Finsbury
+brat,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat,1 star,Shoreditch
+lyle's,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s,1 star,Shoreditch
+galvin la chapelle,2019,Spitalfields,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle,1 star,Spitalfields
+city social,2019,City Of London,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social,1 star,City Of London
+la dame de pic,2019,City Of London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic,1 star,City Of London
+story,2019,Bermondsey,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story,1 star,Bermondsey
+trinity,2019,Clapham Common,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity,1 star,Clapham Common
+chez bruce,2019,Wandsworth,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce,1 star,Wandsworth
+sorrel,2019,Dorking,United Kingdom,british,,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459,1 star,Dorking
+restaurant tristan,2019,Horsham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan,1 star,Horsham
+gravetye manor,2019,Gravetye,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor,1 star,Gravetye
+the sportsman,2019,Seasalter,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman,1 star,Seasalter
+west house,2019,Biddenden,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house,1 star,Biddenden
+fordwich arms,2019,Fordwich,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms,1 star,Fordwich
+samphire,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987,1 star,Saint Helier Saint Helier
+bohemia,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia,1 star,Saint Helier Saint Helier
+patrick guilbaud,2019,City Centre,Ireland,french,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud,2 stars,City Centre
+andrew fairlie at gleneagles,2019,Auchterarder,United Kingdom,french,,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles,2 stars,Auchterarder
+l'enclume,2019,Cartmel,United Kingdom,creative,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume,2 stars,Cartmel
+raby hunt,2019,Summerhouse,United Kingdom,british,,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt,2 stars,Summerhouse
+moor hall,2019,Aughton,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall,2 stars,Aughton
+restaurant sat bains,2019,Nottingham,United Kingdom,creative,,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains,2 stars,Nottingham
+restaurant nathan outlaw,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw,2 stars,Port Isaac
+belmond le manoir aux quat' saisons,2019,Great Milton,United Kingdom,french,,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons,2 stars,Great Milton
+midsummer house,2019,Cambridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house,2 stars,Cambridge
+hand and flowers,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers,2 stars,Marlow
+ledbury,2019,North Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury,2 stars,North Kensington
+core by clare smyth,2019,North Kensington,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth,2 stars,North Kensington
+le gavroche,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche,2 stars,Mayfair
+kitchen table at bubbledogs,2019,Bloomsbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs,2 stars,Bloomsbury
+hélène darroze at the connaught,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught,2 stars,Mayfair
+dinner by heston blumenthal,2019,Hyde Park,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal,2 stars,Hyde Park
+umu,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu,2 stars,Mayfair
+sketch (the lecture room & library),2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library,2 stars,Mayfair
+greenhouse,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393,2 stars,Mayfair
+claude bosi at bibendum,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum,2 stars,Chelsea
+fat duck,2019,Bray,United Kingdom,creative,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck,3 stars,Bray
+waterside inn,2019,Bray,United Kingdom,french,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn,3 stars,Bray
+alain ducasse at the dorchester,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester,3 stars,Mayfair
+the araki,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki,3 stars,Mayfair
+gordon ramsay,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay,3 stars,Chelsea
diff --git a/data/clean/CleanStarss.csv b/data/clean/CleanStarss.csv
new file mode 100644
index 00000000..30eb35f4
--- /dev/null
+++ b/data/clean/CleanStarss.csv
@@ -0,0 +1,696 @@
+name,year,city,region,cuisine,price,url,stars,cuisine_original,major_city
+ho hung kee,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee,1 star,noodles and congee,Hong Kong
+feng wei ju,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju,2 stars,hunanese and sichuan,Macau
+imperial treasure fine teochew cuisine (orchard),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard,1 star,chinese,Singapore
+shisen hanten,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten,2 stars,chinese,Singapore
+ma cuisine,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine,1 star,french,Singapore
+alma,2018,Singapore,Singapore,other european,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma,1 star,european contemporary,Singapore
+lei garden,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509,1 star,cantonese,Singapore
+the song of india,2018,Singapore,Singapore,indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india,1 star,indian,Singapore
+putien (kitchener road),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road,1 star,fujian,Singapore
+hill street tai hwa pork noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle,1 star,street food,Singapore
+jin jin,2019,Seoul,South Korea,chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin,1 star,chinese,Seoul
+summer palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658,1 star,cantonese,Singapore
+cheek by jowl,2018,Singapore,Singapore,australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl,1 star,australian,Singapore
+nouri,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri,1 star,innovative,Singapore
+liao fan hong kong soya sauce chicken rice & noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle,1 star,street food,Singapore
+garibaldi,2018,Singapore,Singapore,italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi,1 star,italian,Singapore
+candlenut,2018,Singapore,Singapore,other asian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut,1 star,peranakan,Singapore
+rhubarb,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb,1 star,french contemporary,Singapore
+tim ho wan (sham shui po),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po,1 star,dim sum,Hong Kong
+lei garden (mong kok),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok,1 star,cantonese,Hong Kong
+yè shanghai (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui,1 star,shanghainese,Hong Kong
+crystal jade golden palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace,1 star,chinese,Singapore
+pang's kitchen,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen,1 star,cantonese,Hong Kong
+qi (wan chai),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai,1 star,sichuan,Hong Kong
+kam's roast goose,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose,1 star,cantonese roast meats,Hong Kong
+yat lok,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok,1 star,cantonese roast meats,Hong Kong
+king,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226,1 star,cantonese,Macau
+the golden peacock,2019,Macau,Macau,indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock,1 star,indian,Macau
+suan thip,2019,Bangkok,Thailand,other asian,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip,1 star,thai,Bangkok
+da san yuan,2019,Taipei,Taipei,chinese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan,1 star,cantonese,Taipei
+lei garden (kwun tong),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong,1 star,cantonese,Hong Kong
+tainan tan tsu mien seafood,2019,Taipei,Taipei,seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood,1 star,seafood,Taipei
+parachute,2019,Chicago,Chicago,other asian,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute,1 star,fusion,Chicago
+beefbar,2019,Hong Kong,Hong Kong,american,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar,1 star,steakhouse,Hong Kong
+claro,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro,1 star,mexican,New York
+yee tung heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen,1 star,cantonese,Hong Kong
+the guest house,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house,2 stars,sichuan-huai yang,Taipei
+lai heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen,1 star,cantonese,Macau
+ying,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying,1 star,cantonese,Macau
+fu ho (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui,1 star,cantonese,Hong Kong
+im teppanyaki & wine,2019,Hong Kong,Hong Kong,japanese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine,1 star,teppanyaki,Hong Kong
+ah yat harbour view (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui,1 star,cantonese,Hong Kong
+methavalai sorndaeng,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng,1 star,thai,Bangkok
+imperial treasure fine chinese cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine,1 star,cantonese,Hong Kong
+zi yat heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen,1 star,cantonese,Macau
+corner house,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house,1 star,innovative,Singapore
+yat tung heen (jordan),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan,1 star,cantonese,Hong Kong
+jade dragon,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon,3 stars,cantonese,Macau
+balwoo gongyang,2019,Seoul,South Korea,korean,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang,1 star,temple cuisine,Seoul
+le palais,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais,3 stars,cantonese,Taipei
+bistro na's,2019,Los Angeles,California,chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s,1 star,chinese,Los Angeles
+kin khao,2019,San Francisco,California,other asian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao,1 star,thai,San Francisco
+commonwealth,2019,San Francisco,California,contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth,1 star,contemporary,San Francisco
+béni,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni,1 star,french contemporary,Singapore
+forum,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum,2 stars,cantonese,Hong Kong
+labyrinth,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth,1 star,innovative,Singapore
+braci,2018,Singapore,Singapore,italian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci,1 star,italian contemporary,Singapore
+sun tung lok,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok,2 stars,cantonese,Hong Kong
+casa enríque,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique,1 star,mexican,New York
+meta,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta,1 star,innovative,Singapore
+bacchanalia,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia,1 star,innovative,Singapore
+shinji (bras basah road),2018,Singapore,Singapore,japanese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road,1 star,sushi,Singapore
+summer pavilion,2018,Singapore,Singapore,chinese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion,1 star,cantonese,Singapore
+state bird provisions,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions,1 star,american,San Francisco
+faro,2019,New York,New York City,american,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro,1 star,american,New York
+saint pierre,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre,1 star,french contemporary,Singapore
+whitegrass,2018,Singapore,Singapore,australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass,1 star,australian,Singapore
+rose's luxury,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury,1 star,contemporary,"Washington, D.C."
+burnt ends,2018,Singapore,Singapore,american,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends,1 star,barbecue,Singapore
+zhejiang heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen,1 star,shanghainese,Hong Kong
+uncle boons,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons,1 star,thai,New York
+bloom in the park,2019,Malmo,Sweden,creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park,1 star,creative,Malmo
+golden formosa,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa,1 star,taiwanese,Taipei
+ruean panya,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya,1 star,thai,Bangkok
+søllerød kro,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro,1 star,modern cuisine,Kobenhavn
+relæ,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae,1 star,modern cuisine,Kobenhavn
+kiin kiin,2019,Kobenhavn,Denmark,other asian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin,1 star,thai,Kobenhavn
+formel b,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b,1 star,modern cuisine,Kobenhavn
+danny's steakhouse,2019,Taipei,Taipei,american,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse,1 star,steakhouse,Taipei
+marchal,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal,1 star,modern cuisine,Kobenhavn
+tien hsiang lo,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo,1 star,hang zhou,Taipei
+era ora,2019,Kobenhavn,Denmark,italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora,1 star,italian,Kobenhavn
+ya ge,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge,1 star,cantonese,Taipei
+ying jee club,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club,2 stars,cantonese,Hong Kong
+jardin de jade (wan chai),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai,1 star,shanghainese,Hong Kong
+tail up goat,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat,1 star,contemporary,"Washington, D.C."
+mathias dahlgren-matbaren,2019,Stockholm,Sweden,international cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren,1 star,modern cuisine,Stockholm
+jeju noodle bar,2019,New York,New York City,korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar,1 star,korean,New York
+saneh jaan,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan,1 star,thai,Bangkok
+mizumi (macau),2019,Macau,Macau,japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau,2 stars,japanese,Macau
+al's place,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place,1 star,californian,San Francisco
+wing lei,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei,1 star,cantonese,Macau
+golden flower,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower,2 stars,chinese,Macau
+tim's kitchen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen,1 star,cantonese,Macau
+celebrity cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine,1 star,cantonese,Hong Kong
+mountain and sea house,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house,1 star,taiwanese,Taipei
+rasa,2019,San Francisco,California,indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa,1 star,indian,San Francisco
+jay fai,2019,Bangkok,Thailand,international cuisine,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai,1 star,street food,Bangkok
+longtail,2019,Taipei,Taipei,international cuisine,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail,1 star,innovative,Taipei
+tuome,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome,1 star,fusion,New York
+dusek's (board & beer),2019,Chicago,Chicago,gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer,1 star,gastropub,Chicago
+café china,2019,New York,New York City,chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china,1 star,chinese,New York
+the dabney,2019,"Washington, D.C.",Washington DC,american,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney,1 star,american,"Washington, D.C."
+bresca,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca,1 star,contemporary,"Washington, D.C."
+loaf on,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on,1 star,cantonese,Hong Kong
+new punjab club,2019,Hong Kong,Hong Kong,indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club,1 star,indian,Hong Kong
+kwonsooksoo,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo,2 stars,korean,Seoul
+shin sushi,2019,Los Angeles,California,japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi,1 star,japanese,Los Angeles
+osteria mozza,2019,Los Angeles,California,italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza,1 star,italian,Los Angeles
+kali,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali,1 star,californian,Los Angeles
+bouchon,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon,1 star,french,San Francisco
+taco maría,2019,Costa Mesa,California,mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria,1 star,mexican,Costa Mesa
+bar crenn,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn,1 star,french,San Francisco
+alla prima,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima,2 stars,innovative,Seoul
+henne kirkeby kro,2019,Henne,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro,2 stars,classic cuisine,Henne
+band of bohemia,2019,Chicago,Chicago,gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia,1 star,gastropub,Chicago
+masseria,2019,"Washington, D.C.",Washington DC,italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria,1 star,italian,"Washington, D.C."
+entente,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente,1 star,contemporary,Chicago
+kinship,2019,"Washington, D.C.",Washington DC,contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship,1 star,contemporary,"Washington, D.C."
+rustic canyon,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon,1 star,californian,Los Angeles
+mingles,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles,2 stars,korean contemporary,Seoul
+duddell's,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s,1 star,cantonese,Hong Kong
+mourad,2019,San Francisco,California,moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad,1 star,moroccan,San Francisco
+boka,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka,1 star,contemporary,Chicago
+roister,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister,1 star,contemporary,Chicago
+jungsik,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965,2 stars,korean contemporary,Seoul
+sorrel,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel,1 star,californian,San Francisco
+lord stanley,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley,1 star,californian,San Francisco
+north pond,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond,1 star,contemporary,Chicago
+blue duck tavern,2019,"Washington, D.C.",Washington DC,american,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern,1 star,american,"Washington, D.C."
+kato,2019,Los Angeles,California,other asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato,1 star,asian,Los Angeles
+domestic,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic,1 star,modern cuisine,Aarhus
+me‚mu,2019,Vejle,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu,1 star,modern cuisine,Vejle
+jordnær,2019,Kobenhavn,Denmark,danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer,1 star,danish,Kobenhavn
+kokkeriet,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet,1 star,modern cuisine,Kobenhavn
+gastromé,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome,1 star,modern cuisine,Aarhus
+sepia,2019,Chicago,Chicago,american,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia,1 star,american,Chicago
+blackbird,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird,1 star,contemporary,Chicago
+substans,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans,1 star,modern cuisine,Aarhus
+stud!o at the standard,2019,Kobenhavn,Denmark,creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard,1 star,creative,Kobenhavn
+the progress,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress,1 star,californian,San Francisco
+ti trin ned,2019,Fredericia,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned,1 star,modern cuisine,Fredericia
+olo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo,1 star,modern cuisine,Helsingfors   Helsinki
+rich table,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table,1 star,contemporary,San Francisco
+octavia,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia,1 star,californian,San Francisco
+demo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo,1 star,modern cuisine,Helsingfors   Helsinki
+grön,2019,Helsingfors   Helsinki,Finland,finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron,1 star,finnish,Helsingfors   Helsinki
+écriture,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture,2 stars,french contemporary,Hong Kong
+palace,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace,1 star,modern cuisine,Helsingfors   Helsinki
+mister jiu's,2019,San Francisco,California,chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius,1 star,chinese,San Francisco
+sushi wadatsumi,2019,Hong Kong,Hong Kong,japanese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi,1 star,sushi,Hong Kong
+j'aime by jean-michel lorain,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain,1 star,french contemporary,Bangkok
+frederiksminde,2019,Praesto,Denmark,creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde,1 star,creative,Praesto
+t'ang court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court,3 stars,cantonese,Hong Kong
+savelberg,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg,1 star,french contemporary,Bangkok
+the eight,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight,3 stars,chinese,Macau
+ask,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask,1 star,modern cuisine,Helsingfors   Helsinki
+ora,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora,1 star,modern cuisine,Helsingfors   Helsinki
+nahm,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm,1 star,thai,Bangkok
+xin rong ji,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji,1 star,taizhou,Hong Kong
+tin lung heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen,2 stars,cantonese,Hong Kong
+les amis,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis,2 stars,french,Singapore
+aster,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster,1 star,californian,San Francisco
+épure,2019,nan,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure,1 star,french,nan
+summer palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace,1 star,cantonese,Hong Kong
+alouette,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette,1 star,modern cuisine,Kobenhavn
+tosca,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519,1 star,italian,Hong Kong
+octavium,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium,1 star,italian,Hong Kong
+spring moon,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon,1 star,cantonese,Hong Kong
+shang palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace,1 star,cantonese,Hong Kong
+ming court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court,1 star,cantonese,Hong Kong
+nico,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico,1 star,contemporary,San Francisco
+arbor,2019,nan,Hong Kong,international cuisine,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor,1 star,innovative,nan
+luce,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce,1 star,contemporary,San Francisco
+yan toh heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen,2 stars,cantonese,Hong Kong
+108,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108,1 star,modern cuisine,Kobenhavn
+spruce,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce,1 star,californian,San Francisco
+spqr,2019,San Francisco,California,italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr,1 star,italian,San Francisco
+arcane,2019,Hong Kong,Hong Kong,other european,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane,1 star,european contemporary,Hong Kong
+the kitchen,2019,Macau,Macau,american,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810,1 star,steakhouse,Macau
+chim by siam wisdom,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom,1 star,thai,Bangkok
+stand,2019,Budapest,Hungary,international cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand,1 star,modern cuisine,Budapest
+sra bua by kiin kiin,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin,1 star,thai contemporary,Bangkok
+belon,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon,1 star,french,Hong Kong
+paste,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste,1 star,thai,Bangkok
+cut,2018,Singapore,Singapore,american,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut,1 star,steakhouse,Singapore
+8 1/2 otto e mezzo - bombana,2019,Macau,Macau,italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana,1 star,italian,Macau
+sushi nomura,2019,Taipei,Taipei,japanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura,1 star,sushi,Taipei
+impromptu by paul lee,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee,1 star,innovative,Taipei
+the finch,2019,New York,New York City,american,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch,1 star,american,New York
+oxomoco,2019,New York,New York City,mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco,1 star,mexican,New York
+jewel bako,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako,1 star,japanese,New York
+ekstedt,2019,Stockholm,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt,1 star,meats and grills,Stockholm
+kontrast,2019,Oslo,Norway,scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast,1 star,scandinavian,Oslo
+jaan,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan,1 star,french contemporary,Singapore
+chef kang's,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s,1 star,cantonese,Singapore
+galt,2019,Oslo,Norway,international cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt,1 star,modern cuisine,Oslo
+siren by rw,2019,"Washington, D.C.",Washington DC,seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw,1 star,seafood,"Washington, D.C."
+sushi ichi,2018,Singapore,Singapore,japanese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi,1 star,sushi,Singapore
+exquisine,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine,1 star,innovative,Seoul
+pearl dragon,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon,1 star,cantonese,Macau
+joo ok,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok,1 star,korean contemporary,Seoul
+the village pub,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub,1 star,contemporary,San Francisco
+muoki,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki,1 star,innovative,Seoul
+yu yuan,2019,Seoul,South Korea,chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan,1 star,chinese,Seoul
+l'amitié,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie,1 star,french,Seoul
+dosa,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871,1 star,innovative,Seoul
+poom,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom,1 star,korean,Seoul
+zero complex,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex,1 star,innovative,Seoul
+soigné,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne,1 star,innovative,Seoul
+table for four,2019,Seoul,South Korea,other european,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four,1 star,european contemporary,Seoul
+the tasting room,2019,Macau,Macau,french,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room,2 stars,french contemporary,Macau
+kanoyama,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama,1 star,japanese,New York
+volt,2019,Stockholm,Sweden,creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt,1 star,creative,Stockholm
+bicena,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena,1 star,korean,Seoul
+the clocktower,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower,1 star,contemporary,New York
+sushi sho,2019,Stockholm,Sweden,japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho,1 star,japanese,Stockholm
+casa mono,2019,New York,New York City,spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono,1 star,spanish,New York
+nix,2019,New York,New York City,vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix,1 star,vegetarian,New York
+bouley at home,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home,1 star,contemporary,New York
+kosushi,2019,Sao Paulo,Sao Paulo,japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi,1 star,japanese,Sao Paulo
+taïrroir,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir,2 stars,innovative,Taipei
+cote,2019,New York,New York City,korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote,1 star,korean,New York
+jiang-nan chun,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun,1 star,cantonese,Singapore
+raw,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw,2 stars,innovative,Taipei
+le coucou,2019,New York,New York City,french,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou,1 star,french,New York
+l'atelier de joël robuchon,2019,Taipei,Taipei,french,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759,1 star,french contemporary,Taipei
+iggy's,2018,Singapore,Singapore,other european,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s,1 star,european contemporary,Singapore
+stay,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay,1 star,french contemporary,Seoul
+hirohisa,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa,1 star,japanese,New York
+da-wan,2019,Taipei,Taipei,american,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan,1 star,barbecue,Taipei
+dining in space,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space,1 star,french contemporary,Seoul
+hansikgonggan,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan,1 star,korean,Seoul
+kyo ya,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya,1 star,japanese,New York
+bhoga,2019,Goteborg,Sweden,creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga,1 star,creative,Goteborg
+meadowsweet,2019,New York,New York City,other european,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet,1 star,mediterranean,New York
+protégé,2019,South San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege,1 star,contemporary,South San Francisco
+thörnströms kök,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok,1 star,classic cuisine,Goteborg
+sk mat & människor,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor,1 star,modern cuisine,Goteborg
+28+,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28,1 star,modern cuisine,Goteborg
+koka,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka,1 star,modern cuisine,Goteborg
+guo fu lou,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828,1 star,cantonese,Hong Kong
+kajitsu,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu,1 star,japanese,New York
+mume,2019,Taipei,Taipei,other european,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume,1 star,european contemporary,Taipei
+madera,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera,1 star,contemporary,San Francisco
+sav,2019,Malmo,Sweden,creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav,1 star,creative,Malmo
+ming fu,2019,Taipei,Taipei,other asian,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu,1 star,taiwanese,Taipei
+the musket room,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room,1 star,contemporary,New York
+pm & vänner,2019,Vaxjo,Sweden,creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner,1 star,creative,Vaxjo
+quince,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince,3 stars,contemporary,San Francisco
+man wah,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah,1 star,cantonese,Hong Kong
+costes downtown,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown,1 star,modern cuisine,Budapest
+singlethread,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread,3 stars,contemporary,San Francisco
+borkonyha winekitchen,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen,1 star,modern cuisine,Budapest
+zz's clam bar,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar,1 star,seafood,New York
+manresa,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa,3 stars,contemporary,South San Francisco
+shinji by kanesaka,2019,Macau,Macau,japanese,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka,1 star,sushi,Macau
+alinea,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea,3 stars,contemporary,Chicago
+rech,2019,Hong Kong,Hong Kong,seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech,1 star,seafood,Hong Kong
+mandarin grill + bar,2019,Hong Kong,Hong Kong,other european,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar,1 star,european contemporary,Hong Kong
+fagn,2019,Trondheim,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn,1 star,modern cuisine,Trondheim
+atelier crenn,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn,3 stars,contemporary,San Francisco
+benu,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu,3 stars,asian,San Francisco
+re-naa,2019,Stavanger,Norway,creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa,1 star,creative,Stavanger
+credo,2019,Trondheim,Norway,creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo,1 star,creative,Trondheim
+tuju,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju,2 stars,creative,Sao Paulo
+gaon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon,3 stars,korean,Seoul
+the french laundry,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry,3 stars,contemporary,San Francisco
+gaggan,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan,2 stars,innovative,Bangkok
+the restaurant at meadowood,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood,3 stars,contemporary,San Francisco
+bâtard,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard,1 star,contemporary,New York
+geranium,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium,3 stars,creative,Kobenhavn
+kosaka,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka,1 star,japanese,New York
+wallsé,2019,New York,New York City,austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse,1 star,austrian,New York
+okuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda,1 star,japanese,New York
+le grill de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon,1 star,french,New York
+del posto,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto,1 star,italian,New York
+statholdergaarden,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden,1 star,classic cuisine,Oslo
+sabi omakase,2019,Stavanger,Norway,japanese,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase,1 star,sushi,Stavanger
+sushi nakazawa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa,1 star,japanese,New York
+l'appart,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart,1 star,french,New York
+daniel berlin,2019,Skane Tranas,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin,2 stars,creative,Skane Tranas
+aska,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska,2 stars,scandinavian,New York
+nomad,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad,1 star,contemporary,New York
+sushi shikon,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon,3 stars,sushi,Hong Kong
+daniel,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel,2 stars,french,New York
+gramercy tavern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern,1 star,contemporary,New York
+ai fiori,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori,1 star,italian,New York
+the river café,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe,1 star,contemporary,New York
+bar uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu,1 star,japanese,New York
+contra,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra,1 star,contemporary,New York
+atomix,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix,1 star,korean,New York
+agern,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern,1 star,scandinavian,New York
+caviar russe,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe,1 star,contemporary,New York
+tempura matsui,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui,1 star,japanese,New York
+sushi yasuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda,1 star,japanese,New York
+sushi amane,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane,1 star,japanese,New York
+blue hill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill,1 star,american,New York
+café boulud,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud,1 star,french,New York
+vollmers,2019,Malmo,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers,2 stars,creative,Malmo
+shoun ryugin,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin,2 stars,japanese contemporary,Taipei
+sushi amamoto,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto,2 stars,sushi,Taipei
+sühring,2019,Bangkok,Thailand,other european,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring,2 stars,european contemporary,Bangkok
+carbone,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone,1 star,italian,New York
+le normandie,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie,2 stars,french contemporary,Bangkok
+pineapple and pearls,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls,2 stars,contemporary,"Washington, D.C."
+atelier amaro,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro,1 star,modern cuisine,Warszawa
+peter luger,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger,1 star,steakhouse,New York
+sushi inoue,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue,1 star,japanese,New York
+sushi noz,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz,1 star,japanese,New York
+satsuki,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki,1 star,japanese,New York
+minibar,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar,2 stars,contemporary,"Washington, D.C."
+babbo,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo,1 star,italian,New York
+lee jong kuk 104,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104,1 star,korean,Seoul
+amador,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador,3 stars,creative,Wien
+pfefferschiff,2019,Hallwang,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff,1 star,classic cuisine,Hallwang
+shoukouwa,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa,2 stars,sushi,Singapore
+olympe,2019,Rio De Janeiro,Rio de Janeiro,french,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe,1 star,french,Rio De Janeiro
+lasai,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai,1 star,modern,Rio De Janeiro
+ta vie,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie,2 stars,innovative,Hong Kong
+sushi saito,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito,2 stars,sushi,Hong Kong
+tenku ryugin,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin,2 stars,japanese,Hong Kong
+spondi,2019,Athina,Greece,french,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi,2 stars,french,Athina
+steirereck im stadtpark,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark,2 stars,creative,Wien
+silvio nickol gourmet restaurant,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant,2 stars,modern cuisine,Wien
+konstantin filippou,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou,2 stars,modern cuisine,Wien
+mraz & sohn,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn,2 stars,creative,Wien
+ikarus,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus,2 stars,creative,Salzburg
+senns.restaurant,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant,2 stars,creative,Salzburg
+pru,2019,Phuket,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru,1 star,innovative,Phuket
+sorn,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn,1 star,southern thai,Bangkok
+costes,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes,1 star,modern cuisine,Budapest
+bo.lan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan,1 star,thai,Bangkok
+mosu,2019,Seoul,South Korea,international cuisine,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu,1 star,innovative,Seoul
+huto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto,1 star,japanese,Sao Paulo
+kan suke,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke,1 star,japanese,Sao Paulo
+kinoshita,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita,1 star,japanese,Sao Paulo
+tangará jean-georges,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges,1 star,modern,Sao Paulo
+ryo gastronomia,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia,1 star,japanese,Sao Paulo
+picchi,2019,Sao Paulo,Sao Paulo,italian,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi,1 star,italian,Sao Paulo
+evvai,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai,1 star,modern,Sao Paulo
+maní,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani,1 star,creative,Sao Paulo
+jun sakamoto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto,1 star,japanese,Sao Paulo
+cipriani,2019,Rio De Janeiro,Rio de Janeiro,italian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani,1 star,italian,Rio De Janeiro
+mee,2019,Rio De Janeiro,Rio de Janeiro,other asian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee,1 star,asian influences,Rio De Janeiro
+oteque,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque,1 star,modern,Rio De Janeiro
+kadeau bornholm,2019,Pedersker,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm,1 star,creative,Pedersker
+upstairs at mikkeller,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller,1 star,innovative,Bangkok
+oro,2019,Rio De Janeiro,Rio de Janeiro,creative,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro,2 stars,creative,Rio De Janeiro
+noel,2019,Zagreb,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel,1 star,modern cuisine,Zagreb
+carpe diem,2019,Salzburg,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem,1 star,market cuisine,Salzburg
+d.o.m.,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m,2 stars,creative,Sao Paulo
+kojima,2019,Seoul,South Korea,japanese,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima,2 stars,sushi,Seoul
+waku ghin,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin,2 stars,japanese contemporary,Singapore
+mezzaluna,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna,2 stars,innovative,Bangkok
+bo innovation,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation,3 stars,innovative,Hong Kong
+kilian stuba,2019,Kleinwalsertal,Austria,creative,$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba,1 star,creative,Kleinwalsertal
+onyx,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx,2 stars,modern cuisine,Budapest
+the ocean,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean,1 star,french,Hong Kong
+tate,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate,1 star,innovative,Hong Kong
+kaiseki den by saotome,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome,1 star,japanese,Hong Kong
+vea,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea,1 star,innovative,Hong Kong
+takumi by daisuke mori,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori,1 star,innovative,Hong Kong
+sushi tokami,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami,1 star,sushi,Hong Kong
+hytra,2019,Athina,Greece,international cuisine,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra,1 star,modern cuisine,Athina
+esszimmer,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer,1 star,creative,Salzburg
+varoulko seaside,2019,Athina,Greece,seafood,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside,1 star,seafood,Athina
+la degustation bohême bourgeoise,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise,1 star,modern cuisine,Praha
+field,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field,1 star,modern cuisine,Praha
+360º,2019,Dubrovnik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA,1 star,modern cuisine,Dubrovnik
+babel,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel,1 star,modern cuisine,Budapest
+pelegrini,2019,Sibenik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini,1 star,modern cuisine,Sibenik
+draga di lovrana,2019,Lovran,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana,1 star,modern cuisine,Lovran
+monte,2019,Rovinj,Croatia,creative,$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte,1 star,creative,Rovinj
+le ciel by toni mörwald,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald,1 star,classic cuisine,Wien
+aend,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend,1 star,modern cuisine,Wien
+tian,2019,Wien,Austria,vegetarian,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian,1 star,vegetarian,Wien
+shiki,2019,Wien,Austria,japanese,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki,1 star,japanese,Wien
+walter bauer,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer,1 star,classic cuisine,Wien
+pramerl & the wolf,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf,1 star,creative,Wien
+das loft,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft,1 star,modern cuisine,Wien
+botrini's,2019,Athina,Greece,other european,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s,1 star,mediterranean,Athina
+gotham bar and grill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill,1 star,american,New York
+a‚o‚c,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac,2 stars,modern cuisine,Kobenhavn
+junoon,2019,New York,New York City,indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon,1 star,indian,New York
+r-haan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan,1 star,thai,Bangkok
+canvas,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292,1 star,innovative,Bangkok
+fäviken magasinet,2019,Jarpen,Sweden,creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet,2 stars,creative,Jarpen
+elements,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements,1 star,french contemporary,Bangkok
+ginza sushi ichi,2019,Bangkok,Thailand,japanese,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi,1 star,sushi,Bangkok
+blanca,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca,2 stars,contemporary,New York
+komi,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi,1 star,mediterranean,"Washington, D.C."
+sushi taro,2019,"Washington, D.C.",Washington DC,japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro,1 star,japanese,"Washington, D.C."
+plume,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume,1 star,european,"Washington, D.C."
+shinji (tanglin road),2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road,1 star,sushi,Singapore
+coi,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi,2 stars,contemporary,San Francisco
+jean-georges,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges,2 stars,contemporary,New York
+atera,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera,2 stars,contemporary,New York
+jungsik,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik,2 stars,korean,New York
+gastrologik,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik,2 stars,creative,Stockholm
+campton place,2019,San Francisco,California,indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place,2 stars,indian,San Francisco
+saison,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison,2 stars,californian,San Francisco
+lazy bear,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear,2 stars,contemporary,San Francisco
+californios,2019,San Francisco,California,mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios,2 stars,mexican,San Francisco
+commis,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis,2 stars,contemporary,San Francisco
+baumé,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume,2 stars,contemporary,South San Francisco
+odette,2018,Singapore,Singapore,french,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette,2 stars,french contemporary,Singapore
+fiola,2019,"Washington, D.C.",Washington DC,italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola,1 star,italian,"Washington, D.C."
+upper house,2019,Goteborg,Sweden,creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house,1 star,creative,Goteborg
+métier,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier,1 star,contemporary,"Washington, D.C."
+operakällaren,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren,1 star,classic cuisine,Stockholm
+urasawa,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa,2 stars,japanese,Los Angeles
+the inn at little washington,2019,"Washington, D.C.",Washington DC,american,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington,3 stars,american,"Washington, D.C."
+noda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda,1 star,japanese,New York
+oaxen krog,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog,2 stars,creative,Stockholm
+l'atelier de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505,2 stars,french,New York
+dialogue,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue,1 star,contemporary,Los Angeles
+gaa,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa,1 star,innovative,Bangkok
+aloë,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe,1 star,creative,Stockholm
+n/naka,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka,2 stars,contemporary,Los Angeles
+aquavit,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit,2 stars,scandinavian,New York
+sushi kimura,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura,1 star,sushi,Singapore
+smyth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth,2 stars,contemporary,Chicago
+providence,2019,Los Angeles,California,seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence,2 stars,seafood,Los Angeles
+vespertine,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine,2 stars,contemporary,Los Angeles
+oriole,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole,2 stars,contemporary,Chicago
+gabriel kreuther,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther,2 stars,contemporary,New York
+pierre,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre,2 stars,french contemporary,Hong Kong
+koks,2019,Leynar,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780,2 stars,creative,Leynar
+alain ducasse at morpheus,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus,2 stars,french contemporary,Macau
+noma,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma,2 stars,creative,Kobenhavn
+the modern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern,2 stars,contemporary,New York
+kadeau copenhagen,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen,2 stars,modern cuisine,Kobenhavn
+kashiwaya,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya,2 stars,japanese,Hong Kong
+ko,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko,2 stars,contemporary,New York
+somni,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni,2 stars,contemporary,Los Angeles
+ichimura at uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu,2 stars,japanese,New York
+marea,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea,2 stars,seafood,New York
+acadia,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia,2 stars,contemporary,Chicago
+sushi ginza onodera,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850,2 stars,japanese,Los Angeles
+agrikultur,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur,1 star,modern cuisine,Stockholm
+acquerello,2019,San Francisco,California,italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello,2 stars,italian,San Francisco
+le du,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du,1 star,thai,Bangkok
+saawaan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan,1 star,thai contemporary,Bangkok
+kitcho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho,1 star,sushi,Taipei
+logy,2019,Taipei,Taipei,other asian,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy,1 star,asian contemporary,Taipei
+ken an ho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho,1 star,japanese,Taipei
+sushi ryu,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu,1 star,sushi,Taipei
+amber,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032,2 stars,french contemporary,Hong Kong
+sushi ginza onodera,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera,2 stars,japanese,New York
+aldea,2019,New York,New York City,other european,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea,1 star,mediterranean,New York
+cut,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418,1 star,steakhouse,Los Angeles
+shunji,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji,1 star,japanese,Los Angeles
+eleven madison park,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park,3 stars,contemporary,New York
+le bernardin,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin,3 stars,seafood,New York
+per se,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se,3 stars,contemporary,New York
+masa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa,3 stars,japanese,New York
+maaemo,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo,3 stars,modern cuisine,Oslo
+chez tj,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj,1 star,contemporary,South San Francisco
+plumed horse,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse,1 star,contemporary,South San Francisco
+wakuriya,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya,1 star,japanese,San Francisco
+sushi yoshizumi,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi,1 star,japanese,San Francisco
+maum,2019,South San Francisco,California,korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum,1 star,korean,South San Francisco
+omakase,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase,1 star,japanese,San Francisco
+birdsong,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong,1 star,american,San Francisco
+in situ,2019,San Francisco,California,international cuisine,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ,1 star,international,San Francisco
+auberge du soleil,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil,1 star,californian,San Francisco
+chef's table at brooklyn fare,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare,3 stars,contemporary,New York
+la toque,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque,1 star,contemporary,San Francisco
+aubergine,2019,Monterey,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine,1 star,contemporary,Monterey
+madcap,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap,1 star,contemporary,San Francisco
+wako,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako,1 star,japanese,San Francisco
+clou,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou,1 star,modern cuisine,Kobenhavn
+gary danko,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko,1 star,contemporary,San Francisco
+keiko à nob hill,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill,1 star,fusion,San Francisco
+jū-ni,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni,1 star,japanese,San Francisco
+sons & daughters,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters,1 star,contemporary,San Francisco
+michael mina,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina,1 star,contemporary,San Francisco
+angler,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler,1 star,contemporary,San Francisco
+hashiri,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri,1 star,japanese,San Francisco
+kinjo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo,1 star,japanese,San Francisco
+kong hans kælder,2019,Kobenhavn,Denmark,french,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder,1 star,classic french,Kobenhavn
+frederikshøj,2019,Aarhus,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj,1 star,creative,Aarhus
+kenzo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo,1 star,japanese,San Francisco
+nozawa bar,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar,1 star,japanese,Los Angeles
+edvard,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard,1 star,modern cuisine,Wien
+frantzén,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen,3 stars,modern cuisine,Stockholm
+maude,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude,1 star,contemporary,Los Angeles
+mori sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi,1 star,japanese,Los Angeles
+trois mec,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec,1 star,contemporary,Los Angeles
+le comptoir,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir,1 star,californian,Los Angeles
+q sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi,1 star,japanese,Los Angeles
+shibumi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644,1 star,japanese,Los Angeles
+hayato,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato,1 star,japanese,Los Angeles
+orsa & winston,2019,Los Angeles,California,other asian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston,1 star,fusion,Los Angeles
+hana re,2019,Costa Mesa,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re,1 star,japanese,Costa Mesa
+addison,2019,San Diego,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison,1 star,contemporary,San Diego
+harbor house,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house,1 star,californian,San Francisco
+goosefoot,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot,1 star,contemporary,Chicago
+el ideas,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas,1 star,contemporary,Chicago
+schwa,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa,1 star,contemporary,Chicago
+la yeon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon,3 stars,korean,Seoul
+temporis,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis,1 star,contemporary,Chicago
+everest,2019,Chicago,Chicago,french,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest,1 star,french,Chicago
+topolobampo,2019,Chicago,Chicago,mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo,1 star,mexican,Chicago
+spiaggia,2019,Chicago,Chicago,italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia,1 star,italian,Chicago
+robuchon au dôme,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome,3 stars,french contemporary,Macau
+l'atelier de joël robuchon,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon,3 stars,french contemporary,Hong Kong
+8½ otto e mezzo - bombana,2019,Hong Kong,Hong Kong,italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana,3 stars,italian,Hong Kong
+caprice,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice,3 stars,french,Hong Kong
+lung king heen,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen,3 stars,cantonese,Hong Kong
+gotgan,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan,1 star,korean,Seoul
+slotskøkkenet,2019,Horve,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet,1 star,creative,Horve
+elizabeth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth,1 star,contemporary,Chicago
+madrona manor,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor,1 star,contemporary,San Francisco
+the kitchen,2019,Sacramento,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371,1 star,contemporary,Sacramento
+farmhouse inn & restaurant,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant,1 star,californian,San Francisco
+elske,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske,1 star,contemporary,Chicago
+senses,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses,1 star,modern cuisine,Warszawa
+aniar,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar,1 star,creative,Gaillimh Galway
+loam,2019,Gaillimh Galway,Ireland,creative,,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam,1 star,creative,Gaillimh Galway
+wild honey inn,2019,Lios Duin Bhearna Lisdoonvarna,Ireland,international cuisine,,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn,1 star,classic cuisine,Lios Duin Bhearna Lisdoonvarna
+chestnut,2019,Ballydehob,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut,1 star,modern cuisine,Ballydehob
+mews,2019,Baltimore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews,1 star,modern cuisine,Baltimore
+ichigo ichie,2019,Corcaigh Cork,Ireland,japanese,,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie,1 star,japanese,Corcaigh Cork
+chapter one,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one,1 star,modern cuisine,City Centre
+greenhouse,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse,1 star,modern cuisine,City Centre
+l'ecrivain,2019,City Centre,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain,1 star,modern cuisine,City Centre
+campagne,2019,Cill Chainnigh Kilkenny,Ireland,british,,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne,1 star,modern british,Cill Chainnigh Kilkenny
+heron & grey,2019,Blackrock,Ireland,international cuisine,,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey,1 star,modern cuisine,Blackrock
+lady helen,2019,Baile Mhic Andain Thomastown,Ireland,international cuisine,,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen,1 star,modern cuisine,Baile Mhic Andain Thomastown
+house,2019,Aird Mhor Ardmore,Ireland,international cuisine,,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house,1 star,modern cuisine,Aird Mhor Ardmore
+loch bay,2019,Waternish,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay,1 star,modern cuisine,Waternish
+braidwoods,2019,Dalry,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods,1 star,classic cuisine,Dalry
+eipic,2019,Belfast,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic,1 star,modern cuisine,Belfast
+ox,2019,Belfast,United Kingdom,british,,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109,1 star,modern british,Belfast
+the peat inn,2019,Peat Inn,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn,1 star,classic cuisine,Peat Inn
+kitchin,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin,1 star,modern cuisine,Leith
+martin wishart,2019,Leith,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart,1 star,modern cuisine,Leith
+number one,2019,Edinburgh,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one,1 star,modern cuisine,Edinburgh
+21212,2019,Edinburgh,United Kingdom,creative,,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212,1 star,creative,Edinburgh
+the cellar,2019,Anstruther,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar,1 star,modern cuisine,Anstruther
+forest side,2019,Grasmere,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side,1 star,modern british,Grasmere
+hrishi,2019,Bowness On Windermere,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi,1 star,modern cuisine,Bowness On Windermere
+rogan & co,2019,Cartmel,United Kingdom,british,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co,1 star,creative british,Cartmel
+house of tides,2019,Newcastle Upon Tyne,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides,1 star,modern cuisine,Newcastle Upon Tyne
+sosban & the old butchers,2019,Menai Bridge Porthaethwy,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers,1 star,modern cuisine,Menai Bridge Porthaethwy
+northcote,2019,Langho,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote,1 star,modern british,Langho
+yorke arms,2019,Pateley Bridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms,1 star,modern cuisine,Pateley Bridge
+fraiche,2019,Birkenhead,United Kingdom,creative,,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche,1 star,creative,Birkenhead
+white swan,2019,Fence,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan,1 star,modern british,Fence
+tyddyn llan,2019,Llandrillo,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan,1 star,classic cuisine,Llandrillo
+ynyshir,2019,Machynlleth,United Kingdom,creative,,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir,1 star,creative,Machynlleth
+black swan,2019,Oldstead,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan,1 star,modern british,Oldstead
+simon radley at chester grosvenor,2019,Chester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor,1 star,modern cuisine,Chester
+star inn at harome,2019,Harome,United Kingdom,british,,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome,1 star,modern british,Harome
+the man behind the curtain,2019,Leeds,United Kingdom,creative,,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain,1 star,creative,Leeds
+the checkers,2019,Montgomery Trefaldwyn,United Kingdom,french,,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers,1 star,french,Montgomery Trefaldwyn
+pipe and glass,2019,South Dalton,United Kingdom,british,,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass,1 star,modern british,South Dalton
+fischer's at baslow hall,2019,Baslow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall,1 star,modern cuisine,Baslow
+winteringham fields,2019,Winteringham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields,1 star,modern cuisine,Winteringham
+thomas carr @ the olive room,2019,Ilfracombe,United Kingdom,seafood,,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room,1 star,seafood,Ilfracombe
+walnut tree,2019,Llanddewi Skirrid,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree,1 star,modern british,Llanddewi Skirrid
+paul ainsworth at no.6,2019,Padstow,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6,1 star,modern cuisine,Padstow
+simpsons,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons,1 star,modern cuisine,Birmingham
+purnell's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s,1 star,modern cuisine,Birmingham
+adam's,2019,Birmingham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s,1 star,modern cuisine,Birmingham
+outlaw's fish kitchen,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen,1 star,seafood,Port Isaac
+carters of moseley,2019,Birmingham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley,1 star,modern british,Birmingham
+peel's,2019,Hampton In Arden,United Kingdom,british,,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s,1 star,creative british,Hampton In Arden
+john's house,2019,Mountsorrel,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house,1 star,modern cuisine,Mountsorrel
+the whitebrook,2019,Whitebrook,United Kingdom,british,,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook,1 star,modern british,Whitebrook
+james sommerin,2019,Penarth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin,1 star,modern cuisine,Penarth
+driftwood,2019,Portscatho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood,1 star,modern cuisine,Portscatho
+the cross at kenilworth,2019,Kenilworth,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth,1 star,classic cuisine,Kenilworth
+masons arms,2019,Knowstone,United Kingdom,french,,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms,1 star,classic french,Knowstone
+salt,2019,Stratford Upon Avon,United Kingdom,british,,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869,1 star,modern british,Stratford Upon Avon
+le champignon sauvage,2019,Cheltenham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage,1 star,modern cuisine,Cheltenham
+gidleigh park,2019,Chagford,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park,1 star,modern cuisine,Chagford
+hambleton hall,2019,Upper Hambleton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall,1 star,classic cuisine,Upper Hambleton
+wilks,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks,1 star,modern british,Bristol
+bulrush,2019,Bristol,United Kingdom,british,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush,1 star,modern british,Bristol
+casamia,2019,Bristol,United Kingdom,creative,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia,1 star,creative,Bristol
+paco tapas,2019,Bristol,United Kingdom,spanish,,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas,1 star,spanish,Bristol
+pony & trap,2019,Chew Magna,United Kingdom,british,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap,1 star,modern british,Chew Magna
+the dining room,2019,Malmesbury,United Kingdom,other asian,,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454,1 star,asian influences,Malmesbury
+bybrook,2019,Castle Combe,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook,1 star,modern british,Castle Combe
+restaurant hywel jones by lucknam park,2019,Colerne,United Kingdom,british,,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park,1 star,modern british,Colerne
+olive tree,2019,Bath,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree,1 star,modern cuisine,Bath
+lympstone manor,2019,Lympstone,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor,1 star,modern cuisine,Lympstone
+the neptune,2019,Hunstanton,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune,1 star,modern cuisine,Hunstanton
+elephant,2019,Torquay,United Kingdom,british,,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant,1 star,modern british,Torquay
+oxford kitchen,2019,Oxford,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen,1 star,modern british,Oxford
+nut tree,2019,Murcott,United Kingdom,british,,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree,1 star,traditional british,Murcott
+morston hall,2019,Morston,United Kingdom,british,,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall,1 star,modern british,Morston
+red lion freehouse,2019,East Chisenbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse,1 star,classic cuisine,East Chisenbury
+blackbird,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018,1 star,classic cuisine,Newbury
+woodspeen,2019,Newbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen,1 star,modern cuisine,Newbury
+the coach,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach,1 star,modern british,Marlow
+crown,2019,Burchett'S Green,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549,1 star,regional cuisine,Burchett'S Green
+l'ortolan,2019,Shinfield,United Kingdom,french,,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan,1 star,french,Shinfield
+hinds head,2019,Bray,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head,1 star,traditional british,Bray
+black rat,2019,Winchester,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat,1 star,modern cuisine,Winchester
+matt worswick at the latymer,2019,Bagshot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer,1 star,modern cuisine,Bagshot
+coworth park,2019,Ascot,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park,1 star,modern cuisine,Ascot
+tudor room,2019,Egham,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room,1 star,modern cuisine,Egham
+the glasshouse,2019,Kew,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse,1 star,modern cuisine,Kew
+la trompette,2019,Chiswick,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette,1 star,modern british,Chiswick
+tim allen's flitch of bacon,2019,Little Dunmow,United Kingdom,british,,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon,1 star,modern british,Little Dunmow
+river café,2019,Hammersmith,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe,1 star,italian,Hammersmith
+kitchen w8,2019,Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8,1 star,modern cuisine,Kensington
+trishna,2019,Marylebone,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna,1 star,indian,Marylebone
+roganic,2019,Marylebone,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic,1 star,creative british,Marylebone
+locanda locatelli,2019,Marylebone,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli,1 star,italian,Marylebone
+texture,2019,Marylebone,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture,1 star,creative,Marylebone
+portland,2019,Regent'S Park,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland,1 star,modern cuisine,Regent'S Park
+harwood arms,2019,Fulham,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms,1 star,modern british,Fulham
+pied à terre,2019,Bloomsbury,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre,1 star,creative,Bloomsbury
+the ninth,2019,Bloomsbury,United Kingdom,other european,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth,1 star,mediterranean cuisine,Bloomsbury
+kai,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638,1 star,chinese,Mayfair
+pollen street social,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social,1 star,creative,Mayfair
+hakkasan mayfair,2019,Mayfair,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair,1 star,chinese,Mayfair
+the square,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391,1 star,creative french,Mayfair
+alyn williams at the westbury,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury,1 star,modern cuisine,Mayfair
+benares,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares,1 star,indian,Mayfair
+hakkasan hanway place,2019,Bloomsbury,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place,1 star,chinese,Bloomsbury
+social eating house,2019,Soho,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house,1 star,modern cuisine,Soho
+galvin at windows,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows,1 star,modern cuisine,Mayfair
+murano,2019,Mayfair,United Kingdom,italian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano,1 star,italian,Mayfair
+marcus,2019,Belgravia,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus,1 star,modern cuisine,Belgravia
+sabor,2019,Mayfair,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor,1 star,spanish,Mayfair
+clock house,2019,Ripley,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house,1 star,modern cuisine,Ripley
+yauatcha soho,2019,Soho,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho,1 star,chinese,Soho
+céleste,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste,1 star,creative french,Belgravia
+gymkhana,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana,1 star,indian,Mayfair
+amaya,2019,Belgravia,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya,1 star,indian,Belgravia
+pétrus,2019,Belgravia,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688,1 star,french,Belgravia
+veeraswamy,2019,Mayfair,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy,1 star,indian,Mayfair
+barrafina,2019,Soho,United Kingdom,spanish,,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina,1 star,spanish,Soho
+hide,2019,Mayfair,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide,1 star,modern british,Mayfair
+ritz restaurant,2019,Westminster,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant,1 star,modern british,Westminster
+elystan street,2019,Chelsea,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street,1 star,modern british,Chelsea
+seven park place,2019,Saint James'S,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place,1 star,modern cuisine,Saint James'S
+ikoyi,2019,Saint James'S,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi,1 star,creative,Saint James'S
+aquavit,2019,Saint James'S,United Kingdom,scandinavian,,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082,1 star,scandinavian,Saint James'S
+five fields,2019,Chelsea,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields,1 star,modern cuisine,Chelsea
+dining room at the goring,2019,Victoria,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring,1 star,traditional british,Victoria
+st john,2019,Clerkenwell,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john,1 star,traditional british,Clerkenwell
+quilon,2019,Victoria,United Kingdom,indian,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon,1 star,indian,Victoria
+club gascon,2019,London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon,1 star,french,London
+a. wong,2019,Victoria,United Kingdom,chinese,,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong,1 star,chinese,Victoria
+the clove club,2019,Shoreditch,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club,1 star,modern cuisine,Shoreditch
+leroy,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy,1 star,modern british,Shoreditch
+angler,2019,Finsbury,United Kingdom,seafood,,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235,1 star,seafood,Finsbury
+brat,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat,1 star,traditional british,Shoreditch
+lyle's,2019,Shoreditch,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s,1 star,modern british,Shoreditch
+galvin la chapelle,2019,Spitalfields,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle,1 star,french,Spitalfields
+city social,2019,City Of London,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social,1 star,modern cuisine,City Of London
+la dame de pic,2019,City Of London,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic,1 star,modern french,City Of London
+story,2019,Bermondsey,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story,1 star,modern cuisine,Bermondsey
+trinity,2019,Clapham Common,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity,1 star,modern cuisine,Clapham Common
+chez bruce,2019,Wandsworth,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce,1 star,french,Wandsworth
+sorrel,2019,Dorking,United Kingdom,british,,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459,1 star,modern british,Dorking
+restaurant tristan,2019,Horsham,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan,1 star,modern british,Horsham
+gravetye manor,2019,Gravetye,United Kingdom,british,,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor,1 star,modern british,Gravetye
+the sportsman,2019,Seasalter,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman,1 star,modern british,Seasalter
+west house,2019,Biddenden,United Kingdom,british,,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house,1 star,modern british,Biddenden
+fordwich arms,2019,Fordwich,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms,1 star,modern cuisine,Fordwich
+samphire,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987,1 star,modern cuisine,Saint Helier Saint Helier
+bohemia,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia,1 star,modern cuisine,Saint Helier Saint Helier
+patrick guilbaud,2019,City Centre,Ireland,french,,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud,2 stars,modern french,City Centre
+andrew fairlie at gleneagles,2019,Auchterarder,United Kingdom,french,,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles,2 stars,creative french,Auchterarder
+l'enclume,2019,Cartmel,United Kingdom,creative,,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume,2 stars,creative,Cartmel
+raby hunt,2019,Summerhouse,United Kingdom,british,,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt,2 stars,modern british,Summerhouse
+moor hall,2019,Aughton,United Kingdom,british,,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall,2 stars,modern british,Aughton
+restaurant sat bains,2019,Nottingham,United Kingdom,creative,,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains,2 stars,creative,Nottingham
+restaurant nathan outlaw,2019,Port Isaac,United Kingdom,seafood,,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw,2 stars,seafood,Port Isaac
+belmond le manoir aux quat' saisons,2019,Great Milton,United Kingdom,french,,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons,2 stars,french,Great Milton
+midsummer house,2019,Cambridge,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house,2 stars,modern cuisine,Cambridge
+hand and flowers,2019,Marlow,United Kingdom,british,,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers,2 stars,modern british,Marlow
+ledbury,2019,North Kensington,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury,2 stars,modern cuisine,North Kensington
+core by clare smyth,2019,North Kensington,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth,2 stars,modern british,North Kensington
+le gavroche,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche,2 stars,french,Mayfair
+kitchen table at bubbledogs,2019,Bloomsbury,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs,2 stars,modern cuisine,Bloomsbury
+hélène darroze at the connaught,2019,Mayfair,United Kingdom,international cuisine,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught,2 stars,modern cuisine,Mayfair
+dinner by heston blumenthal,2019,Hyde Park,United Kingdom,british,,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal,2 stars,traditional british,Hyde Park
+umu,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu,2 stars,japanese,Mayfair
+sketch (the lecture room & library),2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library,2 stars,modern french,Mayfair
+greenhouse,2019,Mayfair,United Kingdom,creative,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393,2 stars,creative,Mayfair
+claude bosi at bibendum,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum,2 stars,french,Chelsea
+fat duck,2019,Bray,United Kingdom,creative,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck,3 stars,creative,Bray
+waterside inn,2019,Bray,United Kingdom,french,,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn,3 stars,classic french,Bray
+alain ducasse at the dorchester,2019,Mayfair,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester,3 stars,french,Mayfair
+the araki,2019,Mayfair,United Kingdom,japanese,,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki,3 stars,japanese,Mayfair
+gordon ramsay,2019,Chelsea,United Kingdom,french,,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay,3 stars,french,Chelsea
diff --git a/data/clean/P.ipynb b/data/clean/P.ipynb
new file mode 100644
index 00000000..dd92b6c3
--- /dev/null
+++ b/data/clean/P.ipynb
@@ -0,0 +1,740 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv(r'C:\\Users\\quint\\Desktop\\IronHack\\WEEKS\\WEEK4\\project\\one-star-michelin-restaurants.csv')\n",
+    "star2_df = pd.read_csv(r'C:\\Users\\quint\\Desktop\\IronHack\\WEEKS\\WEEK4\\project\\two-stars-michelin-restaurants.csv')\n",
+    "star3_df = pd.read_csv(r'C:\\Users\\quint\\Desktop\\IronHack\\WEEKS\\WEEK4\\project\\three-stars-michelin-restaurants.csv')\n",
+    "\n",
+    "star3_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f79ac46f-854b-4337-9f7a-79f506585548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5b3ef80-0c45-4c07-99aa-748d31021f4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84c801f9-7317-4c26-8501-821fa7fd6d8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b20ff0d1-1591-4915-8aa0-71daa52f795e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop unwanted columns\n",
+    "star1_df.drop(columns=['zipCode'], inplace=True)\n",
+    "star1_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35362075-61d9-456e-9d43-516305831f5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['zipCode'], inplace=True)\n",
+    "star2_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a4947e7-fe2d-4547-8a87-4fda6f2177f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['zipCode'], inplace=True)\n",
+    "star3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc8dc856-5819-4356-8d5c-cd74d6913b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a12f0db9-0107-4d06-bfa3-070354d6add0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e64ffc4-75c4-4870-9385-5688ceb027e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e4047ce-07a6-49e8-a80d-2e04670bda00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['year'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.drop(columns=['latitude', 'longitude'], inplace=True)\n",
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce87746b-6e4b-41ad-a858-99c38a990e91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#put in lower for joining for example 'creative' with 'Creative'\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].str.strip().str.lower() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2214de4c-6daa-4057-a268-65b0a589eede",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "international_types = [\"modern cuisine\", \"classic cuisine\",\n",
+    "                       \"street food\", \"meats and grills\", \"international\", \"innovative\"]\n",
+    "\n",
+    "stars_df[\"cuisine_original\"] = stars_df[\"cuisine\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(\n",
+    "    international_types, \"international cuisine\")\n",
+    "\n",
+    "international_sub = stars_df[\n",
+    "    stars_df[\"cuisine_original\"].isin(international_types)]\n",
+    "\n",
+    "ax = international_sub[\"cuisine_original\"].value_counts().plot(kind=\"bar\")\n",
+    "\n",
+    "plt.title(\"Distribution of subtypes within international cuisine\")\n",
+    "plt.ylabel(\"Number of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "note = (\"Modern cuisine combines global flavours with local and seasonal ingredients, \"\"while innovative refers to more experimental concepts such as molecular \"\"gastronomy or 3D‑printed food.\")\n",
+    "\n",
+    "plt.subplots_adjust(bottom=0.3)\n",
+    "\n",
+    "plt.figtext(\n",
+    "    0.5,\n",
+    "    0.02,\n",
+    "    note,\n",
+    "    ha=\"center\",\n",
+    "    va=\"bottom\",\n",
+    "    wrap=True,\n",
+    "    fontsize=9\n",
+    ")\n",
+    "\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53a35045-f5f6-4110-b174-5f88bd9093ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70648bad-2bfc-4a30-8721-5d31abc18443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute, in this case \"Chinese food\" \n",
+    "chinese_type = [\"chinese\", \n",
+    "    \"cantonese\",\n",
+    "    \"cantonese roast meats\",\n",
+    "    \"dim sum\",\n",
+    "    \"shanghainese\",\n",
+    "    \"sichuan\",\n",
+    "    \"hunanese and sichuan\",\n",
+    "    \"sichuan-huai yang\",\n",
+    "    \"fujian\",\n",
+    "    \"taizhou\",\n",
+    "    \"hang zhou\",\n",
+    "    \"noodles and congee\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(chinese_type, \"chinese\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18eff245-8e00-40d4-a6cb-4215aea72c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01836bf8-034e-4cb5-aa25-679c373a6155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute\n",
+    "korean_types = ['korean',\n",
+    "    'korean contemporary',\n",
+    "    'temple cuisine']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33116181-30c8-4b33-a28a-5f770935da02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e33b6a97-f1f2-4cfa-bc73-3065516a4100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thai_types = ['thai',\n",
+    "    'thai contemporary',\n",
+    "    'southern thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a198e63-0358-4d15-91e3-3ab25070f5c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f39652c-5412-47c0-b0bc-4f1e8ca36a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "american_types = ['american',\n",
+    "    'californian',\n",
+    "    'barbecue',\n",
+    "    'steakhouse']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d1d5eca-2061-44dd-8a89-513fa6d5116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f0bf7e-e580-4c53-85b5-112004625e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "french_types = ['french',\n",
+    "    'classic french',\n",
+    "    'french contemporary',\n",
+    "    'modern french',\n",
+    "    'creative french']\n",
+    "\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d995f47c-7f09-4a1d-b8b0-af2639174996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90196101-a614-4d9e-903d-0ddd2136bc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "japanese_types = ['japanese',\n",
+    "    'sushi',\n",
+    "    'teppanyaki',\n",
+    "    'japanese contemporary']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5d63dac-f061-4e69-af34-1913465539ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7c979b4-e012-4a6e-a95f-cd9a32b31f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_asian_types = ['asian',\n",
+    "    'asian influences',\n",
+    "    'asian contemporary',\n",
+    "    'fusion','taiwanese','peranakan','thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65715bf6-d434-4c3a-8e7b-e3666faf90a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "197594f3-4e30-4614-826b-050ca5de00d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "british_types = ['modern british',\n",
+    "    'traditional british',\n",
+    "    'creative british']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "112b202f-5d66-43f9-a999-62856ed29d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0d2074a-bf80-4057-90b5-e035eb5b4a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_types = ['modern cuisine',\n",
+    "    'modern','modern food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8270bdaf-1ae0-469f-be7d-9197ce9b5e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75ce0e4a-0c0f-40d9-bb71-384562cfbdef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca6b75d-eedc-434e-891d-5d171615f124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market_types = ['classic cuisine','market cuisine', 'regional cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "461da05b-22f3-4242-ade1-69072bf55cf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1b5284a-59cf-4aaa-8c44-1b21594cec3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mediterranean_types = ['mediterranean', 'mediterranean cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fbc1139-5470-4594-8382-53236b9e62c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42b900bb-af39-442f-94d2-abcab9710dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_european_types = ['european', 'european contemporary','mediterranean food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec17fcff-6605-477c-a4e6-90b92835266e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88adeee8-713c-4ce2-8100-7754993de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "italian_types = ['italian', 'italian contemporary']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa1fc4be-87bd-4a6f-8cae-c95cc96b2598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56b5bfdd-81f6-4b3f-865c-eff95f23a7a7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b09fd01-fbfc-48f6-98d2-57c2f5bf1b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e540377f-e185-48a3-b3a4-24b480f13ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "868eea3a-d9d9-49ff-a24d-2ae6e36a8e7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scandinavian_types = ['danish','finnish', 'scandinavian']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a2e925-3ccb-4339-89f4-fdeb960ea0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34edec4d-5578-4197-bba9-65cb44ebf311",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36c87b83-e415-4c4c-98c2-aca93f131ef7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0183cb6b-ccfc-4d21-9dcc-ce3e468d1a29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"cuisine\"].value_counts().head(10).plot(kind=\"bar\")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Cuisine\")\n",
+    "plt.ylabel(\"Nº of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caba2d12-dfb8-4550-b736-9930d18fec46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_cuisines = (\n",
+    "    stars_df[\"cuisine\"].value_counts()\n",
+    "    .head(10)\n",
+    "    .index\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.countplot(\n",
+    "    data=stars_df[stars_df[\"cuisine\"].isin(top_cuisines)],\n",
+    "    y=\"cuisine\",\n",
+    "    order=top_cuisines\n",
+    ")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Nº of restaurants\")\n",
+    "plt.ylabel(\"Cuisine\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25a21954-71f6-4ac6-8cf0-2eded24af2a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"stars_n\"] = stars_df[\"stars\"].str[0].astype(int)\n",
+    "\n",
+    "stars_df.groupby(\"region\")[\"stars_n\"].mean().sort_values().plot(kind=\"bar\")\n",
+    "plt.title(\"Mean of Stars per region\")\n",
+    "plt.xlabel(\"Region\")\n",
+    "plt.ylabel(\"Mean of stars\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72feb6f3-9574-4323-8053-f98e76e5e00d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f820ed6-8646-49a9-81db-1dc72d9d7907",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00f80cc0-c718-4357-9961-887437666d2e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86cab2a8-aaba-4ecf-bbbb-e15ac9bcbc61",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9da6188-66a9-4201-a7b4-9a58ee3a9da3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/clean/Z.ipynb b/data/clean/Z.ipynb
new file mode 100644
index 00000000..9022e9d0
--- /dev/null
+++ b/data/clean/Z.ipynb
@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "star1_df = pd.read_csv(\"/Users/ZINA/Desktop/one-star-michelin-restaurants.csv\")\n",
+    "star2_df = pd.read_csv(\"/Users/ZINA/Desktop/two-stars-michelin-restaurants.csv\")\n",
+    "star3_df = pd.read_csv(\"/Users/ZINA/Desktop/three-stars-michelin-restaurants.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f79ac46f-854b-4337-9f7a-79f506585548",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5b3ef80-0c45-4c07-99aa-748d31021f4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84c801f9-7317-4c26-8501-821fa7fd6d8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b20ff0d1-1591-4915-8aa0-71daa52f795e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop unwanted columns\n",
+    "star1_df.drop(columns=['url', 'zipCode'], inplace=True)\n",
+    "star1_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35362075-61d9-456e-9d43-516305831f5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['url', 'zipCode'], inplace=True)\n",
+    "star2_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a4947e7-fe2d-4547-8a87-4fda6f2177f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['url', 'zipCode'], inplace=True)\n",
+    "star3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc8dc856-5819-4356-8d5c-cd74d6913b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a12f0db9-0107-4d06-bfa3-070354d6add0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e64ffc4-75c4-4870-9385-5688ceb027e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e4047ce-07a6-49e8-a80d-2e04670bda00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['year'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8262adb-1282-45d2-a5a1-e17443e58da2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83c8984f-ca39-4346-b5dc-3c40fee6c3a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4c84a80-9cbd-42d7-81f9-533644e8b2ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e790f1a7-b12b-4562-a18f-28c59911999a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44c9b8b5-3c2c-4213-aba9-9c16de5784d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace $$$$$ with $$$$\n",
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef06af31-c018-426b-9988-0e6125abdbd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean weird characters\n",
+    "stars_df['price'] = stars_df['price'].str.strip()\n",
+    "stars_df['price'] = stars_df['price'].str.replace(r'\\s+', '', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d11d2b9-2ed3-4812-b5a7-bdcf015a9ba5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert $ to ordinal numbers\n",
+    "stars_df['price_ordinal'] = stars_df['price'].str.count(r'\\$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5c1416d-1ae6-43d7-b29b-2d93ec576850",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute median ordinal per star group (1 star, 2 stars, 3 stars)\n",
+    "median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c2e6600-b487-47a0-b6e3-6cb7ef99460a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace missing ordinal values using matching star median\n",
+    "stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4bdb2fd-7053-48db-a64c-f341989529c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now convert back to $ string after filling\n",
+    "stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b921567-bb20-4ff1-891b-29d0e7cedfd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping\n",
+    "price_mean_map = {\n",
+    "    \"$\": 20,\n",
+    "    \"$$\": 37.5,\n",
+    "    \"$$$\": 62.5,\n",
+    "    \"$$$$\": 100\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8969323e-7c4d-4086-a2c7-faab2650fac7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new column with the mean price\n",
+    "stars_df['price_mean'] = stars_df['price'].map(price_mean_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cc8e08c-e1e1-4f64-a0bc-4d1c4df26739",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7fada31-307b-4c3b-89f6-512a4e60815d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b77f557-1a90-4034-af7f-edd7b8b00af4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', None)\n",
+    "print(stars_df[\"price_mean\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19837b97-3af2-4ad8-9db7-01299c3a50a7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/clean/anaconda_projects/db/project_filebrowser.db b/data/clean/anaconda_projects/db/project_filebrowser.db
new file mode 100644
index 00000000..e5ae8f7b
Binary files /dev/null and b/data/clean/anaconda_projects/db/project_filebrowser.db differ
diff --git a/data/clean/clean.ipynb b/data/clean/clean.ipynb
new file mode 100644
index 00000000..885b7ebb
--- /dev/null
+++ b/data/clean/clean.ipynb
@@ -0,0 +1,1297 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv(r'C:\\Users\\quint\\Desktop\\IronHack\\WEEKS\\WEEK4\\project\\one-star-michelin-restaurants.csv')\n",
+    "star2_df = pd.read_csv(r'C:\\Users\\quint\\Desktop\\IronHack\\WEEKS\\WEEK4\\project\\two-stars-michelin-restaurants.csv')\n",
+    "star3_df = pd.read_csv(r'C:\\Users\\quint\\Desktop\\IronHack\\WEEKS\\WEEK4\\project\\three-stars-michelin-restaurants.csv')\n",
+    "\n",
+    "star3_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "048ad2db-8a41-41d0-ae6f-1ba835e759e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9efe4bc4-3928-408c-94db-fca59c39b663",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6ea2440-dbc3-4c55-98b7-051971568e95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0121673d-a647-47f2-aa55-dbadc7a55770",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05a1e81e-9f06-4f36-9c56-6ae3c67b99aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81ed15db-76c5-483a-af4a-2267b98d8706",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f4d82fe-4535-4809-ad91-e353fafa0891",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['year'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58b747be-c49f-4cba-b7f3-07430f081cc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cbb7964-664b-44e7-bc5b-10dd75c6920a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dff18402-85ec-49f0-9399-47465737ac83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fdf3937-33ed-4c60-8950-b8370de559bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec4465cb-7e32-4c70-a6b5-6f6179a31f21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace $$$$$ with $$$$\n",
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dfe74f4-f6fa-4a9f-8b17-45f2e93034ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff99db1d-7bb4-40ee-b8fb-70a4f192e228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean weird characters\n",
+    "stars_df['price'] = stars_df['price'].str.strip()\n",
+    "stars_df['price'] = stars_df['price'].str.replace(r'\\s+', '', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc94f5b-6f23-4f4d-86df-cc6a53b36e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert $ to ordinal numbers\n",
+    "stars_df['price_ordinal'] = stars_df['price'].str.count(r'\\$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5354f300-4299-46bd-a61e-ab56f935e334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute median ordinal per star group (1 star, 2 stars, 3 stars)\n",
+    "median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49518bf2-151e-48b3-8e8e-d395851dc981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace missing ordinal values using matching star median\n",
+    "stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8afb6f3-10d9-4775-82a8-f49852a2766c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now convert back to $ string after filling\n",
+    "stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5a1e8ae-c0fe-4281-9755-32a87caa105d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping\n",
+    "price_mean_map = {\n",
+    "    \"$\": 20,\n",
+    "    \"$$\": 37.5,\n",
+    "    \"$$$\": 62.5,\n",
+    "    \"$$$$\": 100\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d22c338b-3d05-4de2-b79f-34d13b6b3746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new column with the mean price\n",
+    "stars_df['price_mean'] = stars_df['price'].map(price_mean_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8753564-1782-4e5b-ab2a-c6a59d59a9d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc4cf519-6e7b-4627-b4cb-9f635bbf29b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "756447a2-2dfc-43af-ba50-16577471e50c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.set_option('display.max_rows', None)\n",
+    "print(stars_df[\"price_mean\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b20ff0d1-1591-4915-8aa0-71daa52f795e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop unwanted columns\n",
+    "star1_df.drop(columns=['zipCode'], inplace=True)\n",
+    "star1_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35362075-61d9-456e-9d43-516305831f5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['zipCode'], inplace=True)\n",
+    "star2_df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a4947e7-fe2d-4547-8a87-4fda6f2177f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['zipCode'], inplace=True)\n",
+    "star3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc8dc856-5819-4356-8d5c-cd74d6913b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a12f0db9-0107-4d06-bfa3-070354d6add0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e64ffc4-75c4-4870-9385-5688ceb027e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e4047ce-07a6-49e8-a80d-2e04670bda00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['year'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.drop(columns=['latitude', 'longitude'], inplace=True)\n",
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce87746b-6e4b-41ad-a858-99c38a990e91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#put in lower for joining for example 'creative' with 'Creative'\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].str.strip().str.lower() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53a35045-f5f6-4110-b174-5f88bd9093ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70648bad-2bfc-4a30-8721-5d31abc18443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute, in this case \"Chinese food\" \n",
+    "chinese_type = [\"chinese\", \n",
+    "    \"cantonese\",\n",
+    "    \"cantonese roast meats\",\n",
+    "    \"dim sum\",\n",
+    "    \"shanghainese\",\n",
+    "    \"sichuan\",\n",
+    "    \"hunanese and sichuan\",\n",
+    "    \"sichuan-huai yang\",\n",
+    "    \"fujian\",\n",
+    "    \"taizhou\",\n",
+    "    \"hang zhou\",\n",
+    "    \"noodles and congee\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(chinese_type, \"chinese\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18eff245-8e00-40d4-a6cb-4215aea72c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01836bf8-034e-4cb5-aa25-679c373a6155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute\n",
+    "korean_types = ['korean',\n",
+    "    'korean contemporary',\n",
+    "    'temple cuisine']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33116181-30c8-4b33-a28a-5f770935da02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e33b6a97-f1f2-4cfa-bc73-3065516a4100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thai_types = ['thai',\n",
+    "    'thai contemporary',\n",
+    "    'southern thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a198e63-0358-4d15-91e3-3ab25070f5c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f39652c-5412-47c0-b0bc-4f1e8ca36a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "american_types = ['american',\n",
+    "    'californian',\n",
+    "    'barbecue',\n",
+    "    'steakhouse']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d1d5eca-2061-44dd-8a89-513fa6d5116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f0bf7e-e580-4c53-85b5-112004625e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "french_types = ['french',\n",
+    "    'classic french',\n",
+    "    'french contemporary',\n",
+    "    'modern french',\n",
+    "    'creative french']\n",
+    "\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d995f47c-7f09-4a1d-b8b0-af2639174996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90196101-a614-4d9e-903d-0ddd2136bc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "japanese_types = ['japanese',\n",
+    "    'sushi',\n",
+    "    'teppanyaki',\n",
+    "    'japanese contemporary']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5d63dac-f061-4e69-af34-1913465539ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7c979b4-e012-4a6e-a95f-cd9a32b31f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_asian_types = ['asian',\n",
+    "    'asian influences',\n",
+    "    'asian contemporary',\n",
+    "    'fusion','taiwanese','peranakan','thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65715bf6-d434-4c3a-8e7b-e3666faf90a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "197594f3-4e30-4614-826b-050ca5de00d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "british_types = ['modern british',\n",
+    "    'traditional british',\n",
+    "    'creative british']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "112b202f-5d66-43f9-a999-62856ed29d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0d2074a-bf80-4057-90b5-e035eb5b4a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_types = ['modern cuisine',\n",
+    "    'modern','modern food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8270bdaf-1ae0-469f-be7d-9197ce9b5e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75ce0e4a-0c0f-40d9-bb71-384562cfbdef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca6b75d-eedc-434e-891d-5d171615f124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market_types = ['classic cuisine','market cuisine', 'regional cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "461da05b-22f3-4242-ade1-69072bf55cf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1b5284a-59cf-4aaa-8c44-1b21594cec3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mediterranean_types = ['mediterranean', 'mediterranean cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fbc1139-5470-4594-8382-53236b9e62c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42b900bb-af39-442f-94d2-abcab9710dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_european_types = ['european', 'european contemporary','mediterranean food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec17fcff-6605-477c-a4e6-90b92835266e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88adeee8-713c-4ce2-8100-7754993de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "italian_types = ['italian', 'italian contemporary']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa1fc4be-87bd-4a6f-8cae-c95cc96b2598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56b5bfdd-81f6-4b3f-865c-eff95f23a7a7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b09fd01-fbfc-48f6-98d2-57c2f5bf1b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e540377f-e185-48a3-b3a4-24b480f13ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "868eea3a-d9d9-49ff-a24d-2ae6e36a8e7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scandinavian_types = ['danish','finnish', 'scandinavian']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a2e925-3ccb-4339-89f4-fdeb960ea0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34edec4d-5578-4197-bba9-65cb44ebf311",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84f3890b-2733-47b9-a271-08f9dc272006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9ff2db0-a438-4594-8851-21ad6ec96cfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['name'].nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb5c0a0b-205e-47c2-99e5-160d2e9b94a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b36c09a-8535-49df-b11d-82c5fae0875a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Restaurant name standardization - lower case\n",
+    "\n",
+    "stars_df['name']= stars_df['name'].str.lower()\n",
+    "print (stars_df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30eb95b6-64bf-4412-be57-e3db0536f906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Trim Excessive Whitespaces:\n",
+    "\n",
+    "stars_df['name'] = stars_df['name'].astype(str)\n",
+    "stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))\n",
+    "\n",
+    "print(stars_df.sample(5)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6eb2f6a-93b2-47d0-8262-10e3dde375d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# year check\n",
+    "\n",
+    "stars_df['year'].nunique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bf643cd-63d0-4762-a772-e99dffa5256a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stars_df['year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caa32e9d-be69-4ede-80a5-47c40dbbe909",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# city names check\n",
+    "stars_df['city'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e50bafb-9f38-4b8a-9575-a65bffc186d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fad5dfe-be46-45e6-9609-414aa470e0db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Remove Leading/Trailing Spaces\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.strip()\n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8e7151e-b4db-4076-835e-cd82139b24e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#convert to lower case\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.lower() \n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "654fc3c3-545b-4115-8f8b-a2514b44e138",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#check for duplicates\n",
+    "\n",
+    "duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee3f59d0-5adb-468b-86db-bd6ea87e1463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# order A–Z\n",
+    "\n",
+    "#stars_df = stars_df.sort_values(by=\"city\")  \n",
+    "#stars_df['city'].unique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a70b42b-accf-46fc-a32a-2377e25b0024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove numbers and zip codes\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def clean_city_name(city_name):\n",
+    "    if isinstance(city_name, str):  # Check if the input is a string\n",
+    "        # Use regex to remove \" - numbers\" at the end of the string\n",
+    "        return re.sub(r'\\s-\\s\\d+$', '', city_name).strip()\n",
+    "    return city_name  # Return as is if it's not a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb772e24-05ae-41ac-9d33-521afbb9068c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].apply(clean_city_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "565e3036-376f-42ae-ab1a-262ae8a046de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#verify results\n",
+    "\n",
+    "print(stars_df['city'].unique())  # Display unique city names to verify the cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ca0d239-d0d6-4bf6-a80b-52abfe117f87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove special characters \n",
+    "\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebe148d4-da9d-49bf-95cb-7dc791e9dbe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb93350d-af11-4757-b2ea-fe2ac99ca466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efa0705f-be86-4f5d-b89a-9831265b29e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#the city column are stripped of accents and are presented in ASCII format.\n",
+    "\n",
+    "import unidecode\n",
+    "stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94bb2635-47d6-47df-aa22-d0d1d1d86463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#verify the results\n",
+    "print(stars_df['city'].unique()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33b41649-7526-40c2-9a6a-9cd76700334a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grouping suburbs into major city and add info in a new column \n",
+    "\n",
+    "#create a dictionary \n",
+    "\n",
+    "location_map = {\n",
+    "    # London + neighborhoods\n",
+    "    'north kensington': 'London',\n",
+    "    'kensington': 'London',\n",
+    "    'westminster': 'London',\n",
+    "    'soho': 'London',\n",
+    "    'mayfair': 'London',\n",
+    "    'marylebone': 'London',\n",
+    "    'chelsea': 'London',\n",
+    "    'clapham common': 'London',\n",
+    "    \"regent's park\": 'London',\n",
+    "    'shoreditch': 'London',\n",
+    "    'spitalfields': 'London',\n",
+    "    'belgravia': 'London',\n",
+    "    'bloomsbury': 'London',\n",
+    "    'finsbury': 'London',\n",
+    "    'fulham': 'London',\n",
+    "    'chiswick': 'London',\n",
+    "    'city centre': 'London',\n",
+    "    'city of london': 'London',\n",
+    "    'hyde park': 'London',\n",
+    "    # San Francisco\n",
+    "    'south san francisco': 'San Francisco',\n",
+    "    # Ireland\n",
+    "    'baile mhic andáin/thomastown': 'Thomastown',\n",
+    "    'gaillimh/galway': 'Galway',\n",
+    "    'cill chainnigh/kilkenny': 'Kilkenny',\n",
+    "    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',\n",
+    "    'athína': 'Athens',\n",
+    "    'ballydehob': 'Ballydehob',\n",
+    "    # Finland\n",
+    "    'helsingfors / helsinki': 'Helsinki',\n",
+    "    # Czech Republic\n",
+    "    'praha': 'Prague',\n",
+    "    # Austria\n",
+    "    'wien': 'Vienna',\n",
+    "    'salzburg': 'Salzburg',\n",
+    "    # Menai Bridge\n",
+    "    'menai bridge/porthaethwy': 'Menai Bridge',\n",
+    "    # USA cities\n",
+    "    'los angeles': 'Los Angeles',\n",
+    "    'san diego': 'San Diego',\n",
+    "    'sacramento': 'Sacramento',\n",
+    "    'new york': 'New York',\n",
+    "    'chicago': 'Chicago',\n",
+    "    'costa mesa': 'Costa Mesa',\n",
+    "    'monterey': 'Monterey',\n",
+    "    'washington, d.c.': 'Washington D.C.',\n",
+    "    'south dalton': 'Dalton',\n",
+    "    # Asia\n",
+    "    'bangkok': 'Bangkok',\n",
+    "    'phuket': 'Phuket',\n",
+    "    'hong kong': 'Hong Kong',\n",
+    "    'taipei': 'Taipei',\n",
+    "    'seoul': 'Seoul',\n",
+    "    'singapore': 'Singapore',\n",
+    "    'macau': 'Macau',\n",
+    "    # Croatia\n",
+    "    'lovran': 'Lovran',\n",
+    "    'rovinj': 'Rovinj',\n",
+    "    'zagreb': 'Zagreb',\n",
+    "    'šibenik': 'Sibenik',\n",
+    "    # Norway / Scandinavia\n",
+    "    'stavanger': 'Stavanger',\n",
+    "    'trondheim': 'Trondheim',\n",
+    "    'oslo': 'Oslo',\n",
+    "    'göteborg': 'Gothenburg',\n",
+    "    'växjö': 'Vaxjo',\n",
+    "    'skåne-tranås': 'Skane-Tranas',\n",
+    "    'vejle': 'Vejle',\n",
+    "    # Denmark\n",
+    "    'fredericia': 'Fredericia',\n",
+    "    'pedersker': 'Pedersker',\n",
+    "    'præstø': 'Praesto',\n",
+    "    # Sweden\n",
+    "    'malmö': 'Malmo',\n",
+    "    'stockholm': 'Stockholm',\n",
+    "    # Portugal / Ireland / UK misc\n",
+    "    'bath': 'Bath',\n",
+    "    'bristol': 'Bristol',\n",
+    "    'cambridge': 'Cambridge',\n",
+    "    'cheltenham': 'Cheltenham',\n",
+    "    'chester': 'Chester',\n",
+    "    'birmingham': 'Birmingham',\n",
+    "    'edinburgh': 'Edinburgh',\n",
+    "    'leeds': 'Leeds',\n",
+    "    'oxford': 'Oxford',\n",
+    "    'stratford-upon-avon': 'Stratford-Upon-Avon',\n",
+    "    'padstow': 'Padstow',\n",
+    "    'torquay': 'Torquay',\n",
+    "    'newcastle upon tyne': 'Newcastle upon Tyne',\n",
+    "    'nottingham': 'Nottingham',\n",
+    "    'bray': 'Bray',\n",
+    "    'bowness-on-windermere': 'Bowness-on-Windermere',\n",
+    "    'cartmel': 'Cartmel',\n",
+    "    'castle combe': 'Castle Combe',\n",
+    "    'chagford': 'Chagford',\n",
+    "    'chew magna': 'Chew Magna',\n",
+    "    'dalry': 'Dalry',\n",
+    "    'dorking': 'Dorking',\n",
+    "    'egham': 'Egham',\n",
+    "    'fence': 'Fence',\n",
+    "    'fordwich': 'Fordwich',\n",
+    "    'grasmere': 'Grasmere',\n",
+    "    'gravetye': 'Gravetye',\n",
+    "    'great milton': 'Great Milton',\n",
+    "    'hallwang': 'Hallwang',\n",
+    "    'hampton in arden': 'Hampton in Arden',\n",
+    "    'harome': 'Harome',\n",
+    "    'henne': 'Henne',\n",
+    "    'horsham': 'Horsham',\n",
+    "    'hunstanton': 'Hunstanton',\n",
+    "    'ilfracombe': 'Ilfracombe',\n",
+    "    'järpen': 'Jarpen',\n",
+    "    'kenilworth': 'Kenilworth',\n",
+    "    'kew': 'Kew',\n",
+    "    'kleinwalsertal': 'Kleinwalsertal',\n",
+    "    'knowstone': 'Knowstone',\n",
+    "    'langho': 'Langho',\n",
+    "    'leith': 'Leith',\n",
+    "    'leynar': 'Leynar',\n",
+    "    'little dunmow': 'Little Dunmow',\n",
+    "    'llanddewi skirrid': 'Llanddewi Skirrid',\n",
+    "    'llandrillo': 'Llandrillo',\n",
+    "    'lovran': 'Lovran',\n",
+    "    'lympstone': 'Lympstone',\n",
+    "    'machynlleth': 'Machynlleth',\n",
+    "    'malmesbury': 'Malmesbury',\n",
+    "    'marlow': 'Marlow',\n",
+    "    'morston': 'Morston',\n",
+    "    'mountsorrel': 'Mountsorrel',\n",
+    "    'murcott': 'Murcott',\n",
+    "    'newbury': 'Newbury',\n",
+    "    'oldstead': 'Oldstead',\n",
+    "    'peat inn': 'Peat Inn',\n",
+    "    'penarth': 'Penarth',\n",
+    "    'port isaac': 'Port Isaac',\n",
+    "    'portscatho': 'Portscatho',\n",
+    "    'ripley': 'Ripley',\n",
+    "    'saint helier/saint-hélier': 'Saint Helier',\n",
+    "    \"saint james's\": 'Saint James',\n",
+    "    'seasalter': 'Seasalter',\n",
+    "    'shinfield': 'Shinfield',\n",
+    "    'summerhouse': 'Summerhouse',\n",
+    "    'upper hambleton': 'Hambleton',\n",
+    "    'victoria': 'Victoria',\n",
+    "    'wandsworth': 'London',\n",
+    "    'whitebrook': 'Whitebrook',\n",
+    "    'winchester': 'Winchester',\n",
+    "    'winteringham': 'Winteringham'\n",
+    "}\n",
+    "\n",
+    "stars_df['major_city'] = stars_df['city'].replace(location_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d936e600-5eaf-4813-a493-029cd4447b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stars_df[['city', 'major_city']].sample(10))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "781e121d-126d-491e-871d-526563dc4d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7256d411-b0f0-4aec-8e92-4888ba57806c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b27be073-e390-4f35-874b-f82d3b6f75f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3acfdf4-815b-4471-8fe9-a1eca7c437d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visuals: \n",
+    "#which are the cities with more restaurants? \n",
+    "#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?\n",
+    "# which are the cuisine dominating a city /region \n",
+    "# avg price point in a specific city based on restaurants?\n",
+    "# time series 2018 vs 2019 any trend? any star restautant grew over past year? \n",
+    "# cheapest vs most expensive cuisine? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea7a53b2-83dc-4e15-8f44-d4c8e751c7ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)\n",
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1182dbce-635c-49ac-b830-6996f42a82a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/clean/cleaned_data_file.csv b/data/clean/cleaned_data_file.csv
deleted file mode 100644
index e69de29b..00000000
diff --git a/data/clean/cleantable_Zina.csv b/data/clean/cleantable_Zina.csv
new file mode 100644
index 00000000..f4e26063
--- /dev/null
+++ b/data/clean/cleantable_Zina.csv
@@ -0,0 +1,696 @@
+name,year,city,region,cuisine,price,url,stars,price_ordinal,price_mean,cuisine_original,major_city,stars_n,Review_rating,Review_count
+108,2019,København,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108,1 star,3,62.5,modern cuisine,København,1,,
+21212,2019,Edinburgh,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212,1 star,3,62.5,creative,Edinburgh,1,,
+28+,2019,Göteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28,1 star,3,62.5,modern cuisine,Göteborg,1,,
+360º,2019,Dubrovnik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA,1 star,4,100.0,modern cuisine,Dubrovnik,1,,
+8 1/2 otto e mezzo - bombana,2019,Macau,Macau,italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana,1 star,3,62.5,italian,Macau,1,,
+8½ otto e mezzo - bombana,2019,Hong Kong,Hong Kong,italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana,3 stars,4,100.0,italian,Hong Kong,3,,
+a. wong,2019,Victoria,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong,1 star,3,62.5,chinese,Victoria,1,,
+acadia,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia,2 stars,4,100.0,contemporary,Chicago,2,,
+acquerello,2019,San Francisco,California,italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello,2 stars,4,100.0,italian,San Francisco,2,,
+adam's,2019,Birmingham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s,1 star,3,62.5,modern cuisine,Birmingham,1,,
+addison,2019,San Diego,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison,1 star,4,100.0,contemporary,San Diego,1,,
+aend,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend,1 star,4,100.0,modern cuisine,Wien,1,,
+agern,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern,1 star,4,100.0,scandinavian,New York,1,,
+agrikultur,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur,1 star,4,100.0,modern cuisine,Stockholm,1,,
+ah yat harbour view (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui,1 star,2,37.5,cantonese,Hong Kong,1,,
+ai fiori,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori,1 star,4,100.0,italian,New York,1,,
+al's place,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place,1 star,2,37.5,californian,San Francisco,1,,
+alain ducasse at morpheus,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus,2 stars,4,100.0,french contemporary,Macau,2,,
+alain ducasse at the dorchester,2019,Mayfair,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester,3 stars,4,100.0,french,Mayfair,3,,
+aldea,2019,New York,New York City,other european,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea,1 star,4,100.0,mediterranean,New York,1,,
+alinea,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea,3 stars,4,100.0,contemporary,Chicago,3,,
+alla prima,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima,2 stars,3,62.5,innovative,Seoul,2,,
+alma,2018,Singapore,Singapore,other european,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma,1 star,1,20.0,european contemporary,Singapore,1,,
+alouette,2019,København,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette,1 star,3,62.5,modern cuisine,København,1,,
+aloë,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe,1 star,4,100.0,creative,Stockholm,1,,
+alyn williams at the westbury,2019,Mayfair,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury,1 star,3,62.5,modern cuisine,Mayfair,1,,
+amador,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador,3 stars,4,100.0,creative,Wien,3,,
+amaya,2019,Belgravia,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya,1 star,3,62.5,indian,Belgravia,1,,
+amber,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032,2 stars,4,100.0,french contemporary,Hong Kong,2,,
+andrew fairlie at gleneagles,2019,Auchterarder,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles,2 stars,4,100.0,creative french,Auchterarder,2,,
+angler,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler,1 star,4,100.0,contemporary,San Francisco,1,,
+angler,2019,Finsbury,United Kingdom,seafood,$$$,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235,1 star,3,62.5,seafood,Finsbury,1,,
+aniar,2019,Gaillimh Galway,Ireland,creative,$$$,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar,1 star,3,62.5,creative,Gaillimh Galway,1,,
+aquavit,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit,2 stars,4,100.0,scandinavian,New York,2,,
+aquavit,2019,Saint James'S,United Kingdom,scandinavian,$$$,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082,1 star,3,62.5,scandinavian,Saint James'S,1,,
+arbor,2019,,Hong Kong,international cuisine,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor,1 star,3,62.5,innovative,,1,,
+arcane,2019,Hong Kong,Hong Kong,other european,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane,1 star,3,62.5,european contemporary,Hong Kong,1,,
+ask,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+aska,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska,2 stars,4,100.0,scandinavian,New York,2,,
+aster,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster,1 star,3,62.5,californian,San Francisco,1,,
+atelier amaro,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro,1 star,4,100.0,modern cuisine,Warszawa,1,,
+atelier crenn,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn,3 stars,4,100.0,contemporary,San Francisco,3,,
+atera,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera,2 stars,4,100.0,contemporary,New York,2,,
+atomix,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix,1 star,4,100.0,korean,New York,1,,
+auberge du soleil,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil,1 star,4,100.0,californian,San Francisco,1,,
+aubergine,2019,Monterey,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine,1 star,4,100.0,contemporary,Monterey,1,,
+a‚o‚c,2019,København,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac,2 stars,4,100.0,modern cuisine,København,2,,
+babbo,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo,1 star,4,100.0,italian,New York,1,,
+babel,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel,1 star,4,100.0,modern cuisine,Budapest,1,,
+bacchanalia,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia,1 star,2,37.5,innovative,Singapore,1,,
+balwoo gongyang,2019,Seoul,South Korea,korean,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang,1 star,2,37.5,temple cuisine,Seoul,1,,
+band of bohemia,2019,Chicago,Chicago,gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia,1 star,3,62.5,gastropub,Chicago,1,,
+bar crenn,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn,1 star,3,62.5,french,San Francisco,1,,
+bar uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu,1 star,4,100.0,japanese,New York,1,,
+barrafina,2019,Soho,United Kingdom,spanish,$$$,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina,1 star,3,62.5,spanish,Soho,1,,
+baumé,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume,2 stars,4,100.0,contemporary,South San Francisco,2,,
+beefbar,2019,Hong Kong,Hong Kong,american,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar,1 star,2,37.5,steakhouse,Hong Kong,1,,
+belmond le manoir aux quat' saisons,2019,Great Milton,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons,2 stars,4,100.0,french,Great Milton,2,,
+belon,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon,1 star,3,62.5,french,Hong Kong,1,,
+benares,2019,Mayfair,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares,1 star,3,62.5,indian,Mayfair,1,,
+benu,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu,3 stars,4,100.0,asian,San Francisco,3,,
+bhoga,2019,Göteborg,Sweden,creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga,1 star,3,62.5,creative,Göteborg,1,,
+bicena,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena,1 star,3,62.5,korean,Seoul,1,,
+birdsong,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong,1 star,4,100.0,american,San Francisco,1,,
+bistro na's,2019,Los Angeles,California,chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s,1 star,2,37.5,chinese,Los Angeles,1,,
+black rat,2019,Winchester,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat,1 star,3,62.5,modern cuisine,Winchester,1,,
+black swan,2019,Oldstead,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan,1 star,3,62.5,modern british,Oldstead,1,,
+blackbird,2019,Newbury,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018,1 star,3,62.5,classic cuisine,Newbury,1,,
+blackbird,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird,1 star,3,62.5,contemporary,Chicago,1,,
+blanca,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca,2 stars,4,100.0,contemporary,New York,2,,
+bloom in the park,2019,Malmö,Sweden,creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park,1 star,2,37.5,creative,Malmö,1,,
+blue duck tavern,2019,"Washington, D.C.",Washington DC,american,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern,1 star,3,62.5,american,"Washington, D.C.",1,,
+blue hill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill,1 star,4,100.0,american,New York,1,,
+bo innovation,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation,3 stars,4,100.0,innovative,Hong Kong,3,,
+bo.lan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan,1 star,4,100.0,thai,Bangkok,1,,
+bohemia,2019,Saint Helier Saint Hélier,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia,1 star,3,62.5,modern cuisine,Saint Helier Saint Hélier,1,,
+boka,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka,1 star,3,62.5,contemporary,Chicago,1,,
+borkonyha winekitchen,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen,1 star,4,100.0,modern cuisine,Budapest,1,,
+botrini's,2019,Athína,Greece,other european,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s,1 star,4,100.0,mediterranean,Athína,1,,
+bouchon,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon,1 star,3,62.5,french,San Francisco,1,,
+bouley at home,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home,1 star,3,62.5,contemporary,New York,1,,
+braci,2018,Singapore,Singapore,italian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci,1 star,2,37.5,italian contemporary,Singapore,1,,
+braidwoods,2019,Dalry,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods,1 star,3,62.5,classic cuisine,Dalry,1,,
+brat,2019,Shoreditch,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat,1 star,3,62.5,traditional british,Shoreditch,1,,
+bresca,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca,1 star,2,37.5,contemporary,"Washington, D.C.",1,,
+bulrush,2019,Bristol,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush,1 star,3,62.5,modern british,Bristol,1,,
+burnt ends,2018,Singapore,Singapore,american,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends,1 star,2,37.5,barbecue,Singapore,1,,
+bybrook,2019,Castle Combe,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook,1 star,3,62.5,modern british,Castle Combe,1,,
+bâtard,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard,1 star,4,100.0,contemporary,New York,1,,
+béni,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni,1 star,2,37.5,french contemporary,Singapore,1,,
+café boulud,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud,1 star,4,100.0,french,New York,1,,
+café china,2019,New York,New York City,chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china,1 star,2,37.5,chinese,New York,1,,
+californios,2019,San Francisco,California,mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios,2 stars,4,100.0,mexican,San Francisco,2,,
+campagne,2019,Cill Chainnigh Kilkenny,Ireland,british,$$$,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne,1 star,3,62.5,modern british,Cill Chainnigh Kilkenny,1,,
+campton place,2019,San Francisco,California,indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place,2 stars,4,100.0,indian,San Francisco,2,,
+candlenut,2018,Singapore,Singapore,other asian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut,1 star,1,20.0,peranakan,Singapore,1,,
+canvas,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292,1 star,4,100.0,innovative,Bangkok,1,,
+caprice,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice,3 stars,4,100.0,french,Hong Kong,3,,
+carbone,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone,1 star,4,100.0,italian,New York,1,,
+carpe diem,2019,Salzburg,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem,1 star,4,100.0,market cuisine,Salzburg,1,,
+carters of moseley,2019,Birmingham,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley,1 star,3,62.5,modern british,Birmingham,1,,
+casa enríque,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique,1 star,2,37.5,mexican,New York,1,,
+casa mono,2019,New York,New York City,spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono,1 star,3,62.5,spanish,New York,1,,
+casamia,2019,Bristol,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia,1 star,3,62.5,creative,Bristol,1,,
+caviar russe,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe,1 star,4,100.0,contemporary,New York,1,,
+celebrity cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine,1 star,2,37.5,cantonese,Hong Kong,1,,
+chapter one,2019,City Centre,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one,1 star,3,62.5,modern cuisine,City Centre,1,,
+cheek by jowl,2018,Singapore,Singapore,australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl,1 star,1,20.0,australian,Singapore,1,,
+chef kang's,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s,1 star,3,62.5,cantonese,Singapore,1,,
+chef's table at brooklyn fare,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare,3 stars,4,100.0,contemporary,New York,3,4.2,575.0
+chestnut,2019,Ballydehob,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut,1 star,3,62.5,modern cuisine,Ballydehob,1,,
+chez bruce,2019,Wandsworth,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce,1 star,3,62.5,french,Wandsworth,1,,
+chez tj,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj,1 star,4,100.0,contemporary,South San Francisco,1,,
+chim by siam wisdom,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom,1 star,3,62.5,thai,Bangkok,1,,
+cipriani,2019,Rio De Janeiro,Rio de Janeiro,italian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani,1 star,4,100.0,italian,Rio De Janeiro,1,,
+city social,2019,City Of London,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social,1 star,3,62.5,modern cuisine,City Of London,1,,
+claro,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro,1 star,2,37.5,mexican,New York,1,,
+claude bosi at bibendum,2019,Chelsea,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum,2 stars,4,100.0,french,Chelsea,2,,
+clock house,2019,Ripley,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house,1 star,3,62.5,modern cuisine,Ripley,1,,
+clou,2019,København,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou,1 star,4,100.0,modern cuisine,København,1,,
+club gascon,2019,London,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon,1 star,3,62.5,french,London,1,,
+coi,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi,2 stars,4,100.0,contemporary,San Francisco,2,,
+commis,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis,2 stars,4,100.0,contemporary,San Francisco,2,,
+commonwealth,2019,San Francisco,California,contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth,1 star,2,37.5,contemporary,San Francisco,1,,
+contra,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra,1 star,4,100.0,contemporary,New York,1,,
+core by clare smyth,2019,North Kensington,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth,2 stars,4,100.0,modern british,North Kensington,2,,
+corner house,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house,1 star,2,37.5,innovative,Singapore,1,,
+costes,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes,1 star,4,100.0,modern cuisine,Budapest,1,,
+costes downtown,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown,1 star,4,100.0,modern cuisine,Budapest,1,,
+cote,2019,New York,New York City,korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote,1 star,3,62.5,korean,New York,1,,
+coworth park,2019,Ascot,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park,1 star,3,62.5,modern cuisine,Ascot,1,,
+credo,2019,Trondheim,Norway,creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo,1 star,4,100.0,creative,Trondheim,1,,
+crown,2019,Burchett'S Green,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549,1 star,3,62.5,regional cuisine,Burchett'S Green,1,,
+crystal jade golden palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace,1 star,1,20.0,chinese,Singapore,1,,
+cut,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418,1 star,4,100.0,steakhouse,Los Angeles,1,,
+cut,2018,Singapore,Singapore,american,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut,1 star,3,62.5,steakhouse,Singapore,1,,
+céleste,2019,Belgravia,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste,1 star,3,62.5,creative french,Belgravia,1,,
+d.o.m.,2019,São Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m,2 stars,4,100.0,creative,São Paulo,2,,
+da san yuan,2019,Taipei,Taipei,chinese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan,1 star,1,20.0,cantonese,Taipei,1,,
+da-wan,2019,Taipei,Taipei,american,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan,1 star,3,62.5,barbecue,Taipei,1,,
+daniel,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel,2 stars,4,100.0,french,New York,2,,
+daniel berlin,2019,Skåne Tranås,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin,2 stars,4,100.0,creative,Skåne Tranås,2,,
+danny's steakhouse,2019,Taipei,Taipei,american,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse,1 star,2,37.5,steakhouse,Taipei,1,,
+das loft,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft,1 star,4,100.0,modern cuisine,Wien,1,,
+del posto,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto,1 star,4,100.0,italian,New York,1,,
+demo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+dialogue,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue,1 star,4,100.0,contemporary,Los Angeles,1,,
+dining in space,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space,1 star,3,62.5,french contemporary,Seoul,1,,
+dining room at the goring,2019,Victoria,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring,1 star,3,62.5,traditional british,Victoria,1,,
+dinner by heston blumenthal,2019,Hyde Park,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal,2 stars,4,100.0,traditional british,Hyde Park,2,,
+domestic,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic,1 star,3,62.5,modern cuisine,Aarhus,1,,
+dosa,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871,1 star,3,62.5,innovative,Seoul,1,,
+draga di lovrana,2019,Lovran,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana,1 star,4,100.0,modern cuisine,Lovran,1,,
+driftwood,2019,Portscatho,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood,1 star,3,62.5,modern cuisine,Portscatho,1,,
+duddell's,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s,1 star,3,62.5,cantonese,Hong Kong,1,,
+dusek's (board & beer),2019,Chicago,Chicago,gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer,1 star,2,37.5,gastropub,Chicago,1,,
+edvard,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard,1 star,4,100.0,modern cuisine,Wien,1,,
+eipic,2019,Belfast,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic,1 star,3,62.5,modern cuisine,Belfast,1,,
+ekstedt,2019,Stockholm,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt,1 star,3,62.5,meats and grills,Stockholm,1,,
+el ideas,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas,1 star,4,100.0,contemporary,Chicago,1,,
+elements,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements,1 star,4,100.0,french contemporary,Bangkok,1,,
+elephant,2019,Torquay,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant,1 star,3,62.5,modern british,Torquay,1,,
+eleven madison park,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park,3 stars,4,100.0,contemporary,New York,3,4.4,3149.0
+elizabeth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth,1 star,4,100.0,contemporary,Chicago,1,,
+elske,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske,1 star,4,100.0,contemporary,Chicago,1,,
+elystan street,2019,Chelsea,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street,1 star,3,62.5,modern british,Chelsea,1,,
+entente,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente,1 star,3,62.5,contemporary,Chicago,1,,
+era ora,2019,København,Denmark,italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora,1 star,2,37.5,italian,København,1,,
+esszimmer,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer,1 star,4,100.0,creative,Salzburg,1,,
+everest,2019,Chicago,Chicago,french,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest,1 star,4,100.0,french,Chicago,1,,
+evvai,2019,São Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai,1 star,4,100.0,modern,São Paulo,1,,
+exquisine,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine,1 star,3,62.5,innovative,Seoul,1,,
+fagn,2019,Trondheim,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn,1 star,4,100.0,modern cuisine,Trondheim,1,,
+farmhouse inn & restaurant,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant,1 star,4,100.0,californian,San Francisco,1,,
+faro,2019,New York,New York City,american,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro,1 star,2,37.5,american,New York,1,,
+fat duck,2019,Bray,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck,3 stars,4,100.0,creative,Bray,3,,
+feng wei ju,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju,2 stars,1,20.0,hunanese and sichuan,Macau,2,,
+field,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field,1 star,4,100.0,modern cuisine,Praha,1,,
+fiola,2019,"Washington, D.C.",Washington DC,italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola,1 star,4,100.0,italian,"Washington, D.C.",1,,
+fischer's at baslow hall,2019,Baslow,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall,1 star,3,62.5,modern cuisine,Baslow,1,,
+five fields,2019,Chelsea,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields,1 star,3,62.5,modern cuisine,Chelsea,1,,
+fordwich arms,2019,Fordwich,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms,1 star,3,62.5,modern cuisine,Fordwich,1,,
+forest side,2019,Grasmere,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side,1 star,3,62.5,modern british,Grasmere,1,,
+formel b,2019,København,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b,1 star,2,37.5,modern cuisine,København,1,,
+forum,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum,2 stars,2,37.5,cantonese,Hong Kong,2,,
+fraiche,2019,Birkenhead,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche,1 star,3,62.5,creative,Birkenhead,1,,
+frantzén,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen,3 stars,4,100.0,modern cuisine,Stockholm,3,,
+frederikshøj,2019,Aarhus,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj,1 star,4,100.0,creative,Aarhus,1,,
+frederiksminde,2019,Præstø,Denmark,creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde,1 star,3,62.5,creative,Præstø,1,,
+fu ho (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui,1 star,2,37.5,cantonese,Hong Kong,1,,
+fäviken magasinet,2019,Järpen,Sweden,creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet,2 stars,4,100.0,creative,Järpen,2,,
+gaa,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa,1 star,4,100.0,innovative,Bangkok,1,,
+gabriel kreuther,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther,2 stars,4,100.0,contemporary,New York,2,,
+gaggan,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan,2 stars,4,100.0,innovative,Bangkok,2,,
+galt,2019,Oslo,Norway,international cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt,1 star,3,62.5,modern cuisine,Oslo,1,,
+galvin at windows,2019,Mayfair,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows,1 star,3,62.5,modern cuisine,Mayfair,1,,
+galvin la chapelle,2019,Spitalfields,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle,1 star,3,62.5,french,Spitalfields,1,,
+gaon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon,3 stars,4,100.0,korean,Seoul,3,,
+garibaldi,2018,Singapore,Singapore,italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi,1 star,1,20.0,italian,Singapore,1,,
+gary danko,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko,1 star,4,100.0,contemporary,San Francisco,1,,
+gastrologik,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik,2 stars,4,100.0,creative,Stockholm,2,,
+gastromé,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome,1 star,3,62.5,modern cuisine,Aarhus,1,,
+geranium,2019,København,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium,3 stars,4,100.0,creative,København,3,,
+gidleigh park,2019,Chagford,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park,1 star,3,62.5,modern cuisine,Chagford,1,,
+ginza sushi ichi,2019,Bangkok,Thailand,japanese,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi,1 star,4,100.0,sushi,Bangkok,1,,
+golden flower,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower,2 stars,2,37.5,chinese,Macau,2,,
+golden formosa,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa,1 star,2,37.5,taiwanese,Taipei,1,,
+goosefoot,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot,1 star,4,100.0,contemporary,Chicago,1,,
+gordon ramsay,2019,Chelsea,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay,3 stars,4,100.0,french,Chelsea,3,,
+gotgan,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan,1 star,4,100.0,korean,Seoul,1,,
+gotham bar and grill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill,1 star,4,100.0,american,New York,1,,
+gramercy tavern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern,1 star,4,100.0,contemporary,New York,1,,
+gravetye manor,2019,Gravetye,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor,1 star,3,62.5,modern british,Gravetye,1,,
+greenhouse,2019,Mayfair,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393,2 stars,4,100.0,creative,Mayfair,2,,
+greenhouse,2019,City Centre,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse,1 star,3,62.5,modern cuisine,City Centre,1,,
+grön,2019,Helsingfors   Helsinki,Finland,finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron,1 star,3,62.5,finnish,Helsingfors   Helsinki,1,,
+guo fu lou,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828,1 star,3,62.5,cantonese,Hong Kong,1,,
+gymkhana,2019,Mayfair,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana,1 star,3,62.5,indian,Mayfair,1,,
+hakkasan hanway place,2019,Bloomsbury,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place,1 star,3,62.5,chinese,Bloomsbury,1,,
+hakkasan mayfair,2019,Mayfair,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair,1 star,3,62.5,chinese,Mayfair,1,,
+hambleton hall,2019,Upper Hambleton,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall,1 star,3,62.5,classic cuisine,Upper Hambleton,1,,
+hana re,2019,Costa Mesa,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re,1 star,4,100.0,japanese,Costa Mesa,1,,
+hand and flowers,2019,Marlow,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers,2 stars,4,100.0,modern british,Marlow,2,,
+hansikgonggan,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan,1 star,3,62.5,korean,Seoul,1,,
+harbor house,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house,1 star,4,100.0,californian,San Francisco,1,,
+harwood arms,2019,Fulham,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms,1 star,3,62.5,modern british,Fulham,1,,
+hashiri,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri,1 star,4,100.0,japanese,San Francisco,1,,
+hayato,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato,1 star,4,100.0,japanese,Los Angeles,1,,
+henne kirkeby kro,2019,Henne,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro,2 stars,3,62.5,classic cuisine,Henne,2,,
+heron & grey,2019,Blackrock,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey,1 star,3,62.5,modern cuisine,Blackrock,1,,
+hide,2019,Mayfair,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide,1 star,3,62.5,modern british,Mayfair,1,,
+hill street tai hwa pork noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle,1 star,1,20.0,street food,Singapore,1,,
+hinds head,2019,Bray,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head,1 star,3,62.5,traditional british,Bray,1,,
+hirohisa,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa,1 star,3,62.5,japanese,New York,1,,
+ho hung kee,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee,1 star,1,20.0,noodles and congee,Hong Kong,1,,
+house,2019,Aird Mhór Ardmore,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house,1 star,3,62.5,modern cuisine,Aird Mhór Ardmore,1,,
+house of tides,2019,Newcastle Upon Tyne,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides,1 star,3,62.5,modern cuisine,Newcastle Upon Tyne,1,,
+hrishi,2019,Bowness On Windermere,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi,1 star,3,62.5,modern cuisine,Bowness On Windermere,1,,
+huto,2019,São Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto,1 star,4,100.0,japanese,São Paulo,1,,
+hytra,2019,Athína,Greece,international cuisine,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra,1 star,4,100.0,modern cuisine,Athína,1,,
+hélène darroze at the connaught,2019,Mayfair,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught,2 stars,4,100.0,modern cuisine,Mayfair,2,,
+ichigo ichie,2019,Corcaigh Cork,Ireland,japanese,$$$,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie,1 star,3,62.5,japanese,Corcaigh Cork,1,,
+ichimura at uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu,2 stars,4,100.0,japanese,New York,2,,
+iggy's,2018,Singapore,Singapore,other european,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s,1 star,3,62.5,european contemporary,Singapore,1,,
+ikarus,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus,2 stars,4,100.0,creative,Salzburg,2,,
+ikoyi,2019,Saint James'S,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi,1 star,3,62.5,creative,Saint James'S,1,,
+im teppanyaki & wine,2019,Hong Kong,Hong Kong,japanese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine,1 star,2,37.5,teppanyaki,Hong Kong,1,,
+imperial treasure fine chinese cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine,1 star,2,37.5,cantonese,Hong Kong,1,,
+imperial treasure fine teochew cuisine (orchard),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard,1 star,1,20.0,chinese,Singapore,1,,
+impromptu by paul lee,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee,1 star,3,62.5,innovative,Taipei,1,,
+in situ,2019,San Francisco,California,international cuisine,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ,1 star,4,100.0,international,San Francisco,1,,
+j'aime by jean-michel lorain,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain,1 star,3,62.5,french contemporary,Bangkok,1,,
+jaan,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan,1 star,3,62.5,french contemporary,Singapore,1,,
+jade dragon,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon,3 stars,2,37.5,cantonese,Macau,3,,
+james sommerin,2019,Penarth,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin,1 star,3,62.5,modern cuisine,Penarth,1,,
+jardin de jade (wan chai),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai,1 star,2,37.5,shanghainese,Hong Kong,1,,
+jay fai,2019,Bangkok,Thailand,international cuisine,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai,1 star,2,37.5,street food,Bangkok,1,,
+jean-georges,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges,2 stars,4,100.0,contemporary,New York,2,,
+jeju noodle bar,2019,New York,New York City,korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar,1 star,2,37.5,korean,New York,1,,
+jewel bako,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako,1 star,3,62.5,japanese,New York,1,,
+jiang-nan chun,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun,1 star,3,62.5,cantonese,Singapore,1,,
+jin jin,2019,Seoul,South Korea,chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin,1 star,1,20.0,chinese,Seoul,1,,
+john's house,2019,Mountsorrel,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house,1 star,3,62.5,modern cuisine,Mountsorrel,1,,
+joo ok,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok,1 star,3,62.5,korean contemporary,Seoul,1,,
+jordnær,2019,København,Denmark,danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer,1 star,3,62.5,danish,København,1,,
+jun sakamoto,2019,São Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto,1 star,4,100.0,japanese,São Paulo,1,,
+jungsik,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik,2 stars,4,100.0,korean,New York,2,,
+jungsik,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965,2 stars,3,62.5,korean contemporary,Seoul,2,,
+junoon,2019,New York,New York City,indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon,1 star,4,100.0,indian,New York,1,,
+jū-ni,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni,1 star,4,100.0,japanese,San Francisco,1,,
+kadeau bornholm,2019,Pedersker,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm,1 star,4,100.0,creative,Pedersker,1,,
+kadeau copenhagen,2019,København,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen,2 stars,4,100.0,modern cuisine,København,2,,
+kai,2019,Mayfair,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638,1 star,3,62.5,chinese,Mayfair,1,,
+kaiseki den by saotome,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome,1 star,4,100.0,japanese,Hong Kong,1,,
+kajitsu,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu,1 star,3,62.5,japanese,New York,1,,
+kali,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali,1 star,3,62.5,californian,Los Angeles,1,,
+kam's roast goose,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose,1 star,1,20.0,cantonese roast meats,Hong Kong,1,,
+kan suke,2019,São Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke,1 star,4,100.0,japanese,São Paulo,1,,
+kanoyama,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama,1 star,3,62.5,japanese,New York,1,,
+kashiwaya,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya,2 stars,4,100.0,japanese,Hong Kong,2,,
+kato,2019,Los Angeles,California,other asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato,1 star,3,62.5,asian,Los Angeles,1,,
+keiko à nob hill,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill,1 star,4,100.0,fusion,San Francisco,1,,
+ken an ho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho,1 star,4,100.0,japanese,Taipei,1,,
+kenzo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo,1 star,4,100.0,japanese,San Francisco,1,,
+kiin kiin,2019,København,Denmark,other asian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin,1 star,2,37.5,thai,København,1,,
+kilian stuba,2019,Kleinwalsertal,Austria,creative,$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba,1 star,4,100.0,creative,Kleinwalsertal,1,,
+kin khao,2019,San Francisco,California,other asian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao,1 star,2,37.5,thai,San Francisco,1,,
+king,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226,1 star,1,20.0,cantonese,Macau,1,,
+kinjo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo,1 star,4,100.0,japanese,San Francisco,1,,
+kinoshita,2019,São Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita,1 star,4,100.0,japanese,São Paulo,1,,
+kinship,2019,"Washington, D.C.",Washington DC,contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship,1 star,3,62.5,contemporary,"Washington, D.C.",1,,
+kitchen table at bubbledogs,2019,Bloomsbury,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs,2 stars,4,100.0,modern cuisine,Bloomsbury,2,,
+kitchen w8,2019,Kensington,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8,1 star,3,62.5,modern cuisine,Kensington,1,,
+kitchin,2019,Leith,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin,1 star,3,62.5,modern cuisine,Leith,1,,
+kitcho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho,1 star,4,100.0,sushi,Taipei,1,,
+ko,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko,2 stars,4,100.0,contemporary,New York,2,,
+kojima,2019,Seoul,South Korea,japanese,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima,2 stars,4,100.0,sushi,Seoul,2,,
+koka,2019,Göteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka,1 star,3,62.5,modern cuisine,Göteborg,1,,
+kokkeriet,2019,København,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet,1 star,3,62.5,modern cuisine,København,1,,
+koks,2019,Leynar,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780,2 stars,4,100.0,creative,Leynar,2,,
+komi,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi,1 star,4,100.0,mediterranean,"Washington, D.C.",1,,
+kong hans kælder,2019,København,Denmark,french,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder,1 star,4,100.0,classic french,København,1,,
+konstantin filippou,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou,2 stars,4,100.0,modern cuisine,Wien,2,,
+kontrast,2019,Oslo,Norway,scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast,1 star,3,62.5,scandinavian,Oslo,1,,
+kosaka,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka,1 star,4,100.0,japanese,New York,1,,
+kosushi,2019,São Paulo,Sao Paulo,japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi,1 star,3,62.5,japanese,São Paulo,1,,
+kwonsooksoo,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo,2 stars,3,62.5,korean,Seoul,2,,
+kyo ya,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya,1 star,3,62.5,japanese,New York,1,,
+l'amitié,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie,1 star,3,62.5,french,Seoul,1,,
+l'appart,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart,1 star,4,100.0,french,New York,1,,
+l'atelier de joël robuchon,2019,Taipei,Taipei,french,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759,1 star,3,62.5,french contemporary,Taipei,1,,
+l'atelier de joël robuchon,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon,3 stars,4,100.0,french contemporary,Hong Kong,3,,
+l'atelier de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505,2 stars,4,100.0,french,New York,2,,
+l'ecrivain,2019,City Centre,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain,1 star,3,62.5,modern cuisine,City Centre,1,,
+l'enclume,2019,Cartmel,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume,2 stars,4,100.0,creative,Cartmel,2,,
+l'ortolan,2019,Shinfield,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan,1 star,3,62.5,french,Shinfield,1,,
+la dame de pic,2019,City Of London,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic,1 star,3,62.5,modern french,City Of London,1,,
+la degustation bohême bourgeoise,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise,1 star,4,100.0,modern cuisine,Praha,1,,
+la toque,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque,1 star,4,100.0,contemporary,San Francisco,1,,
+la trompette,2019,Chiswick,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette,1 star,3,62.5,modern british,Chiswick,1,,
+la yeon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon,3 stars,4,100.0,korean,Seoul,3,,
+labyrinth,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth,1 star,2,37.5,innovative,Singapore,1,,
+lady helen,2019,Baile Mhic Andáin Thomastown,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen,1 star,3,62.5,modern cuisine,Baile Mhic Andáin Thomastown,1,,
+lai heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen,1 star,2,37.5,cantonese,Macau,1,,
+lasai,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai,1 star,4,100.0,modern,Rio De Janeiro,1,,
+lazy bear,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear,2 stars,4,100.0,contemporary,San Francisco,2,,
+le bernardin,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin,3 stars,4,100.0,seafood,New York,3,,
+le champignon sauvage,2019,Cheltenham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage,1 star,3,62.5,modern cuisine,Cheltenham,1,,
+le ciel by toni mörwald,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald,1 star,4,100.0,classic cuisine,Wien,1,,
+le comptoir,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir,1 star,4,100.0,californian,Los Angeles,1,,
+le coucou,2019,New York,New York City,french,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou,1 star,3,62.5,french,New York,1,,
+le du,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du,1 star,4,100.0,thai,Bangkok,1,,
+le gavroche,2019,Mayfair,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche,2 stars,4,100.0,french,Mayfair,2,,
+le grill de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon,1 star,4,100.0,french,New York,1,,
+le normandie,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie,2 stars,4,100.0,french contemporary,Bangkok,2,,
+le palais,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais,3 stars,2,37.5,cantonese,Taipei,3,,
+ledbury,2019,North Kensington,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury,2 stars,4,100.0,modern cuisine,North Kensington,2,,
+lee jong kuk 104,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104,1 star,4,100.0,korean,Seoul,1,,
+lei garden,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509,1 star,1,20.0,cantonese,Singapore,1,,
+lei garden (kwun tong),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong,1 star,1,20.0,cantonese,Hong Kong,1,,
+lei garden (mong kok),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok,1 star,1,20.0,cantonese,Hong Kong,1,,
+leroy,2019,Shoreditch,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy,1 star,3,62.5,modern british,Shoreditch,1,,
+les amis,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis,2 stars,3,62.5,french,Singapore,2,,
+liao fan hong kong soya sauce chicken rice & noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle,1 star,1,20.0,street food,Singapore,1,,
+loaf on,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on,1 star,2,37.5,cantonese,Hong Kong,1,,
+loam,2019,Gaillimh Galway,Ireland,creative,$$$,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam,1 star,3,62.5,creative,Gaillimh Galway,1,,
+locanda locatelli,2019,Marylebone,United Kingdom,italian,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli,1 star,3,62.5,italian,Marylebone,1,,
+loch bay,2019,Waternish,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay,1 star,3,62.5,modern cuisine,Waternish,1,,
+logy,2019,Taipei,Taipei,other asian,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy,1 star,4,100.0,asian contemporary,Taipei,1,,
+longtail,2019,Taipei,Taipei,international cuisine,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail,1 star,2,37.5,innovative,Taipei,1,,
+lord stanley,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley,1 star,3,62.5,californian,San Francisco,1,,
+luce,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce,1 star,3,62.5,contemporary,San Francisco,1,,
+lung king heen,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen,3 stars,4,100.0,cantonese,Hong Kong,3,,
+lyle's,2019,Shoreditch,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s,1 star,3,62.5,modern british,Shoreditch,1,,
+lympstone manor,2019,Lympstone,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor,1 star,3,62.5,modern cuisine,Lympstone,1,,
+ma cuisine,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine,1 star,1,20.0,french,Singapore,1,,
+maaemo,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo,3 stars,4,100.0,modern cuisine,Oslo,3,,
+madcap,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap,1 star,4,100.0,contemporary,San Francisco,1,,
+madera,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera,1 star,3,62.5,contemporary,San Francisco,1,,
+madrona manor,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor,1 star,4,100.0,contemporary,San Francisco,1,,
+man wah,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah,1 star,4,100.0,cantonese,Hong Kong,1,,
+mandarin grill + bar,2019,Hong Kong,Hong Kong,other european,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar,1 star,4,100.0,european contemporary,Hong Kong,1,,
+manresa,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa,3 stars,4,100.0,contemporary,South San Francisco,3,,
+maní,2019,São Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani,1 star,4,100.0,creative,São Paulo,1,,
+marchal,2019,København,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal,1 star,2,37.5,modern cuisine,København,1,,
+marcus,2019,Belgravia,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus,1 star,3,62.5,modern cuisine,Belgravia,1,,
+marea,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea,2 stars,4,100.0,seafood,New York,2,,
+martin wishart,2019,Leith,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart,1 star,3,62.5,modern cuisine,Leith,1,,
+masa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa,3 stars,4,100.0,japanese,New York,3,,
+masons arms,2019,Knowstone,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms,1 star,3,62.5,classic french,Knowstone,1,,
+masseria,2019,"Washington, D.C.",Washington DC,italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria,1 star,3,62.5,italian,"Washington, D.C.",1,,
+mathias dahlgren-matbaren,2019,Stockholm,Sweden,international cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren,1 star,2,37.5,modern cuisine,Stockholm,1,,
+matt worswick at the latymer,2019,Bagshot,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer,1 star,3,62.5,modern cuisine,Bagshot,1,,
+maude,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude,1 star,4,100.0,contemporary,Los Angeles,1,,
+maum,2019,South San Francisco,California,korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum,1 star,4,100.0,korean,South San Francisco,1,,
+meadowsweet,2019,New York,New York City,other european,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet,1 star,3,62.5,mediterranean,New York,1,,
+mee,2019,Rio De Janeiro,Rio de Janeiro,other asian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee,1 star,4,100.0,asian influences,Rio De Janeiro,1,,
+meta,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta,1 star,2,37.5,innovative,Singapore,1,,
+methavalai sorndaeng,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng,1 star,2,37.5,thai,Bangkok,1,,
+mews,2019,Baltimore,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews,1 star,3,62.5,modern cuisine,Baltimore,1,,
+mezzaluna,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna,2 stars,4,100.0,innovative,Bangkok,2,,
+me‚mu,2019,Vejle,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu,1 star,3,62.5,modern cuisine,Vejle,1,,
+michael mina,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina,1 star,4,100.0,contemporary,San Francisco,1,,
+midsummer house,2019,Cambridge,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house,2 stars,4,100.0,modern cuisine,Cambridge,2,,
+ming court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court,1 star,3,62.5,cantonese,Hong Kong,1,,
+ming fu,2019,Taipei,Taipei,other asian,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu,1 star,3,62.5,taiwanese,Taipei,1,,
+mingles,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles,2 stars,3,62.5,korean contemporary,Seoul,2,,
+minibar,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar,2 stars,4,100.0,contemporary,"Washington, D.C.",2,,
+mister jiu's,2019,San Francisco,California,chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius,1 star,3,62.5,chinese,San Francisco,1,,
+mizumi (macau),2019,Macau,Macau,japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau,2 stars,2,37.5,japanese,Macau,2,,
+monte,2019,Rovinj,Croatia,creative,$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte,1 star,4,100.0,creative,Rovinj,1,,
+moor hall,2019,Aughton,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall,2 stars,4,100.0,modern british,Aughton,2,,
+mori sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi,1 star,4,100.0,japanese,Los Angeles,1,,
+morston hall,2019,Morston,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall,1 star,3,62.5,modern british,Morston,1,,
+mosu,2019,Seoul,South Korea,international cuisine,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu,1 star,4,100.0,innovative,Seoul,1,,
+mountain and sea house,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house,1 star,2,37.5,taiwanese,Taipei,1,,
+mourad,2019,San Francisco,California,moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad,1 star,3,62.5,moroccan,San Francisco,1,,
+mraz & sohn,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn,2 stars,4,100.0,creative,Wien,2,,
+mume,2019,Taipei,Taipei,other european,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume,1 star,3,62.5,european contemporary,Taipei,1,,
+muoki,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki,1 star,3,62.5,innovative,Seoul,1,,
+murano,2019,Mayfair,United Kingdom,italian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano,1 star,3,62.5,italian,Mayfair,1,,
+métier,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier,1 star,4,100.0,contemporary,"Washington, D.C.",1,,
+n/naka,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka,2 stars,4,100.0,contemporary,Los Angeles,2,,
+nahm,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm,1 star,3,62.5,thai,Bangkok,1,,
+new punjab club,2019,Hong Kong,Hong Kong,indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club,1 star,2,37.5,indian,Hong Kong,1,,
+nico,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico,1 star,3,62.5,contemporary,San Francisco,1,,
+nix,2019,New York,New York City,vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix,1 star,3,62.5,vegetarian,New York,1,,
+noda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda,1 star,4,100.0,japanese,New York,1,,
+noel,2019,Zagreb,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel,1 star,4,100.0,modern cuisine,Zagreb,1,,
+noma,2019,København,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma,2 stars,4,100.0,creative,København,2,,
+nomad,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad,1 star,4,100.0,contemporary,New York,1,,
+north pond,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond,1 star,3,62.5,contemporary,Chicago,1,,
+northcote,2019,Langho,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote,1 star,3,62.5,modern british,Langho,1,,
+nouri,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri,1 star,1,20.0,innovative,Singapore,1,,
+nozawa bar,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar,1 star,4,100.0,japanese,Los Angeles,1,,
+number one,2019,Edinburgh,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one,1 star,3,62.5,modern cuisine,Edinburgh,1,,
+nut tree,2019,Murcott,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree,1 star,3,62.5,traditional british,Murcott,1,,
+oaxen krog,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog,2 stars,4,100.0,creative,Stockholm,2,,
+octavia,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia,1 star,3,62.5,californian,San Francisco,1,,
+octavium,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium,1 star,3,62.5,italian,Hong Kong,1,,
+odette,2018,Singapore,Singapore,french,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette,2 stars,4,100.0,french contemporary,Singapore,2,,
+okuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda,1 star,4,100.0,japanese,New York,1,,
+olive tree,2019,Bath,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree,1 star,3,62.5,modern cuisine,Bath,1,,
+olo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+olympe,2019,Rio De Janeiro,Rio de Janeiro,french,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe,1 star,4,100.0,french,Rio De Janeiro,1,,
+omakase,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase,1 star,4,100.0,japanese,San Francisco,1,,
+onyx,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx,2 stars,4,100.0,modern cuisine,Budapest,2,,
+operakällaren,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren,1 star,4,100.0,classic cuisine,Stockholm,1,,
+ora,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+oriole,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole,2 stars,4,100.0,contemporary,Chicago,2,,
+oro,2019,Rio De Janeiro,Rio de Janeiro,creative,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro,2 stars,4,100.0,creative,Rio De Janeiro,2,,
+orsa & winston,2019,Los Angeles,California,other asian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston,1 star,4,100.0,fusion,Los Angeles,1,,
+osteria mozza,2019,Los Angeles,California,italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza,1 star,3,62.5,italian,Los Angeles,1,,
+oteque,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque,1 star,4,100.0,modern,Rio De Janeiro,1,,
+outlaw's fish kitchen,2019,Port Isaac,United Kingdom,seafood,$$$,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen,1 star,3,62.5,seafood,Port Isaac,1,,
+ox,2019,Belfast,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109,1 star,3,62.5,modern british,Belfast,1,,
+oxford kitchen,2019,Oxford,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen,1 star,3,62.5,modern british,Oxford,1,,
+oxomoco,2019,New York,New York City,mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco,1 star,3,62.5,mexican,New York,1,,
+paco tapas,2019,Bristol,United Kingdom,spanish,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas,1 star,3,62.5,spanish,Bristol,1,,
+palace,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+pang's kitchen,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen,1 star,1,20.0,cantonese,Hong Kong,1,,
+parachute,2019,Chicago,Chicago,other asian,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute,1 star,2,37.5,fusion,Chicago,1,,
+paste,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste,1 star,3,62.5,thai,Bangkok,1,,
+patrick guilbaud,2019,City Centre,Ireland,french,$$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud,2 stars,4,100.0,modern french,City Centre,2,,
+paul ainsworth at no.6,2019,Padstow,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6,1 star,3,62.5,modern cuisine,Padstow,1,,
+pearl dragon,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon,1 star,3,62.5,cantonese,Macau,1,,
+peel's,2019,Hampton In Arden,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s,1 star,3,62.5,creative british,Hampton In Arden,1,,
+pelegrini,2019,Šibenik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini,1 star,4,100.0,modern cuisine,Šibenik,1,,
+per se,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se,3 stars,4,100.0,contemporary,New York,3,4.5,2009.0
+peter luger,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger,1 star,4,100.0,steakhouse,New York,1,,
+pfefferschiff,2019,Hallwang,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff,1 star,4,100.0,classic cuisine,Hallwang,1,,
+picchi,2019,São Paulo,Sao Paulo,italian,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi,1 star,4,100.0,italian,São Paulo,1,,
+pied à terre,2019,Bloomsbury,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre,1 star,3,62.5,creative,Bloomsbury,1,,
+pierre,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre,2 stars,4,100.0,french contemporary,Hong Kong,2,,
+pineapple and pearls,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls,2 stars,4,100.0,contemporary,"Washington, D.C.",2,,
+pipe and glass,2019,South Dalton,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass,1 star,3,62.5,modern british,South Dalton,1,,
+plume,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume,1 star,4,100.0,european,"Washington, D.C.",1,,
+plumed horse,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse,1 star,4,100.0,contemporary,South San Francisco,1,,
+pm & vänner,2019,Växjö,Sweden,creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner,1 star,3,62.5,creative,Växjö,1,,
+pollen street social,2019,Mayfair,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social,1 star,3,62.5,creative,Mayfair,1,,
+pony & trap,2019,Chew Magna,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap,1 star,3,62.5,modern british,Chew Magna,1,,
+poom,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom,1 star,3,62.5,korean,Seoul,1,,
+portland,2019,Regent'S Park,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland,1 star,3,62.5,modern cuisine,Regent'S Park,1,,
+pramerl & the wolf,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf,1 star,4,100.0,creative,Wien,1,,
+protégé,2019,South San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege,1 star,3,62.5,contemporary,South San Francisco,1,,
+providence,2019,Los Angeles,California,seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence,2 stars,4,100.0,seafood,Los Angeles,2,,
+pru,2019,Phuket,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru,1 star,4,100.0,innovative,Phuket,1,,
+purnell's,2019,Birmingham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s,1 star,3,62.5,modern cuisine,Birmingham,1,,
+putien (kitchener road),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road,1 star,1,20.0,fujian,Singapore,1,,
+pétrus,2019,Belgravia,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688,1 star,3,62.5,french,Belgravia,1,,
+q sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi,1 star,4,100.0,japanese,Los Angeles,1,,
+qi (wan chai),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai,1 star,1,20.0,sichuan,Hong Kong,1,,
+quilon,2019,Victoria,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon,1 star,3,62.5,indian,Victoria,1,,
+quince,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince,3 stars,4,100.0,contemporary,San Francisco,3,,
+r-haan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan,1 star,4,100.0,thai,Bangkok,1,,
+raby hunt,2019,Summerhouse,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt,2 stars,4,100.0,modern british,Summerhouse,2,,
+rasa,2019,San Francisco,California,indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa,1 star,2,37.5,indian,San Francisco,1,,
+raw,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw,2 stars,3,62.5,innovative,Taipei,2,,
+re-naa,2019,Stavanger,Norway,creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa,1 star,4,100.0,creative,Stavanger,1,,
+rech,2019,Hong Kong,Hong Kong,seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech,1 star,4,100.0,seafood,Hong Kong,1,,
+red lion freehouse,2019,East Chisenbury,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse,1 star,3,62.5,classic cuisine,East Chisenbury,1,,
+relæ,2019,København,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae,1 star,2,37.5,modern cuisine,København,1,,
+restaurant hywel jones by lucknam park,2019,Colerne,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park,1 star,3,62.5,modern british,Colerne,1,,
+restaurant nathan outlaw,2019,Port Isaac,United Kingdom,seafood,$$$$,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw,2 stars,4,100.0,seafood,Port Isaac,2,,
+restaurant sat bains,2019,Nottingham,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains,2 stars,4,100.0,creative,Nottingham,2,,
+restaurant tristan,2019,Horsham,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan,1 star,3,62.5,modern british,Horsham,1,,
+rhubarb,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb,1 star,1,20.0,french contemporary,Singapore,1,,
+rich table,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table,1 star,3,62.5,contemporary,San Francisco,1,,
+ritz restaurant,2019,Westminster,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant,1 star,3,62.5,modern british,Westminster,1,,
+river café,2019,Hammersmith,United Kingdom,italian,$$$,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe,1 star,3,62.5,italian,Hammersmith,1,,
+robuchon au dôme,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome,3 stars,4,100.0,french contemporary,Macau,3,,
+rogan & co,2019,Cartmel,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co,1 star,3,62.5,creative british,Cartmel,1,,
+roganic,2019,Marylebone,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic,1 star,3,62.5,creative british,Marylebone,1,,
+roister,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister,1 star,3,62.5,contemporary,Chicago,1,,
+rose's luxury,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury,1 star,2,37.5,contemporary,"Washington, D.C.",1,,
+ruean panya,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya,1 star,2,37.5,thai,Bangkok,1,,
+rustic canyon,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon,1 star,3,62.5,californian,Los Angeles,1,,
+ryo gastronomia,2019,São Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia,1 star,4,100.0,japanese,São Paulo,1,,
+saawaan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan,1 star,4,100.0,thai contemporary,Bangkok,1,,
+sabi omakase,2019,Stavanger,Norway,japanese,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase,1 star,4,100.0,sushi,Stavanger,1,,
+sabor,2019,Mayfair,United Kingdom,spanish,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor,1 star,3,62.5,spanish,Mayfair,1,,
+saint pierre,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre,1 star,2,37.5,french contemporary,Singapore,1,,
+saison,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison,2 stars,4,100.0,californian,San Francisco,2,,
+salt,2019,Stratford Upon Avon,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869,1 star,3,62.5,modern british,Stratford Upon Avon,1,,
+samphire,2019,Saint Helier Saint Hélier,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987,1 star,3,62.5,modern cuisine,Saint Helier Saint Hélier,1,,
+saneh jaan,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan,1 star,2,37.5,thai,Bangkok,1,,
+satsuki,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki,1 star,4,100.0,japanese,New York,1,,
+sav,2019,Malmö,Sweden,creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav,1 star,3,62.5,creative,Malmö,1,,
+savelberg,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg,1 star,3,62.5,french contemporary,Bangkok,1,,
+schwa,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa,1 star,4,100.0,contemporary,Chicago,1,,
+senns.restaurant,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant,2 stars,4,100.0,creative,Salzburg,2,,
+senses,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses,1 star,4,100.0,modern cuisine,Warszawa,1,,
+sepia,2019,Chicago,Chicago,american,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia,1 star,3,62.5,american,Chicago,1,,
+seven park place,2019,Saint James'S,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place,1 star,3,62.5,modern cuisine,Saint James'S,1,,
+shang palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace,1 star,3,62.5,cantonese,Hong Kong,1,,
+shibumi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644,1 star,4,100.0,japanese,Los Angeles,1,,
+shiki,2019,Wien,Austria,japanese,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki,1 star,4,100.0,japanese,Wien,1,,
+shin sushi,2019,Los Angeles,California,japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi,1 star,3,62.5,japanese,Los Angeles,1,,
+shinji (bras basah road),2018,Singapore,Singapore,japanese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road,1 star,2,37.5,sushi,Singapore,1,,
+shinji (tanglin road),2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road,1 star,4,100.0,sushi,Singapore,1,,
+shinji by kanesaka,2019,Macau,Macau,japanese,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka,1 star,4,100.0,sushi,Macau,1,,
+shisen hanten,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten,2 stars,1,20.0,chinese,Singapore,2,,
+shoukouwa,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa,2 stars,4,100.0,sushi,Singapore,2,,
+shoun ryugin,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin,2 stars,4,100.0,japanese contemporary,Taipei,2,,
+shunji,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji,1 star,4,100.0,japanese,Los Angeles,1,,
+silvio nickol gourmet restaurant,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant,2 stars,4,100.0,modern cuisine,Wien,2,,
+simon radley at chester grosvenor,2019,Chester,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor,1 star,3,62.5,modern cuisine,Chester,1,,
+simpsons,2019,Birmingham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons,1 star,3,62.5,modern cuisine,Birmingham,1,,
+singlethread,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread,3 stars,4,100.0,contemporary,San Francisco,3,,
+siren by rw,2019,"Washington, D.C.",Washington DC,seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw,1 star,3,62.5,seafood,"Washington, D.C.",1,,
+sk mat & människor,2019,Göteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor,1 star,3,62.5,modern cuisine,Göteborg,1,,
+sketch (the lecture room & library),2019,Mayfair,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library,2 stars,4,100.0,modern french,Mayfair,2,,
+slotskøkkenet,2019,Hørve,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet,1 star,4,100.0,creative,Hørve,1,,
+smyth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth,2 stars,4,100.0,contemporary,Chicago,2,,
+social eating house,2019,Soho,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house,1 star,3,62.5,modern cuisine,Soho,1,,
+soigné,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne,1 star,3,62.5,innovative,Seoul,1,,
+somni,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni,2 stars,4,100.0,contemporary,Los Angeles,2,,
+sons & daughters,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters,1 star,4,100.0,contemporary,San Francisco,1,,
+sorn,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn,1 star,4,100.0,southern thai,Bangkok,1,,
+sorrel,2019,Dorking,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459,1 star,3,62.5,modern british,Dorking,1,,
+sorrel,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel,1 star,3,62.5,californian,San Francisco,1,,
+sosban & the old butchers,2019,Menai Bridge Porthaethwy,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers,1 star,3,62.5,modern cuisine,Menai Bridge Porthaethwy,1,,
+spiaggia,2019,Chicago,Chicago,italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia,1 star,4,100.0,italian,Chicago,1,,
+spondi,2019,Athína,Greece,french,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi,2 stars,4,100.0,french,Athína,2,,
+spqr,2019,San Francisco,California,italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr,1 star,3,62.5,italian,San Francisco,1,,
+spring moon,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon,1 star,3,62.5,cantonese,Hong Kong,1,,
+spruce,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce,1 star,3,62.5,californian,San Francisco,1,,
+sra bua by kiin kiin,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin,1 star,3,62.5,thai contemporary,Bangkok,1,,
+st john,2019,Clerkenwell,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john,1 star,3,62.5,traditional british,Clerkenwell,1,,
+stand,2019,Budapest,Hungary,international cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand,1 star,3,62.5,modern cuisine,Budapest,1,,
+star inn at harome,2019,Harome,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome,1 star,3,62.5,modern british,Harome,1,,
+state bird provisions,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions,1 star,2,37.5,american,San Francisco,1,,
+statholdergaarden,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden,1 star,4,100.0,classic cuisine,Oslo,1,,
+stay,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay,1 star,3,62.5,french contemporary,Seoul,1,,
+steirereck im stadtpark,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark,2 stars,4,100.0,creative,Wien,2,,
+story,2019,Bermondsey,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story,1 star,3,62.5,modern cuisine,Bermondsey,1,,
+stud!o at the standard,2019,København,Denmark,creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard,1 star,3,62.5,creative,København,1,,
+suan thip,2019,Bangkok,Thailand,other asian,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip,1 star,1,20.0,thai,Bangkok,1,,
+substans,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans,1 star,3,62.5,modern cuisine,Aarhus,1,,
+summer palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace,1 star,3,62.5,cantonese,Hong Kong,1,,
+summer palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658,1 star,1,20.0,cantonese,Singapore,1,,
+summer pavilion,2018,Singapore,Singapore,chinese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion,1 star,2,37.5,cantonese,Singapore,1,,
+sun tung lok,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok,2 stars,2,37.5,cantonese,Hong Kong,2,,
+sushi amamoto,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto,2 stars,4,100.0,sushi,Taipei,2,,
+sushi amane,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane,1 star,4,100.0,japanese,New York,1,,
+sushi ginza onodera,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera,2 stars,4,100.0,japanese,New York,2,,
+sushi ginza onodera,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850,2 stars,4,100.0,japanese,Los Angeles,2,,
+sushi ichi,2018,Singapore,Singapore,japanese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi,1 star,3,62.5,sushi,Singapore,1,,
+sushi inoue,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue,1 star,4,100.0,japanese,New York,1,,
+sushi kimura,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura,1 star,4,100.0,sushi,Singapore,1,,
+sushi nakazawa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa,1 star,4,100.0,japanese,New York,1,,
+sushi nomura,2019,Taipei,Taipei,japanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura,1 star,3,62.5,sushi,Taipei,1,,
+sushi noz,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz,1 star,4,100.0,japanese,New York,1,,
+sushi ryu,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu,1 star,4,100.0,sushi,Taipei,1,,
+sushi saito,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito,2 stars,4,100.0,sushi,Hong Kong,2,,
+sushi shikon,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon,3 stars,4,100.0,sushi,Hong Kong,3,,
+sushi sho,2019,Stockholm,Sweden,japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho,1 star,3,62.5,japanese,Stockholm,1,,
+sushi taro,2019,"Washington, D.C.",Washington DC,japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro,1 star,4,100.0,japanese,"Washington, D.C.",1,,
+sushi tokami,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami,1 star,4,100.0,sushi,Hong Kong,1,,
+sushi wadatsumi,2019,Hong Kong,Hong Kong,japanese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi,1 star,3,62.5,sushi,Hong Kong,1,,
+sushi yasuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda,1 star,4,100.0,japanese,New York,1,,
+sushi yoshizumi,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi,1 star,4,100.0,japanese,San Francisco,1,,
+søllerød kro,2019,København,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro,1 star,2,37.5,modern cuisine,København,1,,
+sühring,2019,Bangkok,Thailand,other european,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring,2 stars,4,100.0,european contemporary,Bangkok,2,,
+t'ang court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court,3 stars,3,62.5,cantonese,Hong Kong,3,,
+ta vie,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie,2 stars,4,100.0,innovative,Hong Kong,2,,
+table for four,2019,Seoul,South Korea,other european,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four,1 star,3,62.5,european contemporary,Seoul,1,,
+taco maría,2019,Costa Mesa,California,mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria,1 star,3,62.5,mexican,Costa Mesa,1,,
+tail up goat,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat,1 star,2,37.5,contemporary,"Washington, D.C.",1,,
+tainan tan tsu mien seafood,2019,Taipei,Taipei,seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood,1 star,2,37.5,seafood,Taipei,1,,
+takumi by daisuke mori,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori,1 star,4,100.0,innovative,Hong Kong,1,,
+tangará jean-georges,2019,São Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges,1 star,4,100.0,modern,São Paulo,1,,
+tate,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate,1 star,4,100.0,innovative,Hong Kong,1,,
+taïrroir,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir,2 stars,3,62.5,innovative,Taipei,2,,
+temporis,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis,1 star,4,100.0,contemporary,Chicago,1,,
+tempura matsui,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui,1 star,4,100.0,japanese,New York,1,,
+tenku ryugin,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin,2 stars,4,100.0,japanese,Hong Kong,2,,
+texture,2019,Marylebone,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture,1 star,3,62.5,creative,Marylebone,1,,
+the araki,2019,Mayfair,United Kingdom,japanese,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki,3 stars,4,100.0,japanese,Mayfair,3,,
+the cellar,2019,Anstruther,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar,1 star,3,62.5,modern cuisine,Anstruther,1,,
+the checkers,2019,Montgomery Trefaldwyn,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers,1 star,3,62.5,french,Montgomery Trefaldwyn,1,,
+the clocktower,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower,1 star,3,62.5,contemporary,New York,1,,
+the clove club,2019,Shoreditch,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club,1 star,3,62.5,modern cuisine,Shoreditch,1,,
+the coach,2019,Marlow,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach,1 star,3,62.5,modern british,Marlow,1,,
+the cross at kenilworth,2019,Kenilworth,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth,1 star,3,62.5,classic cuisine,Kenilworth,1,,
+the dabney,2019,"Washington, D.C.",Washington DC,american,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney,1 star,2,37.5,american,"Washington, D.C.",1,,
+the dining room,2019,Malmesbury,United Kingdom,other asian,$$$,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454,1 star,3,62.5,asian influences,Malmesbury,1,,
+the eight,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight,3 stars,3,62.5,chinese,Macau,3,,
+the finch,2019,New York,New York City,american,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch,1 star,3,62.5,american,New York,1,,
+the french laundry,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry,3 stars,4,100.0,contemporary,San Francisco,3,,
+the glasshouse,2019,Kew,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse,1 star,3,62.5,modern cuisine,Kew,1,,
+the golden peacock,2019,Macau,Macau,indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock,1 star,1,20.0,indian,Macau,1,,
+the guest house,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house,2 stars,2,37.5,sichuan-huai yang,Taipei,2,,
+the inn at little washington,2019,"Washington, D.C.",Washington DC,american,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington,3 stars,4,100.0,american,"Washington, D.C.",3,,
+the kitchen,2019,Sacramento,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371,1 star,4,100.0,contemporary,Sacramento,1,,
+the kitchen,2019,Macau,Macau,american,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810,1 star,3,62.5,steakhouse,Macau,1,,
+the man behind the curtain,2019,Leeds,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain,1 star,3,62.5,creative,Leeds,1,,
+the modern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern,2 stars,4,100.0,contemporary,New York,2,,
+the musket room,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room,1 star,3,62.5,contemporary,New York,1,,
+the neptune,2019,Hunstanton,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune,1 star,3,62.5,modern cuisine,Hunstanton,1,,
+the ninth,2019,Bloomsbury,United Kingdom,other european,$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth,1 star,3,62.5,mediterranean cuisine,Bloomsbury,1,,
+the ocean,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean,1 star,4,100.0,french,Hong Kong,1,,
+the peat inn,2019,Peat Inn,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn,1 star,3,62.5,classic cuisine,Peat Inn,1,,
+the progress,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress,1 star,3,62.5,californian,San Francisco,1,,
+the restaurant at meadowood,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood,3 stars,4,100.0,contemporary,San Francisco,3,,
+the river café,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe,1 star,4,100.0,contemporary,New York,1,,
+the song of india,2018,Singapore,Singapore,indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india,1 star,1,20.0,indian,Singapore,1,,
+the sportsman,2019,Seasalter,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman,1 star,3,62.5,modern british,Seasalter,1,,
+the square,2019,Mayfair,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391,1 star,3,62.5,creative french,Mayfair,1,,
+the tasting room,2019,Macau,Macau,french,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room,2 stars,3,62.5,french contemporary,Macau,2,,
+the village pub,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub,1 star,3,62.5,contemporary,San Francisco,1,,
+the whitebrook,2019,Whitebrook,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook,1 star,3,62.5,modern british,Whitebrook,1,,
+thomas carr @ the olive room,2019,Ilfracombe,United Kingdom,seafood,$$$,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room,1 star,3,62.5,seafood,Ilfracombe,1,,
+thörnströms kök,2019,Göteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok,1 star,3,62.5,classic cuisine,Göteborg,1,,
+ti trin ned,2019,Fredericia,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned,1 star,3,62.5,modern cuisine,Fredericia,1,,
+tian,2019,Wien,Austria,vegetarian,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian,1 star,4,100.0,vegetarian,Wien,1,,
+tien hsiang lo,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo,1 star,2,37.5,hang zhou,Taipei,1,,
+tim allen's flitch of bacon,2019,Little Dunmow,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon,1 star,3,62.5,modern british,Little Dunmow,1,,
+tim ho wan (sham shui po),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po,1 star,1,20.0,dim sum,Hong Kong,1,,
+tim's kitchen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen,1 star,2,37.5,cantonese,Macau,1,,
+tin lung heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen,2 stars,3,62.5,cantonese,Hong Kong,2,,
+topolobampo,2019,Chicago,Chicago,mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo,1 star,4,100.0,mexican,Chicago,1,,
+tosca,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519,1 star,3,62.5,italian,Hong Kong,1,,
+trinity,2019,Clapham Common,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity,1 star,3,62.5,modern cuisine,Clapham Common,1,,
+trishna,2019,Marylebone,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna,1 star,3,62.5,indian,Marylebone,1,,
+trois mec,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec,1 star,4,100.0,contemporary,Los Angeles,1,,
+tudor room,2019,Egham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room,1 star,3,62.5,modern cuisine,Egham,1,,
+tuju,2019,São Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju,2 stars,4,100.0,creative,São Paulo,2,,
+tuome,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome,1 star,2,37.5,fusion,New York,1,,
+tyddyn llan,2019,Llandrillo,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan,1 star,3,62.5,classic cuisine,Llandrillo,1,,
+umu,2019,Mayfair,United Kingdom,japanese,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu,2 stars,4,100.0,japanese,Mayfair,2,,
+uncle boons,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons,1 star,2,37.5,thai,New York,1,,
+upper house,2019,Göteborg,Sweden,creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house,1 star,4,100.0,creative,Göteborg,1,,
+upstairs at mikkeller,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller,1 star,4,100.0,innovative,Bangkok,1,,
+urasawa,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa,2 stars,4,100.0,japanese,Los Angeles,2,,
+varoulko seaside,2019,Athína,Greece,seafood,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside,1 star,4,100.0,seafood,Athína,1,,
+vea,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea,1 star,4,100.0,innovative,Hong Kong,1,,
+veeraswamy,2019,Mayfair,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy,1 star,3,62.5,indian,Mayfair,1,,
+vespertine,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine,2 stars,4,100.0,contemporary,Los Angeles,2,,
+vollmers,2019,Malmö,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers,2 stars,4,100.0,creative,Malmö,2,,
+volt,2019,Stockholm,Sweden,creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt,1 star,3,62.5,creative,Stockholm,1,,
+wako,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako,1 star,4,100.0,japanese,San Francisco,1,,
+waku ghin,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin,2 stars,4,100.0,japanese contemporary,Singapore,2,,
+wakuriya,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya,1 star,4,100.0,japanese,San Francisco,1,,
+wallsé,2019,New York,New York City,austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse,1 star,4,100.0,austrian,New York,1,,
+walnut tree,2019,Llanddewi Skirrid,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree,1 star,3,62.5,modern british,Llanddewi Skirrid,1,,
+walter bauer,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer,1 star,4,100.0,classic cuisine,Wien,1,,
+waterside inn,2019,Bray,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn,3 stars,4,100.0,classic french,Bray,3,,
+west house,2019,Biddenden,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house,1 star,3,62.5,modern british,Biddenden,1,,
+white swan,2019,Fence,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan,1 star,3,62.5,modern british,Fence,1,,
+whitegrass,2018,Singapore,Singapore,australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass,1 star,2,37.5,australian,Singapore,1,,
+wild honey inn,2019,Lios Dúin Bhearna Lisdoonvarna,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn,1 star,3,62.5,classic cuisine,Lios Dúin Bhearna Lisdoonvarna,1,,
+wilks,2019,Bristol,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks,1 star,3,62.5,modern british,Bristol,1,,
+wing lei,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei,1 star,2,37.5,cantonese,Macau,1,,
+winteringham fields,2019,Winteringham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields,1 star,3,62.5,modern cuisine,Winteringham,1,,
+woodspeen,2019,Newbury,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen,1 star,3,62.5,modern cuisine,Newbury,1,,
+xin rong ji,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji,1 star,3,62.5,taizhou,Hong Kong,1,,
+ya ge,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge,1 star,2,37.5,cantonese,Taipei,1,,
+yan toh heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen,2 stars,3,62.5,cantonese,Hong Kong,2,,
+yat lok,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok,1 star,1,20.0,cantonese roast meats,Hong Kong,1,,
+yat tung heen (jordan),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan,1 star,2,37.5,cantonese,Hong Kong,1,,
+yauatcha soho,2019,Soho,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho,1 star,3,62.5,chinese,Soho,1,,
+yee tung heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen,1 star,2,37.5,cantonese,Hong Kong,1,,
+ying,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying,1 star,2,37.5,cantonese,Macau,1,,
+ying jee club,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club,2 stars,2,37.5,cantonese,Hong Kong,2,,
+ynyshir,2019,Machynlleth,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir,1 star,3,62.5,creative,Machynlleth,1,,
+yorke arms,2019,Pateley Bridge,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms,1 star,3,62.5,modern cuisine,Pateley Bridge,1,,
+yu yuan,2019,Seoul,South Korea,chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan,1 star,3,62.5,chinese,Seoul,1,,
+yè shanghai (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui,1 star,1,20.0,shanghainese,Hong Kong,1,,
+zero complex,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex,1 star,3,62.5,innovative,Seoul,1,,
+zhejiang heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen,1 star,2,37.5,shanghainese,Hong Kong,1,,
+zi yat heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen,1 star,2,37.5,cantonese,Macau,1,,
+zz's clam bar,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar,1 star,4,100.0,seafood,New York,1,,
+écriture,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture,2 stars,3,62.5,french contemporary,Hong Kong,2,,
+épure,2019,,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure,1 star,3,62.5,french,,1,,
diff --git a/data/clean/visuals_Zina.ipynb b/data/clean/visuals_Zina.ipynb
new file mode 100644
index 00000000..455352ef
--- /dev/null
+++ b/data/clean/visuals_Zina.ipynb
@@ -0,0 +1,574 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3f5e16c0-0376-44df-89e9-602ef0be62d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### used the cleantable_Zina.csv \n",
+    "\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "df = pd.read_csv(\"/Users/ZINA/Desktop/IRONHACK/Week_4/first_project/data/clean/cleantable_Zina.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "55de8214-1d56-4f9f-8467-d893aa7b2203",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+      "       'price_ordinal', 'price_mean', 'cuisine_original', 'major_city',\n",
+      "       'stars_n', 'Review_rating', 'Review_count'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5328e81e-e324-4e7e-96da-a051b28d0f5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hypothesis: Higher-Star Restaurants Have Higher Prices\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "sns.histplot(data=df, x='price_mean', hue='stars', bins=15, palette='Set2', multiple='stack')\n",
+    "plt.title('Hypothesis: Higher-Star Restaurants Have Higher Prices')\n",
+    "plt.xlabel('Price ($)')\n",
+    "plt.ylabel('Count')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "bec290b2-7eaf-4549-b1bf-86134f32cca1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\2713431191.py:4: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.countplot(data=df, x='stars', palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hypothesis: One-Star Restaurants Are More Common\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "sns.countplot(data=df, x='stars', palette='Set2')\n",
+    "plt.title('Hypothesis: One-Star Restaurants Are More Common')\n",
+    "plt.xlabel('Star Rating')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "98c15a64-aa49-4d2b-a68c-7bdf20b7e14c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\428845957.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=avg_price_region.index, y=avg_price_region.values, palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate average price per region\n",
+    "avg_price_region = df.groupby('region')['price_mean'].mean().sort_values(ascending=False)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(x=avg_price_region.index, y=avg_price_region.values, palette='Set2')\n",
+    "plt.title('Average Michelin Restaurant Price by Region')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Average Price ($)')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a24811b8-a561-4c51-bd0f-f3e348dbf419",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\3015102386.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=avg_price_cuisine.index, y=avg_price_cuisine.values, palette='Set3')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate average price per cuisine\n",
+    "avg_price_cuisine = df.groupby('cuisine')['price_mean'].mean().sort_values(ascending=False)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(12,6))\n",
+    "sns.barplot(x=avg_price_cuisine.index, y=avg_price_cuisine.values, palette='Set3')\n",
+    "plt.title('Average Michelin Restaurant Price by Cuisine Type')\n",
+    "plt.xlabel('Cuisine Type')\n",
+    "plt.ylabel('Average Price ($)')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6f14e0ef-e0e7-4686-bdc8-d3f68cc1b536",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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uMUBeuZVk4I033oh3G7oIvdxzRMer9o1ahjQmULX0SgChC0cFV8HBmI75+N6ftpHY804JRLRdtZpo7J8uPlW+IUOGnJdoI5THuN5jQvOdheq7I7HHofaJxmNqfKRaoXXTeDO1IgUnhLkc2q86JzUFw9tvv+2CBbVQBWcyTAxVUCmo0k3lV8u/WmiUyOFCNA2G3pO+H+rWretadHR8qRIgvgqApP4cEnNeiQImBYl6TyqrKihU1qZNm8banj4zLyiNKzHjSoHUhkAKCAPe4PCLpXtWLb0GXuumHz9llnr++efdhaMuhi4lS9eFqIYx7kWbBu+rFj24Zly1p3GpNUcZ3Dx+yqZB3KohV81ucK2wuiF5jycFbUfzd+kCILgFJKlfR10HBw8e7Fq64iYRuBAFYCqbarb9PO9C21PNcGIunFOKBqtrsLvmJlKKeB03Krda9HTcX+w4utRzRIPsNcBeF+i6UPfE161Mx3x8Xer8tGCqW5TOD7VmBJfpYhfeF5LU579fulBWl9rgzHiXexwqQFH3Md10LqiV6t1333WZTi9U8RTf95foM46bKEOf9/Dhw23KlCkuWNP7uJzU+163RiXOuNhno+NASVr0+h4lxojvOzWp+Tmv1BKmrpGqmFBFh45bVQKoFS14e/reVqtwKCrtgJRA1z4glVM2LM3Xoh+qBx98MMH1VFMel3dx7aWq9eYUSaofYS/rVfCPvi4O1NUm+Mdz8eLFsVIbq2tQ3DTpfsqmlgnV7ivbUzBdYOsHP/j1L4deR12Zgvv+a5yKxomoa5fXretyqauZLpCDL5YSQxcqCgyUaS5u7fSltERovJbGdMQ32a0+F733lKZ5tDQWTtnZNNbJK7dahtRqEF/XKC972eWcI16LT/B+1d/xpdrWMa9g20sPLrog9bIDJkZ8r6dsjPp8LlVSn/9+6VjVMauAZPny5ec97r3XxB6HGi8Ud/teJU7ctPfx0Xit4PTl6nqnfRz3+0Pb1E1ZFDW3m1qDEjOfm4Lz+M5DbwyWxgoGfzbxfS46DuJuQ98/CbVuJqXEnlfBrVL6rtf4OY19Cu7W521P5dbvWVz6TFPquAQuBy1SQCqi2k5dgOlHZffu3S6IUo23Wj40OPlCk1vqYlrdljQQXOurv7m6oqhrlObN8S7wNEh5zJgxriVHP97qWnap/eo1VkTbVjcVlVdpiVULHJyiXUGCAix18dAPqbolqdtScPIHv2VT7bO6dqklQeMZNLeTumbpwlrdSuJu+1Ip5a9qt9XFSvNrqaZa70UXxHqviRkjkRj6vOLOtZMY2tfaB7ow0eB8dXVTDbBSJmsMmbqB+aEuZDrOlCLcS1GuiyW1xuh9a1/HHRuTElQ2jRtRymtdmGt6AHUlevzxx93Fq2q8dcGmc0nLdUGuVoDLOUfUlU+PPf300+7iUi0ruqiObwySxuqotUutFkr7rtfR9pTuXWOEEkOfgWr11eqm8qrbp7ah8XBqrbkU+jxFyVJUNl2kB6emTg5qAdS5qkoIL6W2Kl/UJUxjsLTvE3sc6rtFwbG6ZuozVIufggwFx944xoudP/rclc5egZfOaY1Fi69LoVql9NlLYrv1qVuexvPpM9Txo8okpTJXxYy+S4IT++g9qrVGx43OXX3v6bjTPlCPBHWT02evAFPrXWjaBj+UvEUtXHH5Oa88+n7XPtJNvw1xWxT1matFWd9L6pKp6RmUvEctg/r8VSkRPOcUEBZSOm0ggP+Xuta7KV230mI3btzYpRIPTrOdUCrs2bNnx9x5550uVa+er/+VujduCmGlRa5cuXJMhgwZYqXwVarZKlWqxPtxJJT+/Isvvojp169fTMGCBV0qYaX0Dk4D7Rk+fLhLla601zfeeKNLYx1fetuEyhY3bbQcOXLEpSnW+8yYMWNM+fLlXSrf4LTfou0o7XNcCaVlj2v37t0xHTp0iMmfP7/br9WqVYs37fGlpD+/kMSkP/d89NFHLjW+9u8VV1zh9uusWbMu+nrxfQbar/pMy5Ur596v3vcNN9wQ8/rrr8dK25zY9OeJfd34JPTZyYABA9zjOhZFZRs6dKg7hr39ULt27ZiBAwfGHDp0KEnOEaWnb9SokUvprP3SqVOnQCr9uMeEUuaXLVvWvU7NmjVd2uuE0p/Hl4Jax/Grr77q1tf70ec7depUX9uI+xkpLfaTTz4ZU6BAAZey/WKXABf6Tkjs5xS3DKLvCKVBVzn03rSf9PzgdO+JOQ6//vrrmNtuu819/2idkiVLxjz22GMxO3fuvGB5g/eZvptKlCjhynHTTTe5zzM+2qbSmFeoUCEmsaZNm+ZS1SuFvI4ZlVHvR5+BvleC/f777zH169d336Mqm/fdpJT33vePttGkSRO3btzvr8RMoRHfPkjoptTniT2vgun7Xc9/9NFHE3zt9957z21D7zVnzpzuO1Wp4Xfs2BFYh/TnCBdR+ielgzkAAIDUSl3VlL1PLaEafwUAwhgpAACACxg3bpzr1qbubgDgYYwUAABAPDROVVkxX3nlFTduKG5GPwBpG137AAAA4tGwYUOXIEKJFpQkR5OWA4CHQAoAAAAAfGKMFAAAAAD4RCAFAAAAAD6RbMLMoqOjbceOHW7yxaioKL/7EAAAAECE0OxQR44ccRNkp0uXcLsTgZSZC6JKlCiRnJ8PAAAAgFRs+/btVrx48QQfJ5Aycy1R3s7KlStX8n06AAAAAFKVw4cPu0YWL0ZICIGUUhf+X3c+BVEEUgAAAACiLjLkh2QTAAAAAOATgRQAAAAA+EQgBQAAAAA+MUYKAAAASAHnzp2zM2fOsO+TWcaMGS19+vSXvR0CKQAAACCZ5ynatWuXHTx4kP2eQvLkyWOFCxe+rDlkCaQAAACAZOQFUQULFrRs2bJd1sU8/Aexx48ftz179rj7RYoUsUtFIAUAAAAkY3c+L4jKly8f+z0FZM2a1f2vYEqfw6V28yPZBAAAAJBMvDFRaolCyvH2/+WMUSOQAgAAAJIZ3fnCf/8TSAEAAACATwRSAAAAAOATgRQAAACAgPbt21urVq3YIxdBIAUAAAAgyZ0+fTqi9yqBFAAAAJAGff3111atWjWXDlyp2Bs1amTPPPOMffzxx/bdd9+5hAy6zZ07163ft29fq1Chgst4V7ZsWXvxxRdjZb0bMGCA1axZ0z744AMrU6aMZcmSJcHXOXbsmIU75pECAAAA0pidO3fa/fffb8OGDbO77rrLjhw5Yj/++KO1bdvWtm3bZocPH7axY8e6dfPmzev+z5kzp40bN86KFi1qv/32m3Xq1Mkt69OnT2C7GzdutIkTJ9qkSZPc/EwJvY4mxg13BFIAAABAGqMA5+zZs3b33XdbqVKl3DK1Golajk6dOmWFCxeO9ZwXXngh8Hfp0qXt6aefti+//DJWIKXufOPHj7cCBQq4+z///HOCrxPu6NoHAAAApDE1atSwW2+91QU19957r73//vt24MCBCz5nwoQJduONN7oAK0eOHC6wUutVMAVLXhB1qa8TLgikAAAAgDRG3e5mzZpl06ZNs8qVK9uoUaOsYsWKtmXLlnjXX7RokT344IPWvHlzmzp1qv3yyy/2/PPPn5dQInv27Jf1OuGEQAoAAABIg5RIQi1MAwcOdIFRpkyZ7JtvvnH/nzt3Lta6CxcudK1NCp6uueYaK1++vG3duvWyXifcMUYKAIAQ6T1tfMTs2+HN2qZ0EQAkoSVLltjs2bPttttus4IFC7r7e/futUqVKtnJkydtxowZtn79epdlL3fu3C5wUjc+jYm69tpr7T//+U+igqELvU64I5ACAAAA0phcuXLZ/PnzbcSIES5Dn1qbhg8fbs2aNXMtTkp5rv+PHj1qc+bMsZYtW1rPnj2tW7duLhHF7bff7tKfK+X5pb5OuIuKiYTcg5dJH6oi7UOHDrkPGwCApECLFIC41Nqj8UHB8ywhdX0OiY0NGCMFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPGfw+AQAAAEDS6z1tfLLu1uHN2vpaf/78+fbPf/7TVqxYYTt37rRvvvnGWrVqdcmvP27cOOvRo4cdPHjQwhEtUgAAAAAu6tixY1ajRg0bPXp0qtpbMTExdvbs2WR/XQIpAAAAABfVrFkzGzx4sN11112J3lu//vqr3XzzzZYzZ07LlSuX1a5d25YvX25z5861Dh062KFDhywqKsrdBgwY4J7zySef2DXXXOOeU7hwYXvggQdsz549gW3quVp/2rRpbnuZM2e2BQsWJPsnSCAFAAAAICQefPBBK168uC1btsx1CXz22WctY8aMdsMNN9iIESNccKVugro9/fTT7jlnzpyxl19+2QVh3377rf3555/Wvn3787atbb322mu2bt06q169erJ/goyRAgAAABAS27Zts2eeecauuuoqd798+fKBx3Lnzu1altTqFOyRRx4J/F22bFkbOXKkXXvttXb06FHLkSNH4LFBgwZZ48aNU+yTo0UKAAAAQEj06tXLHn30UWvUqJFrPdq0adNFn6OWqxYtWljJkiVd974GDRoEgrJg6v6XkgikAAAAAITEgAEDbM2aNXb77bfbDz/8YJUrV3bZ/i6U0KJJkyauy99nn33mugR6658+fTrWutmzZ0/RT41ACgAAAEDIVKhQwXr27GkzZ860u+++28aOHeuWZ8qUyc6dOxdr3d9//9327dvnWq9uuukm1yUwONFEakIgBQAAAOCiNEZp5cqV7iZbtmxxf8ftcuc5ceKEdevWzWXZ27p1q/3000+uhalSpUru8dKlS7ttzp492/7++287fvy4686nAGvUqFG2efNmmzx5sks8kRqRbAIAAABIBfxOkJvclLZcqcyDxz9Ju3bt3OS6caVPn961LrVt29Z2795t+fPndy1SAwcOdI8rc9/jjz9ubdq0ceu99NJLriugtvXcc8+5JBNXX321vf7669ayZUtLbaJiNINVGnf48GGXNUR57NUfEwCApNB72viI2ZGp/QIPCBcnT550LTllypSxLFmypHRx0qyTF/gcEhsb0LUPAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfMvh9AgAAAICkt+edPsm6Wwt2GeZr/SFDhtikSZPs999/t6xZs9oNN9xgQ4cOtYoVK17S6w8YMMC+/fZbW7lypYUjWqQAAAAAXNS8efOsa9eutnjxYps1a5adOXPGbrvtNjt27FiK7r1z585ZdHR0sr8ugRQAAACAi5o+fbq1b9/eqlSpYjVq1LBx48bZtm3bbMWKFQk+Z+7cuXbddddZ9uzZLU+ePHbjjTfa1q1b3XMHDhxov/76q0VFRbmblskbb7xh1apVc88pUaKEPfHEE3b06NHANrWetjV58mSrXLmyZc6c2ZUjodcKFbr2AQAAAPDt0KFD7v+8efPG+/jZs2etVatW1qlTJ/viiy/s9OnTtnTpUhc0tWnTxlavXu2Cs++//96tnzt3bvd/unTpbOTIkVamTBnbvHmzC6T69Oljb7/9dmDbx48fd90KP/jgA8uXL58rQ82aNeN9rVAhkAIAAADgS3R0tPXo0cO1+lStWjXedQ4fPuyCrTvuuMOuvPJKt6xSpUqBx3PkyGEZMmSwwoULx3qetuspXbq0DR482B5//PFYgZS6Feq+WsZk//79F3ytUKBrHwAAAABfunbt6lqUvvzyywTXUSuRugI2adLEWrRoYW+99Zbt3LnzottWC9Wtt95qxYoVs5w5c9rDDz9s+/btc61QnkyZMln16tUv+7UuB4EUAAAAgETr1q2bTZ061ebMmWPFixe/4Lpjx461RYsWuQx/EyZMsAoVKrhkFQn5888/XauSgqSJEye68VejR492j6m7nkdZA+N22/P7WpeLQAoAAADARcXExLgg6ptvvrEffvjBjWFKjFq1alm/fv1s4cKFrhvg559/HmhVUsa9YAqc1G1w+PDhVqdOHRcM7dixI9GfTkKvFQoEUgAAAAAS1Z3v008/dcFJzpw5bdeuXe524sSJeNffsmWLC2rUSqTseTNnzrQNGzYExi5p/JPW0TxSf//9t506dcrKlSvnxj+NGjXKJZr45JNPbMyYMRct28VeK+KSTSRmUq+GDRu6nPXBHnvssVg7VOkOu3Tp4poXNWitXbt2btsavAYAAACEA78T5Ca3d955J3B9HrdLncYnxZUtWzZ3nf/xxx+7MU5FihRxwZiu5aV169YuFrj55pvt4MGDge0o/bliAgVG9evXd9f1bdu2PW/7fl4rFKJi1EaXQpo2bWr33XefXXvttS494nPPPecGra1du9blf/c+KDXpDRo0KNaOypUrl/tbzYFKdahsH//85z/doDLtaKU+fPXVVxNVDmUUUbpFZfrwtgsAwOXqPW18xOzE4c0ufBEDIHFOnjzpWk/ULS5LlizstlT4OSQ2NkjRJhvljQ+mybUKFizo+kYq+gwOnOKmRfSo2U6Bl7J7FCpUyAVVL7/8svXt29cGDBjg+l4CAAAAQMSOkUpoUq/PPvvM8ufP7waMqYkvOPWh+kFq5mMFUR6lPVQkuWbNmnhfR/0v9XjwDQAAAAASK0Nqn9TrgQcesFKlSlnRokVt1apVrqVp/fr1rj+laIBbcBAl3n09Fh/1sxw4cGBI3w8AAACAyJUhtU3qtWDBgljLO3fuHPhbLU8aOKYJujZt2hSYtdgvtWr16tUrcF8tUiVKlLiM0gMAAABIS9KF26Re119/vft/48aN7n+Nndq9e3esdbz7CY2rypw5sxs4FnwDAAAAgLAIpC5lUi/lmRe1TEndunXtt99+sz179gTWmTVrlguOKleuHMLSAwAAAEirMqR0dz5N6PXdd98FJvUSpRvUvFLqvqfHmzdvbvny5XNjpHr27Oky+lWvXt2te9ttt7mA6eGHH7Zhw4a5bbzwwgtu22p5AgAAAICIapHSpF7K1Ke5otTC5N0mTJjgHlfqcqU1V7B01VVXWe/evd3EXVOmTAlsI3369K5boP5X69RDDz3k5pEKnncKAAAAACKmRepicwErAcS8efMuuh1l9fvvf/+bhCUDAAAAgFSebAIAAAAAwkmqSX8OAAAApGVvfxp7GqBQe+Kher6H5ej2559/uvtVqlSx/v37W7NmzS7p9QcMGGDffvttIJlcuKFFCgAAAMBFaZqi1157zVasWGHLly+3W265xe68805bs2ZNiu69c+fOWXR0dLK/LoEUAAAAgItq0aKFy6Zdvnx5q1Chgr3yyiuWI0cOW7x4cYLPmTt3rl133XWWPXt2y5Mnj9144422detWGzdunA0cONB+/fVXi4qKcjctkzfeeMOqVavmnqOcCU888YQdPXo0sE2tp21NnjzZZe9Wpu5t27Yl+FqhQtc+AAAAAL5bgb766is7duyYy5wdn7Nnz1qrVq2sU6dO9sUXX9jp06dt6dKlLmhq06aNrV692qZPn+6ydHtTIEm6dOls5MiRbo7ZzZs3u0CqT58+9vbbbwe2ffz4cRs6dKh98MEHbpqkvHnzWs2aNeN9rVAhkAIAAACQKL/99psLnE6ePOlao7755hvXKhSfw4cPu6mO7rjjDrvyyivdskqVKgUe1/MzZMhghQsXjvW8Hj16BP4uXbq0DR482B5//PFYgdSZM2fc/Ro1arj7+/fvv+BrhQJd+wAAAAAkSsWKFV1yiCVLlliXLl2sXbt2tnbt2njXVStR+/btrUmTJq5b4FtvvWU7d+686GuoherWW2+1YsWKWc6cOe3hhx+2ffv2uVYoj+abrV69+mW/1uUgkAIAAACQKApgypUrZ7Vr17YhQ4a4FiEFLQkZO3asLVq0yG644QabMGGCG1t1oTFVygioViUFSRMnTnSJLUaPHu0eU3c9T9asWc/rtuf3tS4XgRQAAACASxIdHW2nTp264Dq1atWyfv362cKFC61q1ar2+eefB4IyjbUKpsBJ2xw+fLjVqVPHBUM7duxIdHkSeq1QYIwUAAAAgItSgKI5o0qWLGlHjhxxQYoy5c2YMSPe9bds2WLvvfeetWzZ0ooWLWrr16+3DRs2WNu2bQPjn7SOugoqtbq68am1S+OfRo0a5bro/fTTTzZmzJiLlu1irxUKBFIAAABAKuB3gtzktmfPHheYaOxR7ty5Xfc7BVGNGzeOd/1s2bLZ77//bh9//LEb41SkSBHr2rWrPfbYY+7x1q1b26RJk+zmm2+2gwcPuq55Guek9OfKyKfArX79+q4L4cUCoou9VihExcTExFgap4wiOhiU6SNXrlwpXRwAQIToPW28RYrhzUJXqwukJcp2p9YTpfbOkiVLShcnzTp5gc8hsbEBY6QAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAgGRGvrfw3/8EUgAAAEAyyZgxo/v/+PHj7PMU5O1/7/O4FMwjBQAAACST9OnTW548edycTN78R1FRUez/ZGyJUhCl/a/PQZ/HpSKQAgAAAJJR4cKF3f9eMIXkpyDK+xwuFYEUAAAAkIzUAlWkSBErWLCgnTlzhn2fzNSd73JaojwEUgAAAEAK0MV8UlzQI2WQbAIAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAgHAKpIYMGWLXXnut5cyZ0woWLGitWrWy9evXx1rn5MmT1rVrV8uXL5/lyJHDWrdubbt37461zrZt2+z222+3bNmyue0888wzdvbs2WR+NwAAAADSihQNpObNm+eCpMWLF9usWbPszJkzdtttt9mxY8cC6/Ts2dOmTJliX331lVt/x44ddvfddwceP3funAuiTp8+bQsXLrSPP/7Yxo0bZ/3790+hdwUAAAAg0kXFxMTEWCqxd+9e16KkgKl+/fp26NAhK1CggH3++ed2zz33uHV+//13q1Spki1atMjq1Klj06ZNszvuuMMFWIUKFXLrjBkzxvr27eu2lylTpou+7uHDhy137tzu9XLlyhXy9wkASBt6TxtvkWJ4s7YpXQQASBaJjQ1S1RgpFVby5s3r/l+xYoVrpWrUqFFgnauuuspKlizpAinR/9WqVQsEUdKkSRO3A9asWRPv65w6dco9HnwDAAAAgMRKNYFUdHS09ejRw2688UarWrWqW7Zr1y7XopQnT55Y6ypo0mPeOsFBlPe491hCY7MUZXq3EiVKhOhdAQAAAIhEqSaQ0lip1atX25dffhny1+rXr59r/fJu27dvD/lrAgAAAIgcGSwV6Natm02dOtXmz59vxYsXDywvXLiwSyJx8ODBWK1Sytqnx7x1li5dGmt7XlY/b524MmfO7G4AAAAAEHYtUspzoSDqm2++sR9++MHKlCkT6/HatWtbxowZbfbs2YFlSo+udOd169Z19/X/b7/9Znv27AmsowyAGhhWuXLlZHw3AAAAANIK3y1S6gYXFRUVaDlSa5Cy6ilo6dy5s+/ufHrud9995+aS8sY0adxS1qxZ3f8dO3a0Xr16uQQUCo6efPJJFzwpY58oXbpe++GHH7Zhw4a5bbzwwgtu27Q6AQAAAEgVLVIPPPCAzZkzx/2toKVx48YumHr++edt0KBBvrb1zjvvuDFKDRs2tCJFigRuEyZMCKzz5ptvuvTmmohXKdHVXW/SpEmBx9OnT++6Bep/BVgPPfSQtW3b1ndZAAAAACBk80hdccUVbgLdihUr2siRI13Q89NPP9nMmTPt8ccft82bN1u4YR4pAEAoMI8UAISfkM0jpXmdvC5z33//vbVs2TIwv9POnTsvp8wAAAAAEBZ8B1JVqlSxMWPG2I8//uiSOjRt2tQt37Fjh+XLly8UZQQAAACA8A6khg4dau+++64b13T//fdbjRo13PLJkyfbddddF4oyAgAAAEB4Z+1TAPX333+7voMaL+VRxr7s2bMndfkAAAAAIPxbpG655RY7cuRIrCBKlJ68TZs2SVk2AAAAAIiMQGru3Ll2+vTp85afPHnSjZsCAAAAgEiX6K59q1atCvy9du3awOS5cu7cOZs+fboVK1Ys6UsIAAAAAOEaSNWsWdOioqLcTd374sqaNauNGjUqqcsHAAAAAOEbSG3ZssU0d2/ZsmVt6dKlVqBAgcBjmTJlsoIFC1r69OlDVU4AAAAACL9AqlSpUu7/6OjoUJYHAAAAACIv/bls2LDB5syZY3v27DkvsOrfv39SlQ0AAAAAIiOQev/9961Lly6WP39+K1y4sBsz5dHfBFIAAAAAIp3vQGrw4MH2yiuvWN++fUNTIgAAAACItHmkDhw4YPfee29oSgMAAAAAkRhIKYiaOXNmaEoDAAAAAJHYta9cuXL24osv2uLFi61atWqWMWPGWI937949KcsHAAAAAOEfSL333nuWI0cOmzdvnrsFU7IJAikAAAAAkc53IKWJeQEAAAAgLfM9RgoAAAAA0rpLmpD3r7/+ssmTJ9u2bdvs9OnTsR574403kqpsAAAAABAZgdTs2bOtZcuWVrZsWfv999+tatWq9ueff1pMTIxdffXVoSklAAAAAIRz175+/frZ008/bb/99ptlyZLFJk6caNu3b7cGDRowvxQAAACANMF3ILVu3Tpr27at+ztDhgx24sQJl8Vv0KBBNnTo0FCUEQAAAADCO5DKnj17YFxUkSJFbNOmTYHH/v7776QtHQAAAABEwhipOnXq2IIFC6xSpUrWvHlz6927t+vmN2nSJPcYAAAAAEQ634GUsvIdPXrU/T1w4ED394QJE6x8+fJk7AMAAACQJvgKpM6dO+dSn1evXj3QzW/MmDGhKhsAAAAAhP8YqfTp09ttt91mBw4cCF2JAAAAACDSkk1o3qjNmzeHpjQAAAAAEImB1ODBg908UlOnTrWdO3fa4cOHY90AAAAAINL5TjahTH3SsmVLi4qKCiyPiYlx9zWOCgAAAAAime9Aas6cOaEpCQAAAABEaiDVoEGD0JQEAAAAACI1kJo/f/4FH69fv/7llAcAAAAAIi+Qatiw4XnLgsdKMUYKAAAAQKTzHUjFnUPqzJkz9ssvv9iLL75or7zySlKWDQAAAGGs97TxFkmGN2ub0kVAOAdSuXPnPm9Z48aNLVOmTNarVy9bsWJFUpUNAAAAACJjHqmEFCpUyNavX59UmwMAAACAyGmRWrVqVaz7mj9KE/O+9tprVrNmzaQsGwAAAABERiClYEnJJRRABatTp4599NFHSVk2AAAAAIiMQGrLli2x7qdLl84KFChgWbJkScpyAQAAAEDkBFKlSpUKTUkAAAAAIFIDKTl27JjNmzfPtm3bZqdPn471WPfu3ZOqbAAAAAAQGYGU5oxq3ry5HT9+3AVUefPmtb///tuyZctmBQsWJJACAAAAEPF8pz/v2bOntWjRwk3MmzVrVlu8eLFt3brVateuba+//npoSgkAAAAA4RxIrVy50nr37u2STKRPn95OnTplJUqUsGHDhtlzzz0XmlICAAAAQDgHUhkzZnRBlKgrn8ZJSe7cuW379u1JX0IAAAAACPcxUrVq1bJly5ZZ+fLlrUGDBta/f383RuqTTz6xqlWrhqaUAAAAABDOLVKvvvqqFSlSxP39yiuv2BVXXGFdunSxvXv32rvvvhuKMgIAAABAeLdIXXPNNYG/1bVv+vTpSV0mAAAAAIisFqlbbrnFDh48eN7yw4cPu8cAAAAAINL5DqTmzp173iS8cvLkSfvxxx+TqlwAAAAAEP5d+1atWhX4e+3atbZr167A/XPnzrkufsWKFUv6EgIAAABAuAZSNWvWtKioKHeLrwufJucdNWpUUpcPAAAAAMI3kNqyZYvFxMRY2bJlbenSpVagQIHAY5kyZXKJJzRBLwAAAABEukQHUqVKlXL/R0dHh7I8AAAAABB5ySY+/vhj+89//hO436dPH8uTJ4/dcMMNtnXr1qQuHwAAAABExoS8Gg8lixYtsn/96182bNgwy58/v/Xs2TMUZQQAAACA8J6Qd/v27VauXDn397fffmv33HOPde7c2W688UZr2LBhKMoIAAAAAOHdIpUjRw7bt2+f+3vmzJnWuHFj93eWLFnsxIkTSV9CAAAAAAj3FikFTo8++qjVqlXL/vjjD2vevLlbvmbNGitdunQoyggAAAAA4d0iNXr0aKtbt67t3bvXJk6caPny5XPLV6xYYffff38oyggAAAAA4d0ipQx9SjAR18CBA5OqTAAAAAAQWS1S8uOPP9pDDz3kUp7/73//c8s++eQTW7BgQVKXDwAAAADCP5BSd74mTZq4FOg///yznTp1yi0/dOiQS40OAAAAAJHOdyA1ePBgGzNmjL3//vuWMWPGwHKlP1dgBQAAAACRzncgtX79eqtfv/55y3Pnzm0HDx5MqnIBAAAAQOQEUoULF7aNGzeet1zjo8qWLetrW/Pnz7cWLVpY0aJFLSoqyk3wG6x9+/ZuefCtadOmsdbZv3+/Pfjgg5YrVy6XCKNjx4529OhRv28LAAAAAEIXSHXq1MmeeuopW7JkiQtsduzYYZ999pk9/fTT1qVLF1/bOnbsmNWoUcOlVE+IAqedO3cGbl988UWsxxVEaQ6rWbNm2dSpU11w1rlzZ79vCwAAAABCl/782WeftejoaLv11lvt+PHjrptf5syZXSD15JNP+tpWs2bN3O1CtG21gsVn3bp1Nn36dFu2bJldc801btmoUaPcJMGvv/66a+kCAAAAgBRvkVIr1PPPP++61K1evdoWL17sJud9+eWX7cSJE0lewLlz51rBggWtYsWKrsVr3759gccWLVrkuvN5QZQ0atTI0qVL51rMEqJMg4cPH451AwAAAICQziMlmTJlssqVK9t1113nsve98cYbVqZMGUtK6tY3fvx4mz17tg0dOtTmzZvnWrDOnTvnHt+1a5cLsoJlyJDB8ubN6x5LyJAhQ1xyDO9WokSJJC03AAAAgMiW6EBKrTj9+vVzrT+aiNdLDDF27FgXQL355pvWs2fPJC3cfffdZy1btrRq1apZq1at3BgodeNTK9Xl0PvQvFfebfv27UlWZgAAAACRL9FjpPr372/vvvuu6zq3cOFCu/fee61Dhw6ua59ao3Q/ffr0IS2ssgLmz5/fZQ3UGC2NndqzZ0+sdc6ePeu6HSY0rsobd6UbAAAAAIQ0kPrqq69cNzu1EGlsVPXq1V3Q8uuvv7pxU8nhr7/+cmOkihQp4u7XrVvXzV21YsUKq127tlv2ww8/uGQY119/fbKUCQAAAEDak8FPEOMFK1WrVnUtOurKdzlBlOZ7Cp6TasuWLbZy5Uo3xkm3gQMHWuvWrV3r0qZNm6xPnz5Wrlw5a9KkiVu/UqVKbhyVUrKPGTPGzpw5Y926dXNdAsnYBwAAACDFx0gpwYMSTAQndciRI8dlvfjy5cutVq1a7ia9evVyf6sboboJrlq1yrWAVahQwU20q0Duxx9/jNUtT3NYXXXVVa6rn9Ke16tXz957773LKhcAAAAAJEmLVExMjLVv3z4QxJw8edIef/xxy549e6z1Jk2alNhNWsOGDd12EzJjxoyLbkMtV59//nmiXxMAAAAAki2QateuXaz7Dz300GW/OAAAAABEdCClNOcAAAAAgMuYkBcAAAAA0ioCKQAAAADwiUAKAAAAAHwikAIAAACAUARSV199tR04cMD9PWjQIDt+/Ljf1wEAAACAtBVIrVu3zo4dO+b+HjhwoB09ejTU5QIAAACA8E5/XrNmTevQoYPVq1fPTaD7+uuvW44cOeJdt3///kldRgAAAAAIv0Bq3Lhx9tJLL9nUqVMtKirKpk2bZhkynP9UPUYgBQAAACDSJSqQqlixon355Zfu73Tp0tns2bOtYMGCoS4bAAAAAIRvIBUsOjo6NCUBAAAAgEgNpGTTpk02YsQIl4RCKleubE899ZRdeeWVSV0+AAAAAAj/eaRmzJjhAqelS5da9erV3W3JkiVWpUoVmzVrVmhKCQAAAADh3CL17LPPWs+ePe211147b3nfvn2tcePGSVk+AAAAAAj/Fil15+vYseN5yx955BFbu3ZtUpULAAAAACInkCpQoICtXLnyvOVaRiY/AAAAAGmB7659nTp1ss6dO9vmzZvthhtucMt++uknGzp0qPXq1SsUZQQAAACA8A6kXnzxRcuZM6cNHz7c+vXr55YVLVrUBgwYYN27dw9FGQEAAAAgvAOpqKgol2xCtyNHjrhlCqwAAAAAIK24pHmkPARQAAAAANIi38kmAAAAACCtI5ACAAAAAJ8IpAAAAAAglIHUmTNn7NZbb7UNGzawowEAAACkWb4CqYwZM9qqVatCVxoAAAAAiMSufQ899JB9+OGHoSkNAAAAAERi+vOzZ8/aRx99ZN9//73Vrl3bsmfPHuvxN954IynLBwAAAADhH0itXr3arr76avf3H3/8cd5kvQAAAAAQ6XwHUnPmzAlNSQAAAAAg0tOfb9y40WbMmGEnTpxw92NiYpKyXAAAAAAQOYHUvn37XAr0ChUqWPPmzW3nzp1ueceOHa13796hKCMAAAAAhHcg1bNnT5cGfdu2bZYtW7bA8jZt2tj06dOTunwAAAAAEP5jpGbOnOm69BUvXjzW8vLly9vWrVuTsmwAAAAAEBktUseOHYvVEuXZv3+/Zc6cOanKBQAAAACRE0jddNNNNn78+Fgpz6Ojo23YsGF28803J3X5AAAAACD8u/YpYFKyieXLl9vp06etT58+tmbNGtci9dNPP4WmlAAAAAAQzi1SVatWdRPx1qtXz+68807X1e/uu++2X375xa688srQlBIAAAAAwrlFSnLnzm3PP/980pcGAAAAACI1kDpw4IB9+OGHtm7dOne/cuXK1qFDB8ubN29Slw8AAAAAwr9r3/z586106dI2cuRIF1Dppr/LlCnjHgMAAACASOe7Rapr165u8t133nnH0qdP75adO3fOnnjiCffYb7/9FopyAgAAAED4tkht3LjRevfuHQiiRH/36tXLPQYAAAAAkc53IHX11VcHxkYF07IaNWokVbkAAAAAILy79q1atSrwd/fu3e2pp55yrU916tRxyxYvXmyjR4+21157LXQlBQAAAIBwCqRq1qxpUVFRFhMTE1imiXjjeuCBB9z4KQAAAACwtB5IbdmyJfQlAQAAAIBICqRKlSoV+pIAAAAAQCRPyLtjxw5bsGCB7dmzx6Kjo2M9pjFUAAAAABDJfAdS48aNs8cee8wyZcpk+fLlc2OnPPqbQAoAAABApPMdSL344ovWv39/69evn6VL5zt7OgAAAACEPd+R0PHjx+2+++4jiAIAAACQZvkOpDp27GhfffVVaEoDAAAAAJHYtW/IkCF2xx132PTp061atWqWMWPGWI+/8cYbSVk+AAAAAIiMQGrGjBlWsWJFdz9usgkAAAAAiHS+A6nhw4fbRx99ZO3btw9NiQAAAAAg0sZIZc6c2W688cbQlAYAAAAAIjGQeuqpp2zUqFGhKQ0AAAAARGLXvqVLl9oPP/xgU6dOtSpVqpyXbGLSpElJWT4AAAAACP9AKk+ePHb33XeHpjQAAAAAEImB1NixY0NTEgAAAACI1DFSAAAAAJDW+W6RKlOmzAXni9q8efPllgkAAAAAIiuQ6tGjR6z7Z86csV9++cWmT59uzzzzTFKWDQAAAAAiI5BS+vP4jB492pYvX54UZQIAAACAtDFGqlmzZjZx4sSk2hwAAAAARH4g9fXXX1vevHmTanMAAAAAEDld+2rVqhUr2URMTIzt2rXL9u7da2+//XZSlw8AAAAAwr9FqlWrVnbnnXcGbpqc96WXXrLVq1db586dfW1r/vz51qJFCytatKgLzr799ttYjytI69+/vxUpUsSyZs1qjRo1sg0bNsRaZ//+/fbggw9arly53GTBHTt2tKNHj/p9WwAAAAAQuhYpBU1J5dixY1ajRg175JFHXEAW17Bhw2zkyJH28ccfu7TrL774ojVp0sTWrl1rWbJkcesoiNq5c6fNmjXLZRDs0KGDC+g+//zzJCsnAAAAAFxWIJWUlKBCt/ioNWrEiBH2wgsvuJYvGT9+vBUqVMi1XN133322bt06l3Z92bJlds0117h1Ro0aZc2bN7fXX3/dtXQBAAAAQIp17UuXLp2lT5/+grcMGZIuLtuyZYsbe6XufJ7cuXPb9ddfb4sWLXL39b+683lBlGh9lXXJkiUJbvvUqVN2+PDhWDcAAAAASKxERz7ffPNNgo8poFEXvOjoaEsqCqJELVDBdN97TP8XLFgw1uMK5pQ90FsnPkOGDLGBAwcmWVkBAAAApC2JDqS87nXB1q9fb88++6xNmTLFjVUaNGiQhYN+/fpZr169AvfVIlWiRIkULRMAAACACJ9HaseOHdapUyerVq2anT171lauXOkSQpQqVSrJCla4cGH3/+7du2Mt133vMf2/Z8+eWI+rPMrk560Tn8yZM7ssf8E3AAAAAAhJIHXo0CHr27evlStXztasWWOzZ892rVFVq1a1pKYsfQqG9BrBLUca+1S3bl13X/8fPHjQVqxYEVjnhx9+cF0MNZYKAAAAAFK0a59SkQ8dOtQFN1988UW8Xf380nxPGzdujJVgQq1bGuNUsmRJ69Gjhw0ePNjKly8fSH+uTHyay0oqVapkTZs2da1jY8aMcenPu3Xr5jL6kbEPAAAAQIoHUhoLpUlx1Rqlbny6xWfSpEmJfvHly5fbzTffHLjvjVtq166djRs3zvr06ePmmtK8UGp5qlevnkt37s0hJZ999pkLnm699VaXra9169Yu8QUAAAAApHgg1bZtW4uKikrSF2/YsKGbLyohej0lsLhQEgu1XjH5LgAAAIBUGUiphQgAAAAAcIlZ+wAAAAAgLSOQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAACBU80gBAAAAadmed/pYJCnYZVhKFyGs0SIFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAAAAD4RCAFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAAAAD4RCAFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAAAAD4lMHvEwAgMXpPGx8xO2p4s7YpXQQAAJDK0CIFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAAEUgAAAAAQWrRIAQAAAIBPBFIAAAAA4BOBFAAAAAD4RCAFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAAAABEUiA1YMAAi4qKinW76qqrAo+fPHnSunbtavny5bMcOXJY69atbffu3SlaZgAAAACRL1UHUlKlShXbuXNn4LZgwYLAYz179rQpU6bYV199ZfPmzbMdO3bY3XffnaLlBQAAABD5MlgqlyFDBitcuPB5yw8dOmQffvihff7553bLLbe4ZWPHjrVKlSrZ4sWLrU6dOilQWgAAAABpQapvkdqwYYMVLVrUypYtaw8++KBt27bNLV+xYoWdOXPGGjVqFFhX3f5KlixpixYtuuA2T506ZYcPH451AwAAAICICKSuv/56GzdunE2fPt3eeecd27Jli91000125MgR27Vrl2XKlMny5MkT6zmFChVyj13IkCFDLHfu3IFbiRIlQvxOAAAAAESSVN21r1mzZoG/q1ev7gKrUqVK2b///W/LmjXrJW+3X79+1qtXr8B9tUgRTAEAAACIiBapuNT6VKFCBdu4caMbN3X69Gk7ePBgrHWUtS++MVXBMmfObLly5Yp1AwAAAICIDKSOHj1qmzZtsiJFiljt2rUtY8aMNnv27MDj69evd2Oo6tatm6LlBAAAABDZUnXXvqefftpatGjhuvMptflLL71k6dOnt/vvv9+NberYsaPropc3b17XqvTkk0+6IIqMfQAAAADSbCD1119/uaBp3759VqBAAatXr55Lba6/5c0337R06dK5iXiVia9Jkyb29ttvp3SxAQAAAES4VB1Iffnllxd8PEuWLDZ69Gh3AwAAAIDkElZjpAAAAAAgNSCQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8y+H0CAAAAgPD39qcLLFI88VC9ZH9NWqQAAAAAwCcCKQAAAADwiUAKAAAAAHxijFQI9J423iLJ8GZtU7oIAAAAQKpCixQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAAAAD4RCAFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+EUgBAAAAgE8EUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAAAAD4RCAFAAAAAD4RSAEAAACATwRSAAAAAOATgRQAAAAA+EQgBQAAAAA+ZfD7BAAAkPbseaePRZKCXYaldBEAhDlapAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAHwikAIAAAAAn5iQFwAugolIAQBAXLRIAQAAAIBPBFIAAAAA4BOBFAAAAAD4RCAFAAAAAD4RSAEAAABAWg2kRo8ebaVLl7YsWbLY9ddfb0uXLk3pIgEAAACIUBGR/nzChAnWq1cvGzNmjAuiRowYYU2aNLH169dbwYIFU7p4YS+SUj8X7DIspYsApLi3P11gkeSJh+qldBEQhjgPAFyuiGiReuONN6xTp07WoUMHq1y5sguosmXLZh999FFKFw0AAABABAr7FqnTp0/bihUrrF+/foFl6dKls0aNGtmiRYvifc6pU6fczXPo0CH3/+HDh5OkTKeOn7BIcuTE/9tX4S5LEn3GSFvnQSSdA3IiwzGLJEn13R0KnAepF+dB8oikc0D4PUgbvwWH/29bMTExF1wvKuZia6RyO3bssGLFitnChQutbt26geV9+vSxefPm2ZIlS857zoABA2zgwIHJXFIAAAAA4WL79u1WvHjxyG2RuhRqvdKYKk90dLTt37/f8uXLZ1FRUSlatrRKkX+JEiXcAZsrV66ULg6Q7DgHAM4DgN+D1EHtTEeOHLGiRYtecL2wD6Ty589v6dOnt927d8darvuFCxeO9zmZM2d2t2B58uQJaTmROAqiCKSQlnEOAJwHAL8HKS937tyRn2wiU6ZMVrt2bZs9e3asFibdD+7qBwAAAABJJexbpETd9Nq1a2fXXHONXXfddS79+bFjx1wWPwAAAABIahERSLVp08b27t1r/fv3t127dlnNmjVt+vTpVqhQoZQuGhJJXS1feuml87pcAmkF5wDAeQDwexBewj5rHwAAAAAkt7AfIwUAAAAAyY1ACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAKR60dHRKV0EAIi8eaQQeTZu3GhTpkyxnTt32s0332xXX30184IhTVm7dq3NmTPHunbtmtJFAVLUhg0bLH369Fa2bFk+CeD/aPaiqKiowP9IGbRIIdVZvXq1XXfddTZp0iSbP3++3XXXXdazZ0+bNm1aShcNSBYrV6602rVr27Fjx2ItZ9o/pDW//vqrVa1a1WbMmJHSRQFShf3799uOHTvsjz/+cPe9IIrfh5RBIIVU5cSJE9avXz976KGHbO7cubZ48WL79ttvbd++fTZs2DD75ptvUrqIQMgvHOvVq+daovr06RPrMWodkdYqFOrWrWvdu3e3Ll26pHRxgBS3atUqa9Cggd1yyy2uwlnXSrpOEq91CskrKoa9jlTk3Llzdu2111rr1q3t+eefDyzXF8WQIUPs9OnTNmDAALv++utTtJxAKGzZssWqV6/ufhzfeecdO3PmjI0ZM8a2bdvm/n7sscfsyiuvtEyZMvEBIOK781WuXNn69+9vL774op09e9Zmz57tzoUKFSpYpUqVrGDBgildTCDZ/O9//3PXPvp9aNq0qbseUiWDzoNOnTrZww8/7Najq1/yYowUUg2d/KdOnbIiRYrY33//HQis1De+Tp069vTTT9vjjz/uWqj0ZcKXBSLN999/b/nz57ccOXLYrl27rF27dnbkyBHLnDmz+xGdPHmyvfrqq3bvvfe68wKIRKo0+OCDDyxDhgyui6u0bNnSBVEHDhxwXZt0DjzxxBPutwFIK61R+m145plnLF++fG6ZxtF27tzZ3nvvPcuWLZurhKbnQvKiax9SDZ38+iJo0aKFvf322zZz5kx3sehlarrpppusW7duNnr0aNu7dy9fFog4qlXs0aOH/fTTT25cSLp06WzixIkuwFJ/+Fq1armW2uPHj6d0UYGQyZgxo6tdVwusxseWKlXKLfviiy9s+/btbvzsL7/8YuPGjXPr07EGaYW++w8dOuT+VouUKp7ff/99d36oF4OGQQjnRPIhkEKq4Z34anVq27at3XPPPe6CUheTnnLlylnp0qWpjUfE8SoMnnrqKXfsK1vl4MGD3Q+lV8P44Ycf2tatW23WrFkpXFogtFSRoECqYcOGVqVKFXvttdesWrVq7vegWbNm1qtXL/voo49cYEUNPNICdXU9ePCgffLJJ+6+unir9bZw4cKuUmHJkiX26aefusc4J5IPXfuQagSf+PrRVOKJ2267zdWy1K9f30qUKOEyN+mHNDi4AiKBjmkFU/pfF4lLly51F5PeY6po0Biq8uXLu3FSQKTTOCh16f7rr79cJZp450ju3LnduZArV66ULiYQcjru1TL75ptvusrmYsWK2aOPPuq6v2oIRMmSJV3l2/r16/k0khmBFFKc98MYTP1/1Vyt4EldO9QvWAMqdSGp2vg8efKkWHmBpKDxgBr7FMwLmFSpoIxMwbRM3fxy5szpWqmAtEDBkoIor6LN+61YuHChFS9enN4JSBO84/7uu++2TZs2ufGB6tqn/73xsmqdomIh+ZG1D8lOk+yqO4YGDTdq1CjwJZBQ8ohFixa5gfb60rjhhhtc1z4gnKnWUBUFykSpvu0Xo8oD3ZTB78cff7QaNWokSzmBlK5Ui0sXkerSp3G0Ohe8VlsgEoc6xHdNtHv3bpdc4qWXXrI2bdq4CgXNOaguf8uWLbOrrroqBUqcdhFIIdmzzij7kmri9WWgmnWlt23SpInlzZvXfXnox1TBFVn5EKnngLJOqkVqypQpdvvtt5+3TvCxf/LkSTef1Lx589wPpdKjA5FAvwHKxKcMlUoyJBf63l+zZo3roaDnfPnll1azZs1kLjGQPD0V1Lp0sUq2+fPn2z//+U83DEJdXRVY8fuQ/AikkGyUaU9jndQ03bFjR8uSJYsbC6ILy3/84x9uAtICBQoE1lftu+aU8tLfApEw2a4mGH3kkUdcdiVVGKhmMWvWrLFmp/f+VoYmZbJUMHX48GHmzUHE+O2339x8OJofSoGRatF1YahzwTsH4muhWrBggRsPohsQSVRRoDnTNOWFfhuee+45l94/eN5AnS8aF+WdI/pt0LVUfF3FkTwYsY9kDaR00iuQKlu2rBUtWtTVKqqFSulslXVGNSuyZ88e1/f35Zdfdl8QQLj7+eefXQp/VR7861//cj+QapHasWNHrBnpvSBK6w0bNswFXPqhZPJRRNJku40bN7Y77rjDvv76a1uxYoUdPXrUzYcjccdDjRo1yr766iv3d7169QiiEJHnhIYuqDJZ01xoLKwyVmreQFU0eBREiX439Juh3wZhkvaUQyCFZKOmatWmeHPgeEGTMvQp24yy8+nLRHTR+Pvvv9vQoUOpZUHYU8paBVGaJ0opzUUVBRUqVHCVBfF1Z9J9XUACkUTf/6+88oq1atXKBg0a5DLz6TzQuaFkQnFp8l1lKtO4KAVbQCQaP368q1x79913XQWaKg7eeustV+mm8YDqBusZPny4662zevXqwDLSnaccAikkGw2Q15go9eMVdeHwWpv0haFMfRp8Lwq49ONasWJFPiGEPWWZ1Jxo+gEUBU6qbVd6f9XG//3334HlXsuU1lVlgjeDPRAJVIOumzLxeYmGvN8HzZGmSgdVuonOBY2d/eGHH9xE7MreCkQir2LZu/6RJ5980lU6KJj65ptvAo8/8MAD7vpI3b6R8gikEDLKIqO+vhrb4VFti/oB64tA1KfX+9LQ+Ck9J7j5Gghnqk1ft26da2nVZIrBdBGpyXfVbUPjAb1aRW9siOTPnz9Fyg2EoiVKXbtVgaAWJs0PJV7Fgc4HBVjq0uQNslf3JWVrVaZWdQcHIpXG/ClDsY55Xf/ouBdNSq1kQ88880ygi58qpGfPns18gqkEgRRCYu3atW4sVIMGDVzXjc8++8wt199qfVIq53vvvdfVPHr94DUuKnv27C6w8n5cgXClbhdK76/0tNWqVXPdNTRxoihY0t/qwqofyunTp8fqB++dE3TXQKScC0oopEmmVfOu3giiCgPvGPfGCXrHvi4cvWQUQKTTJLsaG9W6dWs3LlZjnlTxIBo7eMUVV9jy5csD6we35iJlEUghJEGUWpeqVKniah3vu+8+69Chg/3yyy+uxlHJJdTfffHixS5Vp744dLGppuvnn3/e1cZwAYlwPwc0UPjWW291CVXUPUNp/lXbGPeHUN37lMFMySiASKMeCBofqLluypQpEwiigiegFl04KshSBYN+B9SVT+Nn6b6ESPPHH39Y37593XWRKpbVY0HHv4Y9qHJB10PqzeAlklDPHVUyk1AidSL9OZKUTv7777/fpbLVF4RHySRUKz9y5MjAMnX708B77wujS5cu53V/AsKNxjupckC1iyNGjHDLdLHYvHlzF0zpQlLjnkqUKBF4jh7TQPq5c+cGuvcB4U5dtdUz4corr3QD5kXj/lTTrrFPwSnMNYZQU2DccsstLohSN6err746BUsPhKaSTdn5NA2GgqPvv//eTfHSvn17e/jhh23q1KkuAZGyHKvLt7q5aozgBx98YEuWLCFjZSrEQBQkKXXV02Dhe+65x9335gFRTaQCpuAB9eoLr6x8wesB4U5BUNOmTQPngKjCYMaMGW7iUQVaaq194YUXXCpnr+uGKho4BxBJ1LtAY6OUkU8tTZp8Wr8DCqZ0Djz66KNuTkHReppT8K+//iKIQkTSuCcl1FI3V80fKBs3bnS/BcrQqhZZ/Rao4kHBlLq2qkufgqn//Oc/BFGpFFeuSFKFChWyTz/91HXlEG9MSLFixWKN+9DfwUkoqIFHpFBrU7du3ax8+fLuvrr2qcuG/tcAYY0X1MWk/vYoFbR+PIFIokq19evXu8oDjXkS1az/+9//dr8RuoDUPFJy3XXXuW6uqn2nJQqRSF3zlMY8ePJ1Za/U+FlVLCgF+rRp09xY8s8//9xNUD1nzhzXU4FzIvWiax9CJriVST+YGiipQfWiWhn1++3evTsZ+hDRlNJZg4eDfwg1EanOjcmTJ6do2YBQ0oWiMrQq++Sff/7pKhiaNGniHlPLU79+/VxKc3UD10WmejR4GfuASKJKZV0TKbmQhjWowlnHvJdgZfPmza4FSl2+J0yY4J4T3/yCSH1okULoDq6ggcTefdE4EQ0m1kB80pwj0pUqVSoQROmHVONDdPGoyReBSKaLwN69e9vYsWNd1yQvpbMo+YR6MCgZhfc7QBCFSOP1ylFyIR3f7dq1c4m1NBWM1ztH6yi9vyqY1UKrc0IIosIDgRRCyguk9EOpmpbXX3/dNWOrdUoTMAJpiX40X331VTcGROn/gUh3zTXXuO5KonEh3kWiqAVKk657F5tApGXnU8KhnTt3BpZpShiNDe/Zs6fr5hqcwVXjxnU+KAkFwgfJJhBSXiuUamLef/99y5Urly1YsID+vkhzvvrqK5s3b54bK6V51LwxVECk03gojfNQRtdHHnnEJVZR65S6tur3gJYoRBolkVBmvgMHDriu3b169QpMsK4MxcpoqcQS6vqtzJbquaDfCFUuEEiFF8ZIIVmoBUqDiTUxIynOkRapJn7QoEE2YMAAN5gYSGuUeEJjQzSHoCoSnnjiCatatWpKFwtIUgqSNP5bXbmvvfZaNzZQc2oq4UqBAgXcOnpM54Lmk1KLlFqjlIBrypQpVDSHGQIpJOuXCzUtSMsYTA/8/xeRQrp/RCKlMde4QGVw1eS6ylJ53333nRdMiZKwbNu2zaX/V0utMhwjvBBIAQAAACGqOFYmPnVtVfIVtUKpm9/Zs2dtx44dzA8V5hgjBQAAACQRL4hSIhW1vKplypsOQNn4evTo4ZJvaYyU5o/Kli0bWfrCFC1SAAAAQAgogPLmi1LL1MMPP+zSnW/atMmWLVtmNWvWZL+HMQIpAAAAIMRTwag1SnNorly50mWy1LgohDe69gEAAAAhogBK3fyUbGLOnDkukCKIigxMyAsAAACEWJUqVeznn3+26tWrs68jBF37AAAAgGTo4qfWKUQOWqQAAACAECOIijwEUgAAAADgE4EUAAAAAPhEIAUAAAAAPhFIAQAAAIBPBFIAAAAA4BOBFAAgzStdurSNGDEize8HAEDiEUgBACJK+/btXZph3TJlymTlypWzQYMG2dmzZxN8zrJly6xz587JWk4AQHjLkNIFAAAgqTVt2tTGjh1rp06dsv/+97/WtWtXy5gxo/Xr1y/WeqdPn3bBVoECBfgQAAC+0CIFAIg4mTNntsKFC1upUqWsS5cu1qhRI5s8ebJrrWrVqpW98sorVrRoUatYsWK8XfsOHjxojz32mBUqVMiyZMliVatWtalTpwYeX7Bggd10002WNWtWK1GihHXv3t2OHTuWIu8VAJAyaJECAEQ8BTz79u1zf8+ePdty5cpls2bNinfd6Ohoa9asmR05csQ+/fRTu/LKK23t2rWWPn169/imTZtci9fgwYPto48+sr1791q3bt3cTa1gAIC0gUAKABCxYmJiXOA0Y8YMe/LJJ13Qkz17dvvggw9cl774fP/997Z06VJbt26dVahQwS0rW7Zs4PEhQ4bYgw8+aD169HD3y5cvbyNHjrQGDRrYO++841qwAACRj659AICIo254OXLkcEGNWpfatGljAwYMcI9Vq1YtwSBKVq5cacWLFw8EUXH9+uuvNm7cOLd979akSRPXkrVly5aQvScAQOpCixQAIOLcfPPNrnVIAZPGQmXI8P9+7tQidbFugBdy9OhRN35K46LiKlmy5GWUGgAQTgikAAARR8GS0p5fiurVq9tff/1lf/zxR7ytUldffbUbM3Wp2wcARAa69gEAEERjnerXr2+tW7d2CSnUXW/atGk2ffp093jfvn1t4cKFLrmEugFu2LDBvvvuO3cfAJB2EEgBABDHxIkT7dprr7X777/fKleubH369LFz584FWqzmzZvnWqyUAr1WrVrWv39/14UQAJB2RMUopREAAAAAINFokQIAAAAAnwikAAAAAMAnAikAAAAA8IlACgAAAAB8IpACAAAAAJ8IpAAAAADAJwIpAAAAAPCJQAoAAAAAfCKQAgAAAACfCKQAAAAAwCcCKQAAAADwiUAKAAAAAMyf/w+jLFb85V8+4AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribution of Michelin Restaurant Prices by Star Level\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.countplot(data=df, x='price_mean', hue='stars', palette='Set2')\n",
+    "plt.title('Distribution of Michelin Restaurant Prices by Star Level')\n",
+    "plt.xlabel('Price')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a39a7370-ddc2-4f25-b93d-87c7a364fb4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "sns.countplot(data=df, x='region', hue='price_mean', palette='Set3')\n",
+    "plt.title('Price Distribution of Michelin Restaurants by Region')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2c4246fa-594b-4a3b-baac-3130f77b2593",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\518418672.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data=avg_price_stars, x='stars', y='price_mean', palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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1qUjPdNOzj8zuY6ZvCK4e05mW2imqYb2Cq/5XqIgXro6m1K7wD878gzY+amepnX08wYFQOwR1n6IDns7MddY5duxYd6BPrcsd7UzU3kxtudRGSKFbZ7OjRo1yw9Q+0wfpWY9aVrVzijdu7HJGltRklHZ8alunNss6ICqE+7DMWfW56aFwo9oJPXTAV/tpBVmdSKVGpT8aXyFLBxydIOjkUCWcqv2IbQ+nwJnRdR8ZKnQCp/ax+j61XWl9BZ+hbn2CizpTCxHazygAvfvuu67USW0RdXKoboDUPvJwdIKtdpmahrbhChUquPWkdoCRF46kFmK1rlIT77etUkS1a1SNiErzgmVV2I7XzvBIfgP6rKAvT60XtSFV6ZtKNFXymVW07lu3bu1+J6NHj3ZBWSejqhHT9xC5XDpxUqhS4YSCq/7Xb0ftgQPZtT1lp/R8T1oubVexvaEEdOJ1JPtGXduRnuPskexzI2X3MdM3BFePaQPWAUglPNpJ6uxaF2zF27DjVe3pIqugmjzoA1U/+Fi62lEbalDaeqSlIzoLVgDSgSg9G78+V2FJD13QoQvE1GBdDe0PV5qlKjE9NK4OjKp6UulLWgfY7KD1q52PLgSLLN1QaVdmqTmGdtY6q4/dEWc1/c6041OTkngX3aQmKHGK14NFsi/zkVBQVHDV+jrcNqOLI7UdalyV0AWCq/SzmvYNuvhIF8M8+eST7kKs4DtSiEzP9qhqXDU50UMXJiqI6qKtYLtKbVl1ANX2qhPJyBJfBdd4JZ2x/UQHtULpodK3GTNmuAO1freBzDZxCBxu36cgrqZAeqj0S81FtO/JyuCqE3GVVKt0PrLkMV6Vvvab6l1A1dgKuaqdU7iuWLFiUm1P2jfG2w8e6b5Rv81EF+Jk1T7yaB0zfUEbV8+pmk47aV05qQ01sm+5SAq1kXfL0ZXLKg0Kdqra6apUQgfQyAOGNiaVIKjqKxAE2MzegEAHT1Xl60AWb2NVtUggtusalS6ojZN2tqm1X9X6iC0lCdpPRVZPZmd3WLGCkiyd+UbS1buZpZ2R1qUOzrHLq+ex6+5I6ECgKkaFHpXgpZd2uAo2qqJWlXjsPCbzMqdF1bSqho1HJViRTW9S22aCk8zIZdHfuvo+u6jpjL5DfZ9q+6kr5zXsmWeeCQft9G6PKpFTaWzkdnW4ZVXwiyw5VQ8HsR36q+RaJ8qx7RBjt53DibdeJfZq8ozSssUul5YntumB1qkCYuR6yQrxlkufHRv+AwouanerkvHvv//ePU+27Un7Rm1HkTfnUA3Eq6++mulpqgRc09RJUix9f+qdI1mkZx+Z3cdM31Di6jm1P1TbGJ1Vq4ogta5XdHBRFaEu7NDOVDtwdd0RWZWi6i0FWbU3UhcbQXdYajMV2Xei2nCJLkRR10oqqVG1VEY6/R42bJgrJVBbIZUSa8PSzkoNzNU+R3+L2ueo+YPa56hZhC54UUmRus7RGWg8Ct86yKkLHwUuXXCiLkJ0QIwM4BntDutIaJ1px6P1rh1L0B2WStsyW4qtZVM7Ky2HAoCqgbVOtDyqslS3Kqr+zSrqWiUz1B2afnv6bWqeVLqj+f3vf/+b4TtJHe1lTiu4qgpW36Wqn1Vtq4OigpiqxTVv2j6D+VYbOFWpa361rei3rypfvaZ51omlfqM6OMW2dc1qqj5XlbFqa3RRktrB6js67bTT3PaoUiC1l9fBX224FXpE26lCrn7PKnlVV1hBN1Cx+wd1yaNQooOu9hPaZlXyp3WlEnyVSupztW9Se8RIKr3VPkL/q/RaITbYVtJD61FhQG1PdbBW+1ydgAf9bGaWlk37Jy2Hgql+yzo5UbtjXfimC3UU5jWOLo5R86SspP2hgoj2t0FhhfZtCsrxTjqC/n71+wrCT7JtTzoGqZmbqvZ1sWrQHZZKlHUcyMy+Ub9vlUqrxDnoKkvXaKiGQ79XLWvQlWMySM8+MjuPmd5JdLcGiO5WJLUunNSdUWpdXKiLE713yJAhKV4Lut5QF1ajRo0KVapUyXUdcvbZZ7tuPGKp65wWLVq47rPUlUeHDh1CP/74Y4rx1N3ICSec4LrKiuyyJLZ7pNS6QJENGza4cTVP+fLlC5UvX951y/Pss8+Gx3nmmWdcVyHqlkbzXa1atdBdd93luntJrTushQsXuu5xKleu7N6jLr7at28f+vbbb4+oO6zUuruJ993Fdoclu3btctNQNy9FixYNderUKbRs2TI33rBhw1K8V12txPuc2PmdPHly6KyzznLdv+hRq1Yt9zmadnp+P/GkNg+x0tMdlixZssR1L1SyZEnXjVvNmjVD/fv3z9Zlzkh3WOn93EgHDhwIjR8/3n2P+hz91goXLhw644wz3Pa2b9++qPHffffdUJ06dVw3Q5HrSNtXmzZt3G/iuOOOC3Xv3j3chVzketTyaFmzYp9y8OBBty3p8ffff7thv/76q+v2Ttuhtkdt39pu1IVWYPDgwaEmTZq471H7CK13dZmkbq0Cmp66BVK3W+rmKHI7mDBhguteS+tK79U8xttW1LWPuv9RNz/qJuiKK65w3Qql9/sTdf8X/OY0ncsvvzy0du3aI/oN/PTTT25/pGXXa/pO9D1rn6SuwjSv+o7099ixY4/oO0ptu502bVqoXr16bjuqUqVKaPjw4aHnn38+1d/r1Vdf7V7Tbyw16dmesmr+4x0L1BWWjkn6XZx44omuO7gxY8a4aa9fvz7qvfG6Y9Pn6BFpx44doX79+rmu/NRFo7Ytde82cuTI8O818viYXqnNQ6TDTTez+8isPGb6Lpf+SXR4xpFRtaLutKMztNgrLjVMZ28qTT1aJVFIP51Nq1ROJQ6ZuQsSAByL1KG+mq+oVDnZbumNxKKNq+d03jFhwgR35XK8bkKQPGJv4SlqOqArfVWtCQA5Uey+Uc2pdBGoqs8JrYhFG1dPqb2O2vCozYva7ah7GiQ3tbdTP4q6olv9ceoCHj3UpkntIwEgJ9J1FWo7res01LZahTHqFzm1foWRsxFcPaWrCHWBgy74uPfee13/iEhuupBH3RwNGjTIVX+phFwXvekiNwDIqXQRmS6aUuf6uhhLFykpvFIThXho4woAAAAv0MYVAAAAXiC4AgAAwAvHfBtX3WZTdw5Rx7tHeqtSAAAAZE8vSbphkG7uod52cmxwVWjlim0AAIDkt2bNGnc3uhwbXINbnGlF6DaAAAAASC7qAk0FjWndmvaYD65B8wCFVoIrAABA8kqrWScXZwEAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC/kTfQMAACAwxv7yhxWEZLKTdeclZDPpcQVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAF/ImegYA5Bwbn7470bMApFC25wjWCuAJSlwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsJDa4HDx60/v3728knn2yFChWyatWq2aBBgywUCoXH0d8PPPCAVahQwY3Tpk0bW758eSJnGwAAADktuA4fPtyefvppe/LJJ23p0qXu+YgRI+yJJ54Ij6PnY8aMsXHjxtm8efOsSJEi1rZtW9u7d28iZx0AAABHWV5LoK+++souvvhia9eunXtepUoVe/311+2bb74Jl7Y+9thjdv/997vx5KWXXrJy5crZ1KlTrXPnzomcfQAAAOSUEtfmzZvbjBkz7Oeff3bPv//+e5szZ45deOGF7vmKFSts/fr1rnlAoESJEta0aVObO3du3Gnu27fPtm/fHvUAAACA/xJa4tq3b18XLGvVqmV58uRxbV4ffvhhu/rqq93rCq2iEtZIeh68Fmvo0KE2cODAozD3AAAAyDElrm+99Za9+uqr9tprr9nChQvtxRdftJEjR7r/M6tfv362bdu28GPNmjVZOs8AAADIgSWud911lyt1DdqqnnbaabZq1SpXatqlSxcrX768G75hwwbXq0BAz08//fS40yxQoIB7AAAA4NiS0BLX3bt3W+7c0bOgJgOHDh1yf6ubLIVXtYMNqGmBehdo1qzZUZ9fAAAA5NAS1w4dOrg2rZUrV7ZTTz3VFi1aZKNHj7Zu3bq513PlymW33367DR482GrUqOGCrPp9rVixonXq1CmRsw4AAICcFFzVX6uC6E033WQbN250gbRHjx7uhgOBu+++23bt2mU33HCDbd261c466yz76KOPrGDBgomcdQAAABxluUKRt6k6BqlpgbrQ0oVaxYsXT/TsADnaxqfvTvQsACmU7Tki6dfK2FfmJHoWgCg3XXOWJSKvJbSNKwAAAJBeBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOCFvImeAV/d+eFLiZ4FIMqoC69ljQAAjmmUuAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXsib0Tfs27fP5s2bZ6tWrbLdu3fb8ccfb2eccYadfPLJ2TOHAAAAQEaC65dffmmPP/64vffee3bgwAErUaKEFSpUyLZs2eLCbNWqVe2GG26wG2+80YoVK8bKBQAAwNFvKtCxY0e78sorrUqVKvbJJ5/Yjh07bPPmzfb777+7Utfly5fb/fffbzNmzLBTTjnFpk+fnrVzCQAAgBwvXSWu7dq1s8mTJ1u+fPnivq7SVj26dOliP/74o61bty7Hr1gAAAAkILj26NEj3ROsU6eOewAAAABJ16vAwYMHM/3eP/74w6655horU6aMazN72mmn2bfffht+PRQK2QMPPGAVKlRwr7dp08Y1TQAAAEDOkqHg+sUXX0S1X924caOdddZZVqBAAWvYsGGGA+Vff/1lLVq0cE0QPvzwQ9fMYNSoUVaqVKnwOCNGjLAxY8bYuHHjXG8GRYoUsbZt29revXsz9FkAAADIQcFVJZ8rV64MPx8wYIDt37/fpk6dahUrVrTbbrstQx8+fPhwq1Spkk2cONGaNGniutS64IILrFq1auHS1scee8xd+HXxxRdbvXr17KWXXrK1a9e6zwQAAEDOkaHgumzZMleyGpg2bZorEW3fvr2NHTvWvv766wx9uN7fqFEju/zyy61s2bKuP9jx48eHX1+xYoWtX7/eNQ8IqBuupk2b2ty5c+NOU11zbd++PeoBAACAHHJx1nXXXef+V5+tQ4YMcf20qjusTZs2uRJQPQ4dOuS6yerWrZsb9/nnn09zur/99ps9/fTTdscdd9i9995r8+fPt1tvvdXy58/veihQaJVy5cpFvU/Pg9diDR061AYOHJiexQIAAMCxFlxVlS+6aKp169bWs2dPV9K6bdu2cEBVn64ff/xxugJrQGFXJa4Kw6IS1yVLlrj2rAqumdGvXz8XhAMqcVVzBAAAAOSgpgIKrL169bKaNWvafffdZ3369Am/phsTqJ1qRqingNius2rXrm2rV692f5cvX979v2HDhqhx9Dx4LZYuFCtevHjUAwAAADnolq9y0003Wa1atWzRokXWrFkza968efg1Ve+rtDMj1KOA2s1G+vnnn+2kk05yf+tiLQVU3ZHr9NNPD5egqncBhWgAAADkHBkKrtKqVSv3iKW+WDOqd+/eLvyqqcAVV1xh33zzjT377LPuIbly5bLbb7/dBg8ebDVq1HBBtn///q4Hg06dOmX48wAAAHCMB9ddu3a5/lPTK73jN27c2KZMmeJKah966CEXTNX91dVXXx0e5+6773bTu+GGG2zr1q2u39iPPvrIChYsmO75AQAAQA5p41q9enUbNmyYrVu3LtVx1Oeqbk5w4YUXuhsGpJe60lq8eLG7ocDSpUute/fuUa+r1FWhVr0IaJxPP/3UTjnllHRPHwAAADmoxHXmzJmuu6oHH3zQ6tev73oCUHW9Sj119yvd8Ur9qubNm9eVnvbo0SP75xwAAAA5SrqCq3oRmDx5srvaf9KkSe7Wr1999ZXt2bPHjjvuuPCNA1TamidPnuyfawAAAOQ4Gbo4q3LlynbnnXe6BwAAAJC0/bgCAAAAiUJwBQAAgBcIrgAAAPACwRUAAABeILgCAADg2A2u6g5Lt3ht1qyZ/fHHH27Yyy+/bHPmzMnq+QMAAAAyF1zVn2vbtm2tUKFCtmjRItu3b58bvm3bNhsyZEhGJwcAAABkT3AdPHiwjRs3zt1wIF++fOHhLVq0sIULF2Z0cgAAAED2BNdly5bZOeeck2J4iRIlbOvWrRmdHAAAAJA9wbV8+fL2yy+/pBiu9q1Vq1bN6OQAAACA7Amu3bt3t9tuu83mzZtnuXLlsrVr19qrr75qffr0sZ49e2Z0cgAAAEC65LUM6tu3rx06dMhat25tu3fvds0GChQo4IJrr169Mjo5AAAAIHuCq0pZ77vvPrvrrrtck4GdO3danTp1rGjRohmdFAAAAJB9wVXdXh08eNBKly7tAmtgy5YtljdvXitevHhGJwkAAABkfRvXzp072xtvvJFi+FtvveVeAwAAAJIiuOqirPPOOy/F8HPPPde9BgAAACRFcNWdsv7+++8Uww8cOGB79uzJqvkCAAAAjiy4NmnSxJ599tkUw3U3rYYNG2Z0cgAAAED2XJylW762adPGvv/+e9cllsyYMcPmz59vn3zySUYnBwAAAGRPiWuLFi1s7ty5VqlSJXdB1nvvvWfVq1e3H374wc4+++yMTg4AAADInhJXOf30093dsgAAAICkCq7bt28P98+qvw+HflwBAACQsOBaqlQpW7dunZUtW9ZKlizp7p4VKxQKueG6OQEAAACQkOD62WefuTtlyeeff57lMwEAAABkSXBt2bKl+1/9t86aNcu6detmJ554YnreCgAAABz9XgXy5s1rjzzySNwbEAAAAABJ1R1Wq1atXKkrAAAAkNTdYV144YXWt29fW7x4sbtTVpEiRaJe79ixY1bOHwAAAJC54HrTTTe5/0ePHp3iNXoVAAAAQNIE10OHDmXPnAAAAABZFVxXrlxp06dPtwMHDrieBk499dSMvB0AAADI/uCq/lvbt29ve/bs+f9vzJvXnn/+ebvmmmsy/+kAAABAVvcq0L9/fzv//PPtjz/+sM2bN1v37t3t7rvvTu/bAQAAgKMTXJcsWWJDhgyxChUquFvAqj/XjRs3uhALAAAAJE1w3b59ux133HHh54ULF7ZChQrZtm3bsmveAAAAgMxdnPXxxx9biRIlonoYmDFjhiuNDdCPKwAAABIeXLt06ZJiWI8ePcJ/048rAAAAEh5c6b8VAAAAXrRxBQAAABKJ4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAwLEbXLdu3WrPPfec9evXz7Zs2eKGLVy40N0OFgAAAEh4P67yww8/WJs2bdyNCFauXGndu3e30qVL2zvvvGOrV6+2l156KVtmFAAAADlbhktc77jjDuvatastX77cChYsGB5+0UUX2ezZs7N6/gAAAIDMBdf58+dH3S0rcMIJJ9j69eszOjkAAAAge4JrgQIFbPv27SmG//zzz3b88cdndHIAAABA9gTXjh072kMPPWQHDhxwz3PlyuXatt5zzz122WWXZXRyAAAAQPYE11GjRtnOnTutbNmytmfPHmvZsqVVr17dihUrZg8//HBGJwcAAABkT68C6k1g+vTpNmfOHNfDgEJsgwYNXE8DAAAAQNIE18BZZ53lHgAAAEBSBtcxY8bEHa62ruoeS80GzjnnHMuTJ09WzB8AAACQueD66KOP2p9//mm7d++2UqVKuWF//fWXFS5c2IoWLWobN260qlWr2ueff26VKlXK6OQBAACArLk4a8iQIda4cWN3A4LNmze7h7rCatq0qT3++OOuh4Hy5ctb7969MzppAAAAIOtKXO+//36bPHmyVatWLTxMzQNGjhzpusP67bffbMSIEXSNBQAAgMSWuK5bt87+/vvvFMM1LLhzVsWKFW3Hjh1ZM4cAAABAZoLreeed5275umjRovAw/d2zZ09r1aqVe7548WI7+eSTWcEAAABIXHCdMGGClS5d2ho2bOhu/6pHo0aN3DC9JrpISzcqAAAAABLWxlUXXukGBD/99JO7KEtq1qzpHpGlsgAAAEBS3ICgVq1a7gEAAAAkbXD9/fffbdq0aa7rq/3790e9Nnr06KyaNwAAACDzwXXGjBnWsWNHd5MBNReoW7eurVy50kKhkDVo0CCjkwMAAACy5+Ksfv36WZ8+fVzPAbrFq/p0XbNmjbVs2dIuv/zyjE4OAAAAyJ7gunTpUrv22mvd33nz5rU9e/a4XgQeeughGz58eEYnBwAAAGRPcC1SpEi4XWuFChXs119/Db+2adOmjE4OAAAAyJ42rmeeeabNmTPHateubRdddJHdeeedrtnAO++8414DAAAAkiK4qteAnTt3ur8HDhzo/n7zzTetRo0a9CgAAACA5AiuBw8edF1h1atXL9xsYNy4cdk1bwAAAEDm2rjmyZPHLrjgAvvrr78y8jYAAADg6F+cpX5bf/vttyP/ZAAAACA7g+vgwYNdP67vv/++rVu3zrZv3x71AAAAAJLi4iz1JCC6e1auXLnCw3XnLD1XO1gAAAAg4cH1888/z/KZAAAAALI8uOrWrgAAAEDSt3GVL774wq655hpr3ry5/fHHH27Yyy+/7G5MAAAAACRFcJ08ebK1bdvWChUqZAsXLrR9+/a54du2bbMhQ4ZkxzwCAAAAmetVQDcdGD9+vOXLly88vEWLFi7IAgAAAEkRXJctW2bnnHNOiuElSpSwrVu3ZtV8AQAAAEcWXMuXL2+//PJLiuFq31q1alXLrGHDhrnutG6//fbwsL1799rNN99sZcqUsaJFi9pll11mGzZsyPRnAAAAIAcF1+7du9ttt91m8+bNc0Fz7dq19uqrr7qbEvTs2TNTMzF//nx75plnrF69elHDe/fube+9955NmjTJZs2a5T7r0ksvzdRnAAAAIId1h9W3b187dOiQtW7d2nbv3u2aDRQoUMAF1169emV4Bnbu3GlXX321azOr9rMBXew1YcIEe+2116xVq1Zu2MSJE6127dr29ddf25lnnpnhzwIAAEAOKnFVKet9991nW7ZssSVLlrgQ+eeff9qgQYMyNQNqCtCuXTtr06ZN1PAFCxbYgQMHoobXqlXLKleubHPnzk11eurlgNvQAgAAHHsyXOL6yiuvuOr6woULW506dY7ow9944w3XE4GaCsRav3695c+f30qWLBk1vFy5cu611AwdOtQGDhx4RPMFAACAY6DEVe1Oy5Yta//617/sgw8+sIMHD2bqg9esWePayqp9bMGCBS2r9OvXzzUzCB76HAAAAOTA4Lpu3TpXUqomA1dccYVVqFDBVfd/9dVXGZqOmgJs3LjRGjRoYHnz5nUPXYA1ZswY97dKVvfv35+iiy31KqCeDVKj9rbFixePegAAACAHBleFyvbt27uSUgXPRx991FauXGnnnXeeVatWLd3T0cVdixcvtu+++y78aNSokbtQK/hbNziYMWNGVB+yq1evtmbNmmV0tgEAAJDT2rhGUjtX3f71r7/+slWrVtnSpUvT/d5ixYpZ3bp1o4YVKVLE9dkaDL/++uvtjjvusNKlS7uSU/VaoNBKjwIAAAA5T6aCq7rBmjJliit1VYlopUqV7KqrrrK33347S2dOpbm5c+d2Nx5QbwEKyWPHjs3SzwAAAMAxGlw7d+5s77//vittVRvX/v37Z1nV/cyZM6Oe66Ktp556yj0AAACQs2U4uObJk8feeustV/qpvyOpX9fY6n8AAAAgIcFVzQMi7dixw15//XV77rnnXE8Bme0eCwAAAMjSXgUCs2fPti5durjusEaOHOluy6q7aAEAAAAJL3HVHateeOEFmzBhgrutqtq46qKpqVOnHvFdtAAAAIAsKXHt0KGD1axZ03744Qd77LHHbO3atfbEE0+k9+0AAADA0Slx/fDDD+3WW2+1nj17Wo0aNY7sUwEAAIDsKnGdM2eOuxCrYcOG1rRpU3vyySdt06ZNGf08AAAAIHuDq+5WNX78eFu3bp316NHD3njjDatYsaIdOnTIpk+f7kItAAAAkDS9Cui2rN26dXMlsIsXL7Y777zThg0bZmXLlrWOHTtmz1wCAAAgx8t0d1iii7VGjBhhv//+u+vLFQAAAEjK4BrQHbQ6depk06ZNy4rJAQAAANkTXAEAAIDsRnAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOCFhAbXoUOHWuPGja1YsWJWtmxZ69Spky1btixqnL1799rNN99sZcqUsaJFi9pll11mGzZsSNg8AwAAIAcG11mzZrlQ+vXXX9v06dPtwIEDdsEFF9iuXbvC4/Tu3dvee+89mzRpkht/7dq1dumllyZytgEAAJAAeS2BPvroo6jnL7zwgit5XbBggZ1zzjm2bds2mzBhgr322mvWqlUrN87EiROtdu3aLuyeeeaZCZpzAAAA5Og2rgqqUrp0afe/AqxKYdu0aRMep1atWla5cmWbO3du3Gns27fPtm/fHvUAAACA/5ImuB46dMhuv/12a9GihdWtW9cNW79+veXPn99KliwZNW65cuXca6m1my1RokT4UalSpaMy/wAAAMghwVVtXZcsWWJvvPHGEU2nX79+ruQ2eKxZsybL5hEAAAA5tI1r4JZbbrH333/fZs+ebSeeeGJ4ePny5W3//v22devWqFJX9Sqg1+IpUKCAewAAAODYktAS11Ao5ELrlClT7LPPPrOTTz456vWGDRtavnz5bMaMGeFh6i5r9erV1qxZswTMMQAAAHJkiauaB6jHgHfffdf15Rq0W1Xb1EKFCrn/r7/+ervjjjvcBVvFixe3Xr16udBKjwIAAAA5S0KD69NPP+3+P/fcc6OGq8urrl27ur8fffRRy507t7vxgHoMaNu2rY0dOzYh8wsAAIAcGlzVVCAtBQsWtKeeeso9AAAAkHMlTa8CAAAAwOEQXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AAADwAsEVAAAAXiC4AgAAwAsEVwAAAHiB4AoAAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAAC8QHAFAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AUvgutTTz1lVapUsYIFC1rTpk3tm2++SfQsAQAA4ChL+uD65ptv2h133GEDBgywhQsXWv369a1t27a2cePGRM8aAAAAjqKkD66jR4+27t2723XXXWd16tSxcePGWeHChe35559P9KwBAADgKMprSWz//v22YMEC69evX3hY7ty5rU2bNjZ37ty479m3b597BLZt2+b+3759e5bO277de7J0esCRyurfeHbYsef/tk0gWRT0YNvZs2dXomcByNZjTjC9UCjkb3DdtGmTHTx40MqVKxc1XM9/+umnuO8ZOnSoDRw4MMXwSpUqZdt8AsngKbsx0bMA+OnOMYmeA8A7fW7Inunu2LHDSpQo4WdwzQyVzqpNbODQoUO2ZcsWK1OmjOXKlSuh84aUZ1c6oVizZo0VL16c1QOkE9sOkDlsO8lLJa0KrRUrVjzseEkdXI877jjLkyePbdiwIWq4npcvXz7uewoUKOAekUqWLJmt84kjo9BKcAXYdoCjheNOcjpcSasXF2flz5/fGjZsaDNmzIgqQdXzZs2aJXTeAAAAcHQldYmrqNq/S5cu1qhRI2vSpIk99thjtmvXLtfLAAAAAHKOpA+uV155pf3555/2wAMP2Pr16+3000+3jz76KMUFW/CPmnSof97Yph0A2HYAjjuIJ1corX4HAAAAgCSQ1G1cAQAAgADBFQAAAF4guAIAAMALBFcAAAB4geCKDJs9e7Z16NDB3d1CdyObOnXqEa3FF154gZtE4JikW1A3btzYihUrZmXLlrVOnTrZsmXLMj29Bx980PWsAhzrnn76aatXr174RgHqu/3DDz/M9PTYdo4dBFdkmPrRrV+/vj311FNJtfbUQcbff/+d6NkAwmbNmmU333yzff311zZ9+nQ7cOCAXXDBBW4bSqSDBw+6m7kAyerEE0+0YcOG2YIFC+zbb7+1Vq1a2cUXX2z/+9//EjpfbDtJQN1hAZmln9CUKVPSHO+7774LnXvuuaGiRYuGihUrFmrQoEFo/vz5oc8//9xNI/IxYMAA956XXnop1LBhQ/eecuXKha666qrQhg0bwtMM3vvBBx+46eXLl88NA5LVxo0b3W921qxZqY6j33Djxo1DhQsXDpUoUSLUvHnz0MqVK0MTJ05Msa1omIwaNSpUt25d954TTzwx1LNnz9COHTvC09R4mta7774bql27dihPnjyhFStWpPpZQDIqVapU6Lnnnkv1dbadnCHpb0CAY8PVV19tZ5xxhqv+yZMnj3333XeWL18+a968ubsbmm4wEVShFi1a1P2v0qlBgwZZzZo1bePGje4ual27drUPPvggatp9+/a1kSNHWtWqVa1UqVIJWT4gPbZt2+b+L126dNzXVWOg5gTdu3e3119/3fbv32/ffPONa5Kjm7EsWbLE3YDl008/jbqvd+7cuW3MmDF28skn22+//WY33XST3X333TZ27NjwtHfv3m3Dhw+35557zsqUKePmQc0O4n0WkExUyjlp0iRXU5Ha7d7ZdnKQRCdn5IwSV5WyvvDCC3FfC0qD0qISWn1eUJIUlLhOnTo1E3MOHF0HDx4MtWvXLtSiRYtUx9m8ebP7Tc+cOTPu66qNqF+/fpqfNWnSpFCZMmXCz4PSWtV8pPezgET74YcfQkWKFHE1BDpG/Pe//011XLadnIM2rjgqVFr6n//8x9q0aePaLf36669pvkdtm3QRWOXKld3FLS1btnTDV69eHTVeo0aNsm2+gayitq4qMX3jjTdSHUeloKpVaNu2rfvtP/7447Zu3bo0p60S2NatW9sJJ5zgtpV///vftnnzZlfKGsifP7+72OVIPws4WlTbptq5efPmWc+ePa1Lly72448/xh2XbSfnILjiqNAVnWpU365dO/vss8+sTp06NmXKlFTHV5WQDqi6mvTVV1+1+fPnh8dXlWakIkWKZPv8A0filltusffff98+//xzd9HJ4UycONHmzp3rmtG8+eabdsopp7iLu1KzcuVKa9++vQulkydPdid8wYWTkdtKoUKFUjQDyOhnAUeTTraqV69uDRs2dD106KJgnWClhm0nZyC44qjRQbF37972ySef2KWXXup2MsHOSW2YIv3000+uxEils2effbbVqlXLtXMFfKLWNAqtOunSCZvaoKaH2oP369fPvvrqK6tbt6699tprqW4rCqrqIWDUqFF25plnuu1s7dq16Z7H1D4LSDb6ne/bt++w47DtHPsIrsiwnTt3uuobPWTFihXu79gq/MCePXvcwXvmzJm2atUq+/LLL10Jau3atd3rVapUcdOcMWOGbdq0yVVvqnmADtJPPPGEu9hk2rRp7kItwLfmAa+88ooLg6rCX79+vXtom4hH25JCpEpBta3oJG/58uVR20qwvWlb0UFcJVK6kDHYVl5++WUbN25cmvOW1mcBiaTfpvoMV43C4sWL3XMdQ3ShbzxsOzlIohvZwj/xurDSo0uXLnHH37dvX6hz586hSpUqhfLnzx+qWLFi6JZbbgnt2bMnPM6NN97oLiaJ7A7rtddeC1WpUiVUoECBULNmzULTpk1zry9atChqPv7666+jtORAxsTbTiK7sYq1fv36UKdOnUIVKlRw28pJJ50UeuCBB9yFXbJ3797QZZddFipZsmTUdEaPHu3eU6hQoVDbtm1dV3KR20a8CyDT+iwgkbp16+Z+k/ptHn/88aHWrVuHPvnkk1THZ9vJOXLpn0SHZwAAACAtNBUAAACAFwiuAAAA8ALBFQAAAF4guAIAAMALBFcAAAB4geAKAAAALxBcAQAA4AWCKwAAALxAcAUAAIAXCK4AkAB//vmn9ezZ0ypXrmwFChSw8uXLW9u2be3LL790r+fKlcumTp3KdwMAEfJGPgEAHB2XXXaZ7d+/31588UWrWrWqbdiwwWbMmGGbN2/O0s/RZ+TPnz9LpwkAiUKJKwAcZVu3brUvvvjChg8fbuedd56ddNJJ1qRJE+vXr5917NjRqlSp4sa75JJLXMlr8PzXX3+1iy++2MqVK2dFixa1xo0b26effho1bY07aNAgu/baa6148eJ2ww03uPB6yy23WIUKFaxgwYLu84YOHcr3DsA7BFcAOMoUOvVQU4B9+/aleH3+/Pnu/4kTJ9q6devCz3fu3GkXXXSRK5ldtGiR/eMf/7AOHTrY6tWro94/cuRIq1+/vhunf//+NmbMGJs2bZq99dZbtmzZMnv11VfDYRgAfJIrFAqFEj0TAJDTTJ482bp372579uyxBg0aWMuWLa1z585Wr14997pKWqdMmWKdOnU67HTq1q1rN954oytRFQXSM844w703cOutt9r//vc/Vzqr6QKAryhxBYAEtXFdu3atKwlVyenMmTNdgH3hhRdSfY9KXPv06WO1a9e2kiVLulLbpUuXpihxbdSoUdTzrl272nfffWc1a9Z0IfaTTz7JtuUCgOxEcAWABFF70/PPP99V53/11VcuYA4YMCDV8RVaVZI6ZMgQ10ZWYfS0005zbVgjFSlSJOq5AvGKFStc21eV8F5xxRX2z3/+M9uWCwCyC8EVAJJEnTp1bNeuXe7vfPny2cGDB6NeV1dZCre6aEuBVV1orVy5Ml3T1oVaV155pY0fP97efPNN11Rhy5Yt2bIcAJBd6A4LAI4ydXl1+eWXW7du3Vyb1mLFitm3335rI0aMcL0GBG1VdRFWixYtXD+vpUqVsho1atg777zjLshSW1WV1B46dCjNzxs9erTrUUBtX3Pnzm2TJk1yoVfNDQDAJwRXADjK1Da1adOm9uijj7ourg4cOGCVKlVyF2vde++9bpxRo0bZHXfc4UpITzjhBFeyqgCqsNu8eXM77rjj7J577rHt27en+XkKxgrFy5cvtzx58rhutD744AMXYgHAJ/QqAAAAAC9wug0AAAAvEFwBAADgBYIrAAAAvEBwBQAAgBcIrgAAAPACwRUAAABeILgCAADACwRXAAAAeIHgCgAAAC8QXAEAAOAFgisAAADMB/8PiOPevlC/9SgAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Hypothesis: Higher Michelin Star Restaurants Have Higher Prices\n",
+    "\n",
+    "avg_price_stars = df.groupby('stars')['price_mean'].mean().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(8,6))\n",
+    "sns.barplot(data=avg_price_stars, x='stars', y='price_mean', palette='Set2')\n",
+    "plt.title('Hypothesis: Higher Michelin Star Restaurants Have Higher Prices')\n",
+    "plt.xlabel('Stars')\n",
+    "plt.ylabel('Average Price ($)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94faf5c1-1a1f-4cc2-92ce-51b928464c0e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4757809-e0cc-4adf-a903-061f5eedb203",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "314836b8-452a-4774-b771-1056d14e7ea8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e08f15d8-4aee-4890-bb61-b5492c43a2aa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Hypothesis: Certain Cuisine Types Have More Stars\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "# Count restaurants by cuisine and stars\n",
+    "cuisine_stars = df.groupby(['cuisine', 'stars_n']).size().reset_index(name='count')\n",
+    "\n",
+    "sns.barplot(data=cuisine_stars, x='cuisine', y='count', hue='stars_n', palette='Set2')\n",
+    "plt.title('Hypothesis: Certain Cuisine Types Have More Stars')\n",
+    "plt.xlabel('Cuisine Type')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend(title='Stars')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "de5fede0-ab66-4ff2-a12b-d3e2239f0fef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Hypothesis: Some Regions Have More Starred Restaurants (total number of starred restaurants in ascending order)\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "# Count restaurants by region and stars\n",
+    "region_stars = df.groupby(['region', 'stars_n']).size().reset_index(name='count')\n",
+    "\n",
+    "# Sum total restaurants per region\n",
+    "region_totals = region_stars.groupby('region')['count'].sum().reset_index()\n",
+    "region_totals = region_totals.sort_values('count')  # Ascending order\n",
+    "\n",
+    "# Merge to keep order in plot\n",
+    "region_stars['region'] = pd.Categorical(region_stars['region'], categories=region_totals['region'], ordered=True)\n",
+    "\n",
+    "sns.barplot(data=region_stars, x='region', y='count', hue='stars_n', palette='Set2')\n",
+    "plt.title('Hypothesis: Number of Starred Restaurants by Region (Ascending Order)')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend(title='Stars')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "884f06e3-78bd-493f-ab23-8b88eb90b181",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\4239056621.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data=avg_price_stars, x='stars_n', y='price_mean', palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Hypothesis: Higher Star Rating Means Higher Average Price\n",
+    "\n",
+    "plt.figure(figsize=(8,6))\n",
+    "avg_price_stars = df.groupby('stars_n')['price_mean'].mean().reset_index()\n",
+    "\n",
+    "sns.barplot(data=avg_price_stars, x='stars_n', y='price_mean', palette='Set2')\n",
+    "plt.title('Hypothesis: Higher Star Rating Means Higher Average Price')\n",
+    "plt.xlabel('Stars')\n",
+    "plt.ylabel('Average Price ($)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "fce0be3d-8e55-43ab-b5e8-94bb5b16234a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\1778379172.py:9: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data=region_counts, x='region', y='count', palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\1778379172.py:21: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data=cuisine_counts, x='cuisine', y='count', palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Hypothesis: Some Regions Have More 3-Star Restaurants and Certain Cuisines Dominate\n",
+    "\n",
+    "# Top regions with 3 stars\n",
+    "three_star = df[df['stars_n']==3]\n",
+    "region_counts = three_star['region'].value_counts().reset_index()\n",
+    "region_counts.columns = ['region', 'count']\n",
+    "\n",
+    "plt.figure(figsize=(10,5))\n",
+    "sns.barplot(data=region_counts, x='region', y='count', palette='Set2')\n",
+    "plt.title('Regions with Most 3-Star Restaurants')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Number of 3-Star Restaurants')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()\n",
+    "\n",
+    "# Dominant cuisine among 3-star restaurants\n",
+    "cuisine_counts = three_star['cuisine'].value_counts().reset_index()\n",
+    "cuisine_counts.columns = ['cuisine', 'count']\n",
+    "\n",
+    "plt.figure(figsize=(12,5))\n",
+    "sns.barplot(data=cuisine_counts, x='cuisine', y='count', palette='Set2')\n",
+    "plt.title('Dominant Cuisine Types Among 3-Star Restaurants')\n",
+    "plt.xlabel('Cuisine Type')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "2f9fbf02-2405-4c4b-90d8-59bca19e8eb5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ZINA\\AppData\\Local\\Temp\\ipykernel_6856\\2143214954.py:6: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data=stars_price_diff, x='stars_n', y='price_mean', palette='Set2')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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O1plNVgRn1Hr+eFBZ1VmWznQiP1d1jWibRXdliFpMIk2dOtWVb5W7yOfQWY7WN/o51LSs1qKAPiOdXQXlTVTe1FWdVgtcsL31ujpT0liGyNdVC4qkte7R1Hwd7LNqFldzuNZZXYtqlYp8j1pndblGvpb2He3HailKj/5eLTvB56dB+jqb1Rl3sFz/q3wFrc4629OZpcpU5OvqLLl+/frh96i6QOVLrbYqb8Hj1L1wzjnnuPWL7urROJDo9dPfquUiO6lFXO9dr68WXg2e13prP1XrblA/HonKZ9Dyq/elfU8tNurWCOqiSGohj2xFP5LIuiDoMVBrospn0HWTkbKsulit8ZEtenpc0G0dK41T/PDDD129pudTy4necyzvRy36wRAM1aNqkVDLuFpcYu2mjtwu+ty0LfTe09re2l7aNrFSK6q2iW7anx999FHXwhI57Ums+4LqItH+HEtX8pHep45H2ifUC6H9cnEa7zVWar0NhthkZ3nL6nE2W7reVPi1c0YLKtRIGl+hYKTKUE1k6jpTl5a65aLpg09rkK+aM4N+VAma7yLp4KFCEutAW3UjpLX+wfgSNW/qYKrmYt8l5UE/srot9L60XVS41NSnSjCt/vGACoUqMo3rUBO7mjgVFPV3WbkyTsFUTb86sKpLUU2TaW0PFeBY+/mPNOWDmkVVyatpWDffdooMwBkVXDqdlTEikXRwlSBcRIvu3lPTsJqjo59DO7QOdGmJHmOQXnkLtrfKRHrrrq453wEw+nV9B9zIKwNFIUn7n7rVFNiC11IXclZeSxWcQoHKid6fymWrVq3CAUrN8fpf+4q6zYLXVZlKqz6QoEsz+ByP1JWlz0jb2fc5BPfpc0irWzejVG8oRAYUQIOgr4O0uhx0JafWXfWVuvw07kj3RQ9eTou64ceMGZNq3GJa+3Ms+3hAJ1TqutTVfdEHWm3D4EAca1lWXa2Tjmhp1d1HEtmdqIHP6o7WiXIwTkhhMXJgtg7Awbrq/1GjRrmb1icpKcl1nat7XPdpzFR6dIDV4xTKI8NVWl15GdneopNfDdtQENK+oS42lZ/IAeCx7gt6bZ0Qjx071o2T0v6l0KVtFvnZ+WgIi7qj1V2rE9vI4L4jKrhkhG+bxLu8ZfdxNtuvelOa1NlUcGmn/lcajq6ojzbfFQVBAQnORFXQfBVx0IKmkKa+WO1UGsCnA40qQ1WAwWXT0bSjaWf/4osvXP+sKk2NS1ElqGVB5ZpROqDHsm3jdeVBsJ001imtMwfJ6mX5GpegzyujFVF666w++2AQc6ToAc3aoYKDeORzKCSpUkpLdLhIr7xlZN01QF8VYlo06Dqz5UYHschWIr2WWsw0UDwtOolJj8Z1iZ5XZ4A60AWDkDWmQCFYLZeRg/X1uto/NB4wre0W7BvB56gz8ejxOdGPjffn4KPBuMEJnehgEH3Zud6btp1uGueig6DKUXpBSXWnQoIqerWUqPzp/egAF5zUZGYf19+qBU6tGipXKkNquVKrkA4u0a1y2b0NfbROOvirlUkHdr0/jYn79NNPw49RXR09EW0wZkn1q1prNb5V2zu9oKQAr9fTcUv1ucKsgonCROSFOpmtU7UfRNbVOlnQ/qFxNto3MrIviI4dKh///ve/XSucxu2obOh4En2iF02tbHpf6j3QiYyCil5XY5aSIz5/31ivyJOD9LbJ0Sxv8TrOZntQ0ptUelPh1dmTBpvqLDKtNx+cIUZSV0DQ7RQM0FMoiaYzLDXnB60nvg80VjrQqQVDBSCW4KHX1VU7uukMRzuwKn+doR/pElE1J+umx2rnU9O0BgzGcnaZ3YKuSQUV3zZQpSOqQLIj/GpwvSpC7bzxalEK3pcONJldZz2HWhFVucUrdOo507rKM/ox6sJRRZPVMh5NXRqREx/qtfR7etvoSOuhs0HddNBRUAqa0HXw0Rmwuve0j+n3yNdVJahgfKQwFnyOagnK6ROvgA7AOohH7x8+ul9nxxpWkN72VIWvx6s7JvIxwZWdmaUDiFpLdNFA5Nl7LN24Pqqr06rP06q7M0LbVmVD3bja73TAi2xZSG8OMG3r6P3Mt711wqu6WwfXyNYHBYrsoBNvnZirK1wnnvosYt0XAjqJ0k0D2DU4W/WTWnSDUHiksqWQqe0Z0IUA0VcYlv9vC6yWRw7HiDw5yInyll5dmNXj7FH5ChN1s6kwa+S7Kt7oOVwCClGR41l0JYf6mYP5f5Todeao5ufID1CFXgk6mGdHgsCU2Zm5FeTUZKedJa2Dl5pIA+rPjaR0rH5VFXBfv6i2R3QiDs6KI5t4szo9QFbo7EY7qK5uit6OwborbKg5Uzt3ZGWf1nbKKDWraxyRDqS6Gihe1PKlg6smX0tru8ayzhozoPXS+JK0Akdmyp3Km0KQrtKLFmxvva72ETXZp3UQUddzZuiERAexyOk89FpqGteBIpreXzBWJLgixveeFY40jk37cxCUVNYVfNU6EFx2HdBJhvY/tcZG7yP6Pdjf9Dc6kKg7JTLgxaPsZZYOTAptwS0ISqrH0vpstE30fiK7pFR3pdXdEZxcRm4TPa8+o6xI63n1+lkJBKqLdcau9xf5efhaYGPp1lX5Un2sFoigy1tlIHJ7B2OEtB+ldWW2Dui6iit6ewfPH71ddACObC3RFVPZObO1Wm9VJwUtxrHuCxpjFz12S4FJLeGRxxO917T2U71G9PPryrrolqK6/z05iWx5DqYcyMny5vsMYz3O5ooJJ0855RQ3ziUYiKoDsK+LRk31GjirN6EDtC5Zjmz6VzO7gpNaGDQ4L5geQE2FkU3cQcWrA6yaD9XioYHZGZkoUJW4Uq762tUKpp1QB28NblNrgn4WjUlSF44qSXUzagyJ+sHVrO5rBQlmnVZTsAqfzpB08NMBPDLwZXV6gKzQTqYxFdpuKly6DF5hVcFNUwIEB1AN7tfnph1T20kHB41DUwWuQe6qtGI5UKtrQYVaO73+RuVFB0BVGulNMJcR2sZ6XwrwKosqH2pBVOuV5rDR56jPL72+bwV/NW1r/ILKgMqYzqK13rqsWJe0ZoS6U3Rmp8uP1TysMqwypjMvnRUqxGidNWZPg4JVNrWuqsz0mWh5MGfJkahC1bYWNXOr8tfz6+fI1gmtj15bUwYEl+OqUtR0BFpP/V0wN4n2DY1F1FmvLrTQ/h6MbVM40gFSB52gK06Vpean0foqaEdOT6H9QWfAKvt6DXU1aT/SPqAQqcHgOuNW+dTl8qoPNBZP5VNj4RQktW30OevsNR5UkauekWCOJpURnVXrlt53R6qbV9tA+7u2o96v6gld6qxWi8hL3nW/tqVa3NSVp+4B7YP6HNSapOdQ3aLtoc9N2z6toBgrlV2tj14jOJlVXaQwktbJTyxUZ+s9a7/VNCLB9ABqaYqcV8tHn6m6i1T3aj20b+pAqkv5tW3SowsVVJbVdaaWBG1DtWhqe+vYktaxQl1VOolS2VSdoG0c1D3qFVF4U12n41Qs7yEz9Fmq/le51pjPWPcFnYioDKru0D6ofVzbPzjhj3yvOnbpfan1TSfC2sYqW3q8jqNaB9XdelwwVU5kWVErkI69qh/0/NqmQf2ZU+VNxyeti3qttK+qBVBjUNV6FMtxNl0ZuUTOdwl25CWPvsvkR40a5f52+PDhqe4LLmfVZbe6jL1GjRru8r+zzjorfMlwpI8++ijUpk0bN52ALoHt3LlzaNmyZakep8ubddmzLtuMvHTRdzm6LkWMvnRSl5PqsVqnIkWKhKpWreouh3/uuefCj3n22WdDbdu2dZcZa73r1q3rLvXcsWNHqm0XrMPixYtDV155ZahmzZrubzTlwUUXXRT66quvsjQ9wJEus4+cAiCty0ijpwcIzJs3z11qXbp0aXd5adOmTVNMoyC//vqrm55B20fbSdtd7+ett96Kab2Dmz4rXbp/yimnuGkB0rrcNqvTA0S+X12SqkuWNY2FPjddRhz5GWj76z37qBw0b97clUVtH02TMGjQoNDatWtTlKvIS34j9xfdImnai1tvvdVtP10Offzxx7t1iJyiQpcojxw50u1rKjvly5d36zBs2LAUZS7W6QG0D6lMa7+KpqkUhgwZEqpXr55bn0qVKrlpH0aPHu3WIzB//ny3DnpM9FQB+gy1rFGjRime+6GHHnLLhw4dmua6Tps2LXTmmWe67a+bLqdW+V6xYkWKx+nS6EsuuSS8/2l7a0qCpKSkdMtBWpc1pyUoc2nd0rqUP9p3333n6gRNj1ChQgV3abemHtFUCaoLImnalKuuusrtB5HPrykQVH/qd71P7SPvvvuu+0wj1yGyPo3VzJkz3X6t/aBWrVqufL344otpXvIda1nWe9YyPafKs+pjXaIfy/Z+6qmn3Gev8qZtpakVVM9HXiZ+JL/99pubbuOMM85wdWvwHFr3jz/+ONW0C3379nX3FyhQIMXl71pfTUGh7a3yp/KS1iXysdS9sR4r58yZk2ofSm9f0PvVFBGqw7S9Vcbat2+fap/WNAQ6Vqm+0msExztNM3H99de77V2qVClXL+qxaR0Tv/7661DLli3dvq7j19ixY73TA6RVVrKrvE2cONFNL6DpFILjWKzH2fQU0D92FOgMWzMOKxVHj2LXMiVbtRYpHQMAAOQGR2WMkrKY5sFQd0Val/oBAADkRtk6RknjGTTGQeMFNK5Bly0CAAAkimwNSrrKQYPgNNhRgxU1sA4AACBRHLUxSgAAAInmqIxRAgAASEQEJQAAgJyccDI30aR6mrRMk3bF+ysgAADIy0L//QoZTVgZ/R2YeVW+C0oKSZn94lAAAGC2Zs2adL9sN6/Id0Ep+EoRfciaxhwAAMRm586drrEhXl9SngjyXVAKutsUkghKAABk/liaH+SPDkYAAIBMICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAr77gCAvGbj+EE5vQpAtqvcZxRbOY5oUQIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAABAbg9Khw8ftqFDh1rt2rWtRIkSVrduXXvwwQctFAqFH6Of77vvPqtWrZp7TMeOHe3nn3/O0fUGAAB5V64JSiNHjrTx48fbU089ZcuXL3e/jxo1yp588snwY/T7uHHjbMKECbZw4UIrWbKkderUyfbv35+j6w4AAPKmwpZLzJ8/37p27WoXXnih+71WrVr22muv2ZdffhluTXr88cft3nvvdY+TV155xapUqWIzZsyw7t275+j6AwCAvCfXtCi1bt3akpKS7KeffnK/f/vttzZv3jw7//zz3e8rV6609evXu+62QNmyZa1ly5a2YMEC7/MeOHDAdu7cmeIGAACQUC1KgwcPdiGmYcOGVqhQITdm6eGHH7arr77a3a+QJGpBiqTfg/vSMmLECBs2bFg2rz0AAMiLck2L0ptvvmmTJ0+2KVOm2OLFi+3ll1+20aNHu/+zYsiQIbZjx47wbc2aNXFbZwAAkLflmhalO++807UqBWONTjrpJPv9999di1CPHj2satWqbvmGDRvcVW8B/d6sWTPv8xYrVszdAAAAErZFae/evVawYMrVURdccnKy+1nTBigsaRxTQF11uvqtVatWR319AQBA3pdrWpQ6d+7sxiTVrFnTTjzxRFuyZImNHTvWevbs6e4vUKCA9evXzx566CGrX7++C06ad6l69erWrVu3nF59AACQB+WaoKT5khR8br75Ztu4caMLQL1793YTTAYGDRpke/bssRtvvNG2b99uZ555ps2aNcuKFy+eo+sOAADypgKhyKmv8wF112laAQ3sLlOmTE6vDoCjaOP4QWxv5HmV+4zKtufemQ+PoblmjBIAAEBuQ1ACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAr77kDmDHz/FTYd8rwx51+b06sAAEcFLUoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwKWxYdOHDAFi5caL///rvt3bvXjj32WDvllFOsdu3aWX1qAACAxAxKn3/+uT3xxBP2zjvv2KFDh6xs2bJWokQJ27p1qwtPderUsRtvvNFuuukmK126dHzXGgAAILd2vXXp0sWuuOIKq1Wrln344Ye2a9cu27Jli/3xxx+uVennn3+2e++915KSkuxvf/ubzZ49O/5rDgAAkBtblC688EKbNm2aFSlSJM371ZqkW48ePWzZsmW2bt26rK4nAABAYgSl3r17x/zYxo0buxsAAECiyZar3g4fPpypv/vzzz/tmmuusYoVK7rxTieddJJ99dVX4ftDoZDdd999Vq1aNXd/x44dXTcfAABArgtKn332WYrxRxs3brQzzzzTihUrZs2bN89QiNm2bZu1adPGdee9//77rstuzJgxVr58+fBjRo0aZePGjbMJEya4K+1KlixpnTp1sv3792flbQAAAMQ/KKl1Z9WqVeHf77//fjt48KDNmDHDqlevbrfffnvMzzVy5EirUaOGTZo0yU4//XQ3vcB5551ndevWDbcmPf74426QeNeuXa1p06b2yiuv2Nq1a93rAQAA5KqgtGLFCtdyFJg5c6Zr9bnooovsmWeesS+++CLm59LftmjRwi677DKrXLmym4tp4sSJ4ftXrlxp69evd91tAU1J0LJlS1uwYIH3eTVVwc6dO1PcAAAAsm0w9/XXX+/+15xJw4cPd/MkaXqAzZs3u1Ye3ZKTk920AT179nSPffHFF4/4nL/99puNHz/eBgwYYHfffbctWrTIbrvtNitatKi7ek4hSapUqZLi7/R7cF9aRowYYcOGDcvM2wQAAPlcpoKSusdEA63POecc69Onj2tJ2rFjRzgQaU6lDz74IN2AFFCwUouSgpeoRen7779345EUlDJryJAhLnwF1KKkLj4AAIBs7XpTQOrbt681aNDA7rnnHrvjjjvC92kiSo01ipWuZIueRqBRo0a2evVq93PVqlXd/xs2bEjxGP0e3JcWDSwvU6ZMihsAAEC2f9fbzTffbA0bNrQlS5ZYq1atrHXr1uH71GWm1pxY6Yo3jXmK9NNPP9kJJ5zgftbgbgUizfbdrFmzcOuQrn5TYAMAAMh1X4rboUMHd4um+ZAyon///i5oqevt8ssvty+//NKee+45d5MCBQpYv3797KGHHrL69eu74DR06FB3dV23bt2y+jYAAADiE5T27Nnj5jCK5+NPO+00mz59umuFeuCBB1wQ0nQAV199dfgxgwYNcs+lL9vdvn27m7Np1qxZVrx48cy8DQAAgPiPUapXr5498sgjR/wON817pMkozz//fDdJZCw0rcDSpUvdBJLLly+3Xr16pbhfrUoKUbrKTY/56KOP3JfuAgAA5JoWpTlz5rhL+P/5z3/aySef7K5WUxeYWnY0w7Zm1dbcRoULF3YtRBn5bjgAAICEDkq6ym3atGnuirSpU6e6rzKZP3++7du3zypVqhSeLFKtSYUKFYr/WgMAAOT2wdw1a9a0gQMHuhsAAEBek6V5lAAAAPIyghIAAIAHQQkAAMCDoAQAAOBBUAIAAMjOoKTpAfSVJfq+tz///NMte/XVV23evHnxeHoAAIDEDEqaT6lTp05WokQJ9+W4Bw4ccMt37NjhvrcNAAAg3wYlfUnthAkT3ASTRYoUCS9v06aNLV68OKtPDwAAkLhBacWKFda2bdtUy8uWLeu+uBYAACDfBqWqVavaL7/8kmq5xifVqVMnq08PAACQuEGpV69edvvtt9vChQutQIECtnbtWps8ebLdcccd1qdPn/isJQAAQKJ915sMHjzYkpOT7ZxzzrG9e/e6brhixYq5oNS3b9/4rCUAAEAiBiW1It1zzz125513ui643bt3W+PGja1UqVLxWUMAAIBEDUqaBuDw4cNWoUIFF5ACW7dutcKFC1uZMmWy+hIAAACJOUape/fu9vrrr6da/uabb7r7AAAA8m1Q0iDu9u3bp1p+9tlnu/sAAADybVDSTNx//fVXquWHDh2yffv2ZfXpAQAAEjconX766fbcc8+lWq7Zups3b57VpwcAAEjcwdz6CpOOHTvat99+66YIkKSkJFu0aJF9+OGH8VhHAACAxGxR0ne6LViwwGrUqOEGcL/zzjtWr149++677+yss86Kz1oCAAAkYouSNGvWzM3GDQAAYPk9KO3cuTM8P5J+PhLmUQIAAPkqKJUvX97WrVtnlStXtnLlyrnZuaOFQiG3XJNRAgAA5Jug9PHHH7uZuOWTTz6J9zoBAAAkblBq166d+1/zJ3366afWs2dPO/744+O9bgAAAIl71Zu+y+3RRx9Nc8JJAAAAy+/TA3To0MG1KgEAAOQ1WZ4e4Pzzz7fBgwfb0qVL3UzcJUuWTHF/ly5dsvoSAAAAiRmUbr75Zvf/2LFjU93HVW8AACBfB6Xk5OT4rAkAAEBeCkqrVq2y2bNn26FDh9yVcCeeeGL81gwAACBRg5LmT7rooots3759/3miwoXtxRdftGuuuSae6wcAAJB4V70NHTrUzj33XPvzzz9ty5Yt1qtXLxs0aFB81w4AACARg9L3339vw4cPt2rVqrmvNNF8Shs3bnShCQAAIF8HJX0ZbqVKlcK/H3PMMVaiRAnbsWNHvNYNAAAgcQdzf/DBB1a2bNkUV8AlJSW51qYA8ygBAIB8GZR69OiRalnv3r3DPzOPEgAAyJdBifmTAABAXpfl73oDAADIqwhKAAAAHgQlAAAAD4ISAACAB0EJAAAgO4PS9u3b7fnnn7chQ4bY1q1b3bLFixe7rzcBAADIl/MoyXfffWcdO3Z0E0+uWrXKfedbhQoV7O2337bVq1fbK6+8Ep81BQAASLQWpQEDBth1111nP//8sxUvXjy8/IILLrC5c+dm9ekBAAASNygtWrQoxWzcgeOOO87Wr1+f1acHAABI3KBUrFgx9wW50X766Sc79thjs/r0AAAAiRuU9KW3DzzwgB06dCj8/W4am3TXXXfZpZdeGo91BAAASMygNGbMGNu9e7dVrlzZ9u3bZ+3atbN69epZ6dKl7eGHH47PWgIAACTiVW+62m327Nk2b948dwWcQtOpp57qroQDAADI10EpcOaZZ7obAABAXpHloDRu3Lg0l2uskqYLUDdc27ZtrVChQll9KQAAgMQKSo899pht2rTJ9u7da+XLl3fLtm3bZsccc4yVKlXKNm7caHXq1LFPPvnEatSoEY91BgAASIzB3MOHD7fTTjvNTTi5ZcsWd9PUAC1btrQnnnjCXQFXtWpV69+/f3zWGAAAIFFalO69916bNm2a1a1bN7xM3W2jR4920wP89ttvNmrUKKYKAAAA+a9Fad26dfbXX3+lWq5lwczc1atXt127dmX1pQAAABIrKLVv3959hcmSJUvCy/Rznz59rEOHDu73pUuXWu3atbP6UgAAAIkVlF544QWrUKGCNW/e3H2diW4tWrRwy3SfaFC3JqYEAADIV2OUNFBbE07++OOPbhC3NGjQwN0iW50AAADy7YSTDRs2dDcAAIC8Ii5B6Y8//rCZM2e6qQAOHjyY4r6xY8fG4yUAAAASLyglJSVZly5d3KSS6n5r0qSJrVq1ykKhkPvONwAAgHw7mHvIkCF2xx13uCvb9JUlmlNpzZo11q5dO7vsssvis5YAAACJGJSWL19u1157rfu5cOHCtm/fPneV2wMPPGAjR46MxzoCAAAkZlAqWbJkeFxStWrV7Ndffw3ft3nz5qw+PQAAQOKOUTrjjDNs3rx51qhRI7vgggts4MCBrhvu7bffdvcBAADk26Ckq9p2797tfh42bJj7+Y033rD69etzxRsAAMi/Qenw4cNuaoCmTZuGu+EmTJgQr3UDAABI3DFKhQoVsvPOO8+2bdsWvzUCAADIK4O5NW/Sb7/9Fp+1AQAAyEtB6aGHHnLzKL377ru2bt0627lzZ4obAABAvh3MrSvdRLNzFyhQILxcM3Prd41jAgAAyJdB6ZNPPonPmgAAAOS1oKSvKgEAAMiLsjxGST777DO75pprrHXr1vbnn3+6Za+++qqbiBIAACDfBiV9CW6nTp2sRIkStnjxYjtw4IBbvmPHDhs+fHg81hEAACBxr3rTJJMTJ060IkWKhJe3adPGBScAAIB8G5RWrFhhbdu2TbW8bNmytn379qw+PQAAQOIGpapVq9ovv/ySarnGJ9WpUyfTz/vII4+46QX69esXXrZ//3675ZZbrGLFilaqVCm79NJLbcOGDZl+DQAAgGwNSr169bLbb7/dFi5c6ILN2rVrbfLkyW4Syj59+mTqORctWmTPPvts+DvkAv3797d33nnHpk6dap9++ql7rUsuuSSrbwEAACB7pgcYPHiwJScn2znnnGN79+513XDFihVzQalv374Zfr7du3fb1Vdf7cY8afxTQIPDX3jhBZsyZYp16NDBLZs0aZI1atTIvvjiCzvjjDOy+lYAAADi26KkVqR77rnHtm7dat9//70LLZs2bbIHH3wwU8+nrrULL7zQOnbsmGL5119/bYcOHUqxvGHDhlazZk1bsGCB9/l0FR5fqwIAAHKkRelf//qX6/465phjrHHjxll6rtdff91dKaeut2jr16+3okWLWrly5VIsr1KlirvPZ8SIETZs2LAsrRcAAMifstyipHFDlStXtquuusree++9TH+325o1a9xYJ41vKl68uMXLkCFDXLddcNPrAAAAHJWgtG7dOtcSpC64yy+/3KpVq+a6z+bPn5+h51HX2saNG+3UU0+1woULu5sGbI8bN879rJajgwcPpppyQFe96co7H42XKlOmTIobAADAUQlKCjEXXXSRawlS0Hnsscds1apV1r59e6tbt27Mz6PB4EuXLrVvvvkmfGvRooUb2B38rAktk5KSUszhtHr1amvVqlVW3wYAAED8xyhF0jglfZ3Jtm3b7Pfff7fly5fH/LelS5e2Jk2apFhWsmRJN2dSsPyGG26wAQMGWIUKFVzLkK6qU0jiijcAAJBrg5KmBZg+fbprVVKLT40aNezKK6+0t956y+JJrVUFCxZ0E03qajaFsmeeeSaurwEAABC3oNS9e3d79913XWuSxigNHTo0bl1hc+bMSfG7Bnk//fTT7gYAAJDrg1KhQoXszTffdK07+jmS5lWK7k4DAADIN0FJ3W2Rdu3aZa+99po9//zz7kq2zE4XAAAAkPBXvQXmzp1rPXr0cNMDjB492n3NiGbpBgAAyJctSpoR+6WXXnLfwaavCdEYJQ2ynjFjRpZn6QYAAEjYFqXOnTtbgwYN7LvvvrPHH3/c1q5da08++WR81w4AACARW5Tef/99u+2226xPnz5Wv379+K4VAABAIrcozZs3zw3cbt68ubVs2dKeeuop27x5c3zXDgAAIBGDkmbDnjhxovuut969e7vve6tevbolJyfb7NmzXYgCAADI11e96WtGevbs6VqY9F1tAwcOtEceecQqV65sXbp0ic9aAgAAJPL0AKLB3aNGjbI//vjDzaUEAACQyOIalAKaobtbt242c+bM7Hh6AACAxA1KAAAAeQFBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAgNwelEaMGGGnnXaalS5d2ipXrmzdunWzFStWpHjM/v377ZZbbrGKFStaqVKl7NJLL7UNGzbk2DoDAIC8LdcEpU8//dSFoC+++MJmz55thw4dsvPOO8/27NkTfkz//v3tnXfesalTp7rHr1271i655JIcXW8AAJB3FbZcYtasWSl+f+mll1zL0tdff21t27a1HTt22AsvvGBTpkyxDh06uMdMmjTJGjVq5MLVGWeckUNrDgAA8qpc06IUTcFIKlSo4P5XYFIrU8eOHcOPadiwodWsWdMWLFjgfZ4DBw7Yzp07U9wAAAASNiglJydbv379rE2bNtakSRO3bP369Va0aFErV65cisdWqVLF3XeksU9ly5YN32rUqJHt6w8AAPKGXBmUNFbp+++/t9dffz3LzzVkyBDXOhXc1qxZE5d1BAAAeV+uGaMUuPXWW+3dd9+1uXPn2vHHHx9eXrVqVTt48KBt3749RauSrnrTfT7FihVzNwAAgIRtUQqFQi4kTZ8+3T7++GOrXbt2ivubN29uRYoUsaSkpPAyTR+wevVqa9WqVQ6sMQAAyOsK56buNl3R9u9//9vNpRSMO9K4ohIlSrj/b7jhBhswYIAb4F2mTBnr27evC0lc8QYAAPJ0UBo/frz7/+yzz06xXFMAXHfdde7nxx57zAoWLOgmmtTVbJ06dbJnnnkmR9YXAADkfYVzU9dbeooXL25PP/20uwEAAOSbMUoAAAC5DUEJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAAQFACAADIGFqUAAAAPAhKAAAAHgQlAAAAD4ISAACAB0EJAADAg6AEAADgQVACAADwICgBAAB4EJQAAAA8CEoAAAAeBCUAAAAPghIAAIAHQQkAAICgBAAAkA9alJ5++mmrVauWFS9e3Fq2bGlffvllTq8SAADIgxIuKL3xxhs2YMAAu//++23x4sV28sknW6dOnWzjxo05vWoAACCPSbigNHbsWOvVq5ddf/311rhxY5swYYIdc8wx9uKLL+b0qgEAgDymsCWQgwcP2tdff21DhgwJLytYsKB17NjRFixYkObfHDhwwN0CO3bscP/v3LkzW9bxwN592fK8QG6SXftPdtu17//XBUBeVTwb98+d/33uUChk+UVCBaXNmzfb4cOHrUqVKimW6/cff/wxzb8ZMWKEDRs2LNXyGjVqZNt6Annd03ZTTq8CAJ+B47J92+zatcvKli2bLz6DhApKmaHWJ41pCiQnJ9vWrVutYsWKVqBAgRxdN8Tn7Eahd82aNVamTBk2KZCLsH/mPaFQyIWk6tWrW36RUEGpUqVKVqhQIduwYUOK5fq9atWqaf5NsWLF3C1SuXLlsnU9cfQpJBGUgNyJ/TNvKZtPWpIScjB30aJFrXnz5paUlJSihUi/t2rVKkfXDQAA5D0J1aIk6kbr0aOHtWjRwk4//XR7/PHHbc+ePe4qOAAAgHwdlK644grbtGmT3XfffbZ+/Xpr1qyZzZo1K9UAb+QP6lbVnFrR3asAch77J/KCAqH8dI0fAABAXh2jBAAAcDQRlAAAAAhKAAAAGUOLEgAAgAdBCQlp7ty51rlzZzc7rGZYnzFjRk6vEoD/Gj9+vDVt2jQ80aTmuXv//ffZPkhIBCUkJM2ddfLJJ9vTTz+d06sCIMrxxx9vjzzyiPsS86+++so6dOhgXbt2tR9++IFthYTD9ABIeGpRmj59unXr1i2nVwWAR4UKFezRRx+1G264gW2EhJJwE04CABLH4cOHberUqa4VmK+aQiIiKAEA4m7p0qUuGO3fv99KlSrlWn0bN27MlkbCYYwSACDuGjRoYN98840tXLjQ+vTp476jc9myZWxpJBxalAAAcVe0aFGrV6+e+7l58+a2aNEie+KJJ+zZZ59layOh0KIEAMh2ycnJduDAAbY0Eg4tSkhIu3fvtl9++SX8+8qVK10zv66sqVmzZo6uG5DfDRkyxM4//3y3L+7atcumTJlic+bMsQ8++CCnVw3IMKYHQEJSpdu+fftUyzUO4qWXXsqRdQLwH5oCICkpydatW2dly5Z1k0/edddddu6557KJkHAISgAAAB6MUQIAACAoAQAAZAwtSgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAA4EFQAgAA8CAoAQAAeBCUAMTVpk2brE+fPu57vooVK2ZVq1a1Tp062eeff+7uL1CggM2YMYOtDiAh8KW4AOLq0ksvtYMHD9rLL79sderUsQ0bNrjv/dqyZUtcX0evUbRo0bg+JwBEo0UJQNxs377dPvvsMxs5cqT70uITTjjBTj/9dPdt8l26dLFatWq5x1188cWuZSn4/ddff7WuXbtalSpVrFSpUnbaaafZRx99lOK59dgHH3zQrr32WitTpozdeOONLizdeuutVq1aNStevLh7vREjRvCJAogbghKAuFHI0U1dawcOHEh1/6JFi9z/kyZNct8sH/y+e/duu+CCC1zL05IlS+zvf/+7de7c2VavXp3i70ePHm0nn3yye8zQoUNt3LhxNnPmTHvzzTdtxYoVNnny5HD4AoB4KBAKhUJxeSYAMLNp06ZZr169bN++fXbqqadau3btrHv37ta0adP/VDoFCtj06dOtW7duR9xeTZo0sZtuusm1GIkC0CmnnOL+NnDbbbfZDz/84Fqf9LwAEG+0KAGI+xiltWvXupYetQzNmTPHBaaXXnrJ+zdqUbrjjjusUaNGVq5cOdcqtXz58lQtSi1atEjx+3XXXWfffPONNWjQwIWmDz/8kE8TQFwRlADEncYLnXvuua57bP78+S7Q3H///d7HKySppWj48OFujJPCz0knneTGIEUqWbJkit8VwFauXOnGLqkF6/LLL7d//OMffKIA4oagBCDbNW7c2Pbs2eN+LlKkiB0+fDjF/Zo6QGFKg7wVkDSlwKpVq2J6bg3svuKKK2zixIn2xhtvuK6/rVu3Zsv7AJD/MD0AgLjRFACXXXaZ9ezZ041JKl26tH311Vc2atQod1VbMNZIg7bbtGnj5lkqX7681a9f395++203gFtjjdQSlZycnO7rjR071l3xprFLBQsWtKlTp7qQpe47AIgHghKAuNHYopYtW9pjjz3mLvk/dOiQ1ahRww3uvvvuu91jxowZYwMGDHAtQMcdd5xrOVLgUbhq3bq1VapUye666y7buXNnuq+nIKYQ9vPPP1uhQoXctALvvfeeC00AEA9c9QYAAODBaRcAAIAHQQkAAMCDoAQAAOBBUAIAAPAgKAEAAHgQlAAAADwISgAAAB4EJQAAAA+CEgAAgAdBCQAAwIOgBAAAYGn7f+l4zl5Va7QZAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 600x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## Hypothesis: Price Difference Between 1-Star and 3-Star Restaurants\n",
+    "\n",
+    "plt.figure(figsize=(6,6))\n",
+    "stars_price_diff = df[df['stars_n'].isin([1,3])].groupby('stars_n')['price_mean'].mean().reset_index()\n",
+    "\n",
+    "sns.barplot(data=stars_price_diff, x='stars_n', y='price_mean', palette='Set2')\n",
+    "plt.title('Hypothesis: Price Difference Between 1-Star and 3-Star Restaurants')\n",
+    "plt.xlabel('Stars')\n",
+    "plt.ylabel('Average Price ($)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b87384bc-fc2c-4790-b7c5-dfb652ecf4df",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/raw/archive/one-star-michelin-restaurants.csv b/data/raw/archive/one-star-michelin-restaurants.csv
new file mode 100644
index 00000000..3dc9e558
--- /dev/null
+++ b/data/raw/archive/one-star-michelin-restaurants.csv
@@ -0,0 +1,550 @@
+name,year,latitude,longitude,city,region,zipCode,cuisine,price,url
+Kilian Stuba,2019,47.34858,10.17114,Kleinwalsertal,Austria,87568,Creative,$$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba
+Pfefferschiff,2019,47.83787,13.07917,Hallwang,Austria,5300,Classic cuisine,$$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff
+Esszimmer,2019,47.80685,13.03409,Salzburg,Austria,5020,Creative,$$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer
+Carpe Diem,2019,47.80001,13.04006,Salzburg,Austria,5020,Market cuisine,$$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem
+Edvard,2019,48.216503,16.36852,Wien,Austria,1010,Modern cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard
+Das Loft,2019,48.21272,16.37931,Wien,Austria,1020,Modern cuisine,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft
+Pramerl & the Wolf,2019,48.20945,16.37174,Wien,Austria,1090,Creative,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf
+Walter Bauer,2019,48.20923,16.37672,Wien,Austria,1010,Classic cuisine,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer
+SHIKI,2019,48.204067,16.37098,Wien,Austria,1010,Japanese,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki
+Tian,2019,48.20513,16.37456,Wien,Austria,1010,Vegetarian,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian
+aend,2019,48.18957,16.34115,Wien,Austria,1010,Modern cuisine,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend
+Le Ciel by Toni Mörwald,2019,48.20209,16.37156,Wien,Austria,1010,Classic cuisine,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald
+Chez TJ,2019,37.39468,-122.08044,South San Francisco,California,94041,Contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj
+Protégé,2019,37.427853,-122.14362,South San Francisco,California,94301,Contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege
+Madera,2019,37.42014,-122.21151,San Francisco,California,94025,Contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera
+The Village Pub,2019,37.42897,-122.25178,San Francisco,California,94062,Contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub
+Plumed Horse,2019,37.25648,-122.03537,South San Francisco,California,95070,Contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse
+Wakuriya,2019,37.52114,-122.3366,San Francisco,California,94402,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya
+Sushi Yoshizumi,2019,37.565075,-122.3211,San Francisco,California,94401,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi
+Rasa,2019,37.577522,-122.345985,San Francisco,California,94010,Indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa
+Maum,2019,37.72669,-122.414215,South San Francisco,California,94101,Korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum
+Al's Place,2019,37.748924,-122.42013,San Francisco,California,94110,Californian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place
+Aster,2019,37.755054,-122.423134,San Francisco,California,94110,Californian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster
+Omakase,2019,37.77077,-122.40298,San Francisco,California,94103,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase
+Commonwealth,2019,37.761395,-122.41946,San Francisco,California,94110,Contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth
+Luce,2019,37.78194,-122.40498,San Francisco,California,94103,Contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce
+Birdsong,2019,37.779495,-122.41048,San Francisco,California,94101,American,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong
+In Situ,2019,37.785633,-122.40113,San Francisco,California,94103,International,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ
+Mourad,2019,37.78695,-122.39987,San Francisco,California,94105,Moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad
+Hashiri,2019,37.783062,-122.40754,San Francisco,California,94103,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri
+Angler,2019,37.793167,-122.39213,San Francisco,California,94101,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler
+Rich Table,2019,37.774914,-122.42271,San Francisco,California,94102,Contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table
+Kin Khao,2019,37.785267,-122.40951,San Francisco,California,94102,Thai,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao
+Michael Mina,2019,37.79349,-122.399574,San Francisco,California,94102,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina
+Sons & Daughters,2019,37.79037,-122.40917,San Francisco,California,94108,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters
+Mister Jiu's,2019,37.79371,-122.406654,San Francisco,California,94108,Chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius
+Nico,2019,37.795803,-122.40333,San Francisco,California,94101,Contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico
+jū-ni,2019,37.77672,-122.43886,San Francisco,California,94117,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni
+Keiko à Nob Hill,2019,37.793106,-122.41445,San Francisco,California,94109,Fusion,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill
+The Progress,2019,37.78371,-122.43282,San Francisco,California,94115,Californian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress
+State Bird Provisions,2019,37.783737,-122.43283,San Francisco,California,94115,American,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions
+Octavia,2019,37.787857,-122.42709,San Francisco,California,94109,Californian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia
+SPQR,2019,37.78732,-122.43375,San Francisco,California,94115,Italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr
+Lord Stanley,2019,37.79592,-122.42208,San Francisco,California,94109,Californian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley
+Kinjo,2019,37.79699,-122.422005,San Francisco,California,94109,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo
+Sorrel,2019,37.788334,-122.44614,San Francisco,California,94101,Californian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel
+Gary Danko,2019,37.80599,-122.42066,San Francisco,California,94109,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko
+Bar Crenn,2019,37.798435,-122.43581,San Francisco,California,94101,French,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn
+Spruce,2019,37.78772,-122.45264,San Francisco,California,94118,Californian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce
+Wako,2019,37.783035,-122.461525,San Francisco,California,94118,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako
+Madcap,2019,37.974712,-122.56168,San Francisco,California,94960,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap
+Aubergine,2019,36.55406,-121.924355,Monterey,California,93921,Contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine
+Kenzo,2019,38.29924,-122.28928,San Francisco,California,94559,Japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo
+La Toque,2019,38.30351,-122.28349,San Francisco,California,94573,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque
+Bouchon,2019,38.40258,-122.3619,San Francisco,California,94599,French,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon
+Auberge du Soleil,2019,38.49199,-122.40534,San Francisco,California,94573,Californian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil
+Farmhouse Inn & Restaurant,2019,38.49039,-122.8835,San Francisco,California,95436,Californian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant
+The Kitchen,2019,38.58894,-121.41424,Sacramento,California,95825,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371
+Madrona Manor,2019,38.60428,-122.88647,San Francisco,California,95448,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor
+Harbor House,2019,39.135876,-123.719444,San Francisco,California,N/A,Californian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house
+Shin Sushi,2019,34.15784,-118.49415,Los Angeles,California,91436,Japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi
+Rustic Canyon,2019,34.024952,-118.49118,Los Angeles,California,90401,Californian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon
+Dialogue,2019,34.01679,-118.49748,Los Angeles,California,90401,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue
+Kato,2019,34.041683,-118.460915,Los Angeles,California,90025,Asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato
+CUT,2019,34.067036,-118.401024,Los Angeles,California,90212,Steakhouse,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418
+Nozawa Bar,2019,34.0683,-118.39849,Los Angeles,California,90210,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar
+Shunji,2019,34.028496,-118.45195,Los Angeles,California,90064,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji
+Maude,2019,34.064346,-118.39904,Los Angeles,California,90212,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude
+Mori Sushi,2019,34.033398,-118.44229,Los Angeles,California,90064,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi
+Trois Mec,2019,34.084255,-118.33851,Los Angeles,California,90028,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec
+Osteria Mozza,2019,34.08342,-118.338974,Los Angeles,California,90028,Italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza
+Kali,2019,34.083443,-118.32455,Los Angeles,California,90028,Californian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali
+Le Comptoir,2019,34.06354,-118.30057,Los Angeles,California,90020,Californian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir
+Q Sushi,2019,34.046883,-118.255844,Los Angeles,California,90017,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi
+Shibumi,2019,34.044155,-118.256134,Los Angeles,California,90014,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644
+Hayato,2019,34.03311,-118.24265,Los Angeles,California,90001,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato
+Bistro Na's,2019,34.104412,-118.07159,Los Angeles,California,91780,Chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s
+Orsa & Winston,2019,33.92145,-118.275406,Los Angeles,California,90013,Fusion,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston
+Taco María,2019,33.69475,-117.924965,Costa Mesa,California,92626,Mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria
+Hana re,2019,33.67759,-117.886566,Costa Mesa,California,92626,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re
+Addison,2019,32.939903,-117.20013,San Diego,California,92130,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison
+Parachute,2019,41.94483,-87.70638,Chicago,Chicago,60618,Fusion,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute
+Goosefoot,2019,41.96856,-87.69596,Chicago,Chicago,60625,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot
+Elizabeth,2019,41.969555,-87.688736,Chicago,Chicago,60625,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth
+EL Ideas,2019,41.863,-87.68686,Chicago,Chicago,60618,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas
+Band of Bohemia,2019,41.967308,-87.67477,Chicago,Chicago,60640,Gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia
+Schwa,2019,41.90888,-87.66792,Chicago,Chicago,60662,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa
+Temporis,2019,41.898907,-87.66725,Chicago,Chicago,N/A,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis
+Entente,2019,41.93721,-87.6652,Chicago,Chicago,60657,Contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente
+Elske,2019,41.884426,-87.66071,Chicago,Chicago,60607,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske
+Dusek's (Board & Beer),2019,41.857853,-87.65757,Chicago,Chicago,60608,Gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer
+Roister,2019,41.886658,-87.65187,Chicago,Chicago,60607,Contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister
+Boka,2019,41.91363,-87.64806,Chicago,Chicago,60614,Contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka
+Blackbird,2019,41.88423,-87.64356,Chicago,Chicago,60661,Contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird
+Sepia,2019,41.88394,-87.64247,Chicago,Chicago,60661,American,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia
+North Pond,2019,41.9296,-87.63762,Chicago,Chicago,60614,Contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond
+Everest,2019,41.87597,-87.63212,Chicago,Chicago,60605,French,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest
+Topolobampo,2019,41.89047,-87.63085,Chicago,Chicago,60654,Mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo
+Spiaggia,2019,41.90062,-87.62457,Chicago,Chicago,60611,Italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia
+Monte,2019,45.08279,13.631168,Rovinj,Croatia,52210,Creative,$$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte
+Draga di Lovrana,2019,45.275867,14.251401,Lovran,Croatia,51415,Modern cuisine,$$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana
+Noel,2019,45.811054,15.988205,Zagreb,Croatia,10000,Modern cuisine,$$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel
+Pelegrini,2019,43.73603,15.888903,Šibenik,Croatia,22000,Modern cuisine,$$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini
+360º,2019,42.641563,18.111523,Dubrovnik,Croatia,20000,Modern cuisine,$$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA
+Field,2019,50.0919,14.42193,Praha,Czech Republic,110 00,Modern cuisine,$$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field
+La Degustation Bohême Bourgeoise,2019,50.09115,14.42511,Praha,Czech Republic,110 00,Modern cuisine,$$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise
+Gastromé,2019,56.15788,10.20937,Aarhus,Denmark,8000,Modern cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome
+Domestic,2019,56.15893,10.212132,Aarhus,Denmark,8000,Modern cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic
+Substans,2019,56.15415,10.20372,Aarhus,Denmark,8000,Modern cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans
+Frederikshøj,2019,56.12497,10.208638,Aarhus,Denmark,8000,Creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj
+Me‚Mu,2019,55.70901,9.53302,Vejle,Denmark,7100,Modern cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu
+Ti Trin Ned,2019,55.56659,9.752689,Fredericia,Denmark,7000,Modern cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned
+Slotskøkkenet,2019,55.771236,11.390805,Hørve,Denmark,4534,Creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet
+Søllerød Kro,2019,55.81325,12.49548,København,Denmark,2840,Modern cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro
+Jordnær,2019,55.748226,12.541014,København,Denmark,2820,Danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer
+Relæ,2019,55.693233,12.543222,København,Denmark,2200 N,Modern cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae
+Kiin Kiin,2019,55.69188,12.55754,København,Denmark,2200 N,Thai,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin
+formel B,2019,55.67031,12.53798,København,Denmark,1800 C,Modern cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b
+Kokkeriet,2019,55.68768,12.58492,København,Denmark,1306 K,Modern cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet
+Clou,2019,55.68342,12.58497,København,Denmark,2100 K,Modern cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou
+Marchal,2019,55.68034,12.58508,København,Denmark,1050 K,Modern cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal
+Kong Hans Kælder,2019,55.6787,12.584003,København,Denmark,1070 K,Classic French,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder
+Stud!o at The Standard,2019,55.67799,12.59191,København,Denmark,1058 K,Creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard
+108,2019,55.67785,12.5980835,København,Denmark,1401 K,Modern cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108
+Era Ora,2019,55.673004,12.591292,København,Denmark,1414 K,Italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora
+Alouette,2019,55.66265,12.57634,København,Denmark,2300 S,Modern cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette
+Frederiksminde,2019,55.12417,12.04623,Præstø,Denmark,4720,Creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde
+Kadeau Bornholm,2019,55.00457,14.969216,Pedersker,Denmark,3720,Creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm
+Ask,2019,60.17236,24.95648,Helsingfors / Helsinki,Finland,00170,Modern cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask
+Grön,2019,60.16446,24.93308,Helsingfors / Helsinki,Finland,00180,Finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron
+Demo,2019,60.164806,24.941635,Helsingfors / Helsinki,Finland,00120,Modern cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo
+Olo,2019,60.16616,24.947704,Helsingfors / Helsinki,Finland,00170,Modern cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo
+Palace,2019,60.16556,24.95256,Helsingfors / Helsinki,Finland,00130,Modern cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace
+Ora,2019,60.157578,24.942947,Helsingfors / Helsinki,Finland,00150,Modern cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora
+Botrini's,2019,38.02318,23.79377,Athína,Greece,152 33,Mediterranean,$$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s
+Varoulko Seaside,2019,37.937378,23.658577,Athína,Greece,185 33,Seafood,$$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside
+Hytra,2019,37.95818,23.71895,Athína,Greece,11745,Modern cuisine,$$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra
+Loaf On,2019,22.379858,114.27188,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on
+Lei Garden (Kwun Tong),2019,22.312672,114.22482,Hong Kong,Hong Kong,N/A,Cantonese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong
+Tim Ho Wan (Sham Shui Po),2019,22.32805,114.16665,Hong Kong,Hong Kong,N/A,Dim Sum,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po
+Lei Garden (Mong Kok),2019,22.320219,114.17133,Hong Kong,Hong Kong,N/A,Cantonese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok
+Ming Court,2019,22.318314,114.16933,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court
+Yat Tung Heen (Jordan),2019,22.308014,114.17147,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan
+Fu Ho (Tsim Sha Tsui),2019,22.3022,114.17194,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui
+Shang Palace,2019,22.297325,114.177155,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace
+IM Teppanyaki & Wine,2019,22.280807,114.19211,Hong Kong,Hong Kong,N/A,Teppanyaki,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine
+Ah Yat Harbour View (Tsim Sha Tsui),2019,22.296984,114.17197,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui
+Rech,2019,22.293434,114.174034,Hong Kong,Hong Kong,N/A,Seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech
+Spring Moon,2019,22.294943,114.17182,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon
+Imperial Treasure Fine Chinese Cuisine,2019,22.296177,114.170006,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine
+Sushi Tokami,2019,22.296112,114.16916,Hong Kong,Hong Kong,N/A,Sushi,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami
+Tosca,2019,22.30357,114.16007,Hong Kong,Hong Kong,N/A,Italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519
+Épure,2019,22.29583,114.169304,,Hong Kong,N/A,French,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure
+Yee Tung Heen,2019,22.282295,114.184135,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen
+Yè Shanghai (Tsim Sha Tsui),2019,22.295143,114.168236,Hong Kong,Hong Kong,N/A,Shanghainese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui
+Ho Hung Kee,2019,22.280058,114.18364,Hong Kong,Hong Kong,N/A,Noodles and congee,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee
+Jardin de Jade (Wan Chai),2019,22.28025,114.17716,Hong Kong,Hong Kong,N/A,Shanghainese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai
+Zhejiang Heen,2019,22.2787,114.17763,Hong Kong,Hong Kong,N/A,Shanghainese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen
+Takumi by Daisuke Mori,2019,22.276512,114.17718,Hong Kong,Hong Kong,N/A,Innovative,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori
+Kam's Roast Goose,2019,22.277693,114.175385,Hong Kong,Hong Kong,N/A,Cantonese Roast Meats,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose
+Pang's Kitchen,2019,22.26921,114.18452,Hong Kong,Hong Kong,N/A,Cantonese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen
+Xin Rong Ji,2019,22.278013,114.17273,Hong Kong,Hong Kong,N/A,Taizhou,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji
+Qi (Wan Chai),2019,22.276443,114.17126,Hong Kong,Hong Kong,N/A,Sichuan,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai
+Man Wah,2019,22.282213,114.15951,Hong Kong,Hong Kong,N/A,Cantonese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah
+Summer Palace,2019,22.277136,114.1643,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace
+Octavium,2019,22.282587,114.1572,Hong Kong,Hong Kong,N/A,Italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium
+Arbor,2019,22.283146,114.15542,,Hong Kong,N/A,Innovative,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor
+VEA,2019,22.284931,114.15313,Hong Kong,Hong Kong,N/A,Innovative,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea
+Mandarin Grill + Bar,2019,22.281048,114.1574,Hong Kong,Hong Kong,N/A,European contemporary,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar
+Yat Lok,2019,22.28226,114.155655,Hong Kong,Hong Kong,N/A,Cantonese Roast Meats,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok
+Guo Fu Lou,2019,22.278313,114.16034,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828
+Celebrity Cuisine,2019,22.284334,114.15312,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine
+Sushi Wadatsumi,2019,22.281038,114.15646,Hong Kong,Hong Kong,N/A,Sushi,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi
+Arcane,2019,22.280624,114.15682,Hong Kong,Hong Kong,N/A,European contemporary,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane
+Duddell's,2019,22.28008,114.157364,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s
+Beefbar,2019,22.27982,114.15752,Hong Kong,Hong Kong,N/A,Steakhouse,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar
+New Punjab Club,2019,22.28005,114.15525,Hong Kong,Hong Kong,N/A,Indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club
+Kaiseki Den by Saotome,2019,22.285713,114.147865,Hong Kong,Hong Kong,N/A,Japanese,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome
+Belon,2019,22.281662,114.152405,Hong Kong,Hong Kong,N/A,French,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon
+Tate,2019,22.280996,114.15276,Hong Kong,Hong Kong,N/A,Innovative,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate
+The Ocean,2019,22.235146,114.198524,Hong Kong,Hong Kong,N/A,French,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean
+Costes Downtown,2019,47.50113,19.048595,Budapest,Hungary,1051,Modern cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown
+Borkonyha Winekitchen,2019,47.49953,19.052391,Budapest,Hungary,1051,Modern cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen
+Stand,2019,47.500244,19.059624,Budapest,Hungary,1061,Modern cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand
+Babel,2019,47.492603,19.05256,Budapest,Hungary,1052,Modern cuisine,$$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel
+Costes,2019,47.48873,19.061779,Budapest,Hungary,1092,Modern cuisine,$$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes
+The Kitchen,2019,22.191442,113.543,Macau,Macau,N/A,Steakhouse,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810
+Tim's Kitchen,2019,22.189898,113.5439,Macau,Macau,N/A,Cantonese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen
+Wing Lei,2019,22.188028,113.54578,Macau,Macau,N/A,Cantonese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei
+King,2019,22.190178,113.54028,Macau,Macau,N/A,Cantonese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226
+Ying,2019,22.161234,113.555405,Macau,Macau,N/A,Cantonese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying
+Shinji by Kanesaka,2019,22.149635,113.56397,Macau,Macau,N/A,Sushi,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka
+The Golden Peacock,2019,22.148178,113.56335,Macau,Macau,N/A,Indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock
+Zi Yat Heen,2019,22.145418,113.56152,Macau,Macau,N/A,Cantonese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen
+8 1/2 Otto e Mezzo - Bombana,2019,22.14918,113.55393,Macau,Macau,N/A,Italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana
+Pearl Dragon,2019,22.140524,113.56256,Macau,Macau,N/A,Cantonese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon
+Lai Heen,2019,22.148754,113.551926,Macau,Macau,N/A,Cantonese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen
+FAGN,2019,63.43392,10.39628,Trondheim,Norway,7010,Modern cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn
+Credo,2019,63.43391,10.39631,Trondheim,Norway,7066,Creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo
+RE-NAA,2019,58.972088,5.732877,Stavanger,Norway,4006,Creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa
+Sabi Omakase,2019,58.969936,5.7433133,Stavanger,Norway,4013,Sushi,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase
+Galt,2019,59.91661,10.71369,Oslo,Norway,0263,Modern cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt
+Kontrast,2019,59.92355,10.75106,Oslo,Norway,0178,Scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast
+Statholdergaarden,2019,59.90937,10.74305,Oslo,Norway,0151,Classic cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden
+Del Posto,2019,40.74327,-74.0077,New York,New York City,10011,Italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto
+Le Grill de Joël Robuchon,2019,40.742897,-74.0077,New York,New York City,N/A,French,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon
+L'Appart,2019,40.711903,-74.01544,New York,New York City,10281,French,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart
+Okuda,2019,40.743793,-74.00633,New York,New York City,N/A,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda
+Wallsé,2019,40.73538,-74.00814,New York,New York City,10014,Austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse
+Jeju Noodle Bar,2019,40.732952,-74.00744,New York,New York City,10014,Korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar
+Sushi Nakazawa,2019,40.731716,-74.00451,New York,New York City,10014,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa
+Kosaka,2019,40.738316,-74.00137,New York,New York City,10011,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka
+Bâtard,2019,40.719616,-74.00589,New York,New York City,10013,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard
+Hirohisa,2019,40.72452,-74.003006,New York,New York City,10012,Japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa
+Blue Hill,2019,40.7321,-73.9996,New York,New York City,10011,American,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill
+ZZ's Clam Bar,2019,40.727646,-74.00046,New York,New York City,10012,Seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar
+Babbo,2019,40.73233,-73.99916,New York,New York City,10011,Italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo
+Carbone,2019,40.727966,-74.00017,New York,New York City,10003,Italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone
+Le Coucou,2019,40.719162,-74.00001,New York,New York City,10013,French,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou
+Aldea,2019,40.73885,-73.99366,New York,New York City,10011,Mediterranean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea
+Cote,2019,40.73778,-73.99375,New York,New York City,10010,Korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote
+Bouley at Home,2019,40.74114,-73.99242,New York,New York City,N/A,Contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home
+Gotham Bar and Grill,2019,40.73417,-73.99379,New York,New York City,10003,American,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill
+Nix,2019,40.733166,-73.993515,New York,New York City,10003,Vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix
+Junoon,2019,40.74325,-73.99077,New York,New York City,10010,Indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon
+NoMad,2019,40.74497,-73.98883,New York,New York City,10001,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad
+The Musket Room,2019,40.723915,-73.99375,New York,New York City,10012,Contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room
+Uncle Boons,2019,40.721203,-73.99441,New York,New York City,10012,Thai,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons
+Noda,2019,40.74497,-73.988075,New York,New York City,N/A,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda
+Gramercy Tavern,2019,40.73849,-73.98868,New York,New York City,10003,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern
+The Clocktower,2019,40.7414,-73.987595,New York,New York City,10010,Contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower
+Ai Fiori,2019,40.75013,-73.98357,New York,New York City,10018,Italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori
+Casa Mono,2019,40.73595,-73.98706,New York,New York City,10003,Spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono
+Jewel Bako,2019,40.72713,-73.98928,New York,New York City,10003,Japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako
+The River Café,2019,40.703613,-73.994774,New York,New York City,11201,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe
+Bar Uchū,2019,40.72191,-73.99003,New York,New York City,10002,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu
+Café China,2019,40.749973,-73.98228,New York,New York City,10016,Chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china
+Kanoyama,2019,40.73071,-73.98655,New York,New York City,10003,Japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama
+Contra,2019,40.719917,-73.98922,New York,New York City,10002,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra
+Atomix,2019,40.744408,-73.98292,New York,New York City,N/A,Korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix
+Kyo Ya,2019,40.7266,-73.98526,New York,New York City,10009,Japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya
+Kajitsu,2019,40.72776,-73.98428,New York,New York City,10016,Japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu
+Agern,2019,40.75234,-73.9775,New York,New York City,10017,Scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern
+Caviar Russe,2019,40.760685,-73.97355,New York,New York City,10022,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe
+Tuome,2019,40.724194,-73.9828,New York,New York City,10009,Fusion,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome
+Tempura Matsui,2019,40.74839,-73.97474,New York,New York City,10158,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui
+Sushi Yasuda,2019,40.75108,-73.97364,New York,New York City,10017,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda
+Sushi Amane,2019,40.75133,-73.971725,New York,New York City,10017,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane
+Café Boulud,2019,40.77439,-73.96414,New York,New York City,10021,French,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud
+Claro,2019,40.677395,-73.98615,New York,New York City,N/A,Mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro
+Satsuki,2019,40.801243,-73.95187,New York,New York City,10036,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki
+Sushi Noz,2019,40.77384,-73.958275,New York,New York City,N/A,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz
+Sushi Inoue,2019,40.810448,-73.943726,New York,New York City,10027,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue
+Meadowsweet,2019,40.710354,-73.96317,New York,New York City,11212,Mediterranean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet
+Casa Enríque,2019,40.743492,-73.95425,New York,New York City,11101,Mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique
+Peter Luger,2019,40.70986,-73.96263,New York,New York City,11211,Steakhouse,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger
+Oxomoco,2019,40.730057,-73.95538,New York,New York City,11222,Mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco
+The Finch,2019,40.68704,-73.96297,New York,New York City,11238,American,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch
+Faro,2019,40.707584,-73.923035,New York,New York City,11237,American,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro
+Senses,2019,52.244583,21.005886,Warszawa,Poland,00 085,Modern cuisine,$$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses
+atelier Amaro,2019,52.21708,21.03768,Warszawa,Poland,00 507,Modern cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro
+Aniar,2019,53.27112,-9.057078,Gaillimh/Galway,Ireland,N/A,Creative,N/A,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar
+Loam,2019,53.27384,-9.044447,Gaillimh/Galway,Ireland,N/A,Creative,N/A,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam
+Wild Honey Inn,2019,53.03151,-9.2911,Lios Dúin Bhearna/Lisdoonvarna,Ireland,N/A,Classic cuisine,N/A,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn
+Chestnut,2019,51.562588,-9.460854,Ballydehob,Ireland,N/A,Modern cuisine,N/A,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut
+Mews,2019,51.48285,-9.37234,Baltimore,Ireland,N/A,Modern cuisine,N/A,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews
+Ichigo Ichie,2019,51.898167,-8.479933,Corcaigh/Cork,Ireland,N/A,Japanese,N/A,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie
+Chapter One,2019,53.354374,-6.263786,City Centre,Ireland,D1,Modern cuisine,N/A,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one
+Greenhouse,2019,53.340042,-6.258506,City Centre,Ireland,D2,Modern cuisine,N/A,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse
+L'Ecrivain,2019,53.33627,-6.24834,City Centre,Ireland,D2,Modern cuisine,N/A,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain
+Campagne,2019,52.6559,-7.2464285,Cill Chainnigh/Kilkenny,Ireland,N/A,Modern British,N/A,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne
+Heron & Grey,2019,53.30143,-6.177574,Blackrock,Ireland,N/A,Modern cuisine,N/A,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey
+Lady Helen,2019,52.526096,-7.188277,Baile Mhic Andáin/Thomastown,Ireland,N/A,Modern cuisine,N/A,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen
+House,2019,51.94777,-7.7135396,Aird Mhór/Ardmore,Ireland,N/A,Modern cuisine,N/A,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house
+Olympe,2019,-22.96109,-43.20658,Rio de Janeiro - 22470,Rio de Janeiro,230,French,$$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe
+Lasai,2019,-22.95401,-43.19557,Rio de Janeiro - 22271,Rio de Janeiro,020,modern,$$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai
+Oteque,2019,-22.95747,-43.194424,Rio de Janeiro - 22271,Rio de Janeiro,020,modern,$$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque
+Mee,2019,-22.96764,-43.17925,Rio de Janeiro - 22021,Rio de Janeiro,001,Asian influences,$$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee
+Cipriani,2019,-22.96755,-43.17901,Rio de Janeiro - 22021,Rio de Janeiro,001,Italian,$$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani
+Jun Sakamoto,2019,-23.56318,-46.67737,São Paulo - 05413,Sao Paulo,000,Japanese,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto
+Maní,2019,-23.56621,-46.67974,São Paulo - 05415,Sao Paulo,000,creative,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani
+Evvai,2019,-23.567078,-46.679146,São Paulo - 05415,Sao Paulo,000,modern,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai
+Picchi,2019,-23.565063,-46.666767,São Paulo - 01426,Sao Paulo,001,Italian,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi
+Kosushi,2019,-23.58522,-46.6809,São Paulo - 04538,Sao Paulo,110,Japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi
+Ryo Gastronomia,2019,-23.58132,-46.6767,São Paulo - 04531,Sao Paulo,001,Japanese,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia
+Tangará Jean-Georges,2019,-23.634005,-46.72428,São Paulo - 05706,Sao Paulo,290,modern,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges
+Kinoshita,2019,-23.59159,-46.67146,São Paulo - 04509,Sao Paulo,001,Japanese,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita
+Kan Suke,2019,-23.5684,-46.64936,São Paulo - 01401,Sao Paulo,000,Japanese,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke
+Huto,2019,-23.60882,-46.6598,São Paulo - 04080,Sao Paulo,004,Japanese,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto
+Stay,2019,37.513004,127.10257,Seoul,South Korea,N/A,French contemporary,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay
+Lee Jong Kuk 104,2019,37.592903,126.997284,Seoul,South Korea,N/A,Korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104
+Hansikgonggan,2019,37.577705,126.98852,Seoul,South Korea,N/A,Korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan
+Dining in Space,2019,37.57761,126.98845,Seoul,South Korea,N/A,French contemporary,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space
+Exquisine,2019,37.521046,127.049736,Seoul,South Korea,N/A,Innovative,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine
+Dosa,2019,37.52445,127.04441,Seoul,South Korea,N/A,Innovative,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871
+Joo Ok,2019,37.52252,127.04396,Seoul,South Korea,N/A,Korean contemporary,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok
+Balwoo Gongyang,2019,37.57389,126.98318,Seoul,South Korea,N/A,Temple cuisine,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang
+Muoki,2019,37.518963,127.04217,Seoul,South Korea,N/A,Innovative,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki
+L'Amitié,2019,37.522675,127.032646,Seoul,South Korea,N/A,French,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie
+Yu Yuan,2019,37.57045,126.97534,Seoul,South Korea,N/A,Chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan
+Bicena,2019,37.5384,127.00135,Seoul,South Korea,N/A,Korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena
+Mosu,2019,37.536415,126.99934,Seoul,South Korea,N/A,Innovative,$$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu
+Poom,2019,37.54903,126.98341,Seoul,South Korea,N/A,Korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom
+Soigné,2019,37.49816,127.002,Seoul,South Korea,N/A,Innovative,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne
+Table for Four,2019,37.49769,126.99442,Seoul,South Korea,N/A,European contemporary,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four
+Zero Complex,2019,37.49417,126.9948,Seoul,South Korea,N/A,Innovative,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex
+Jin Jin,2019,37.55777,126.91324,Seoul,South Korea,N/A,Chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin
+Gotgan,2019,37.52212,126.92022,Seoul,South Korea,N/A,Korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan
+Hill Street Tai Hwa Pork Noodle,2018,1.3052,103.8624,Singapore,Singapore,N/A,Street Food,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle
+Putien (Kitchener Road),2018,1.30969,103.8573,Singapore,Singapore,N/A,Fujian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road
+Chef Kang's,2018,1.304735,103.84955,Singapore,Singapore,N/A,Cantonese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s
+Garibaldi,2018,1.296564,103.855,Singapore,Singapore,N/A,Italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi
+Summer Pavilion,2018,1.291284,103.8603,Singapore,Singapore,N/A,Cantonese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion
+Shinji (Bras Basah Road),2018,1.2959028,103.8539,Singapore,Singapore,N/A,Sushi,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road
+The Song of India,2018,1.310767,103.8353,Singapore,Singapore,N/A,Indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india
+Lei Garden,2018,1.295228,103.8521,Singapore,Singapore,N/A,Cantonese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509
+Whitegrass,2018,1.295452,103.8516,Singapore,Singapore,N/A,Australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass
+Jaan,2018,1.293184,103.8529,Singapore,Singapore,N/A,French contemporary,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan
+Labyrinth,2018,1.2899688,103.8562,Singapore,Singapore,N/A,Innovative,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth
+Cut,2018,1.2853268,103.8607,Singapore,Singapore,N/A,Steakhouse,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut
+Alma,2018,1.309123,103.834,Singapore,Singapore,N/A,European contemporary,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma
+Béni,2018,1.302379,103.8366,Singapore,Singapore,N/A,French contemporary,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni
+Crystal Jade Golden Palace,2018,1.303244,103.8353,Singapore,Singapore,N/A,Chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace
+Imperial Treasure Fine Teochew Cuisine (Orchard),2018,1.3032448,103.8349,Singapore,Singapore,N/A,Chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard
+Saint Pierre,2018,1.285781,103.8538,Singapore,Singapore,N/A,French contemporary,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre
+Sushi Ichi,2018,1.305105,103.8324,Singapore,Singapore,N/A,Sushi,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi
+Sushi Kimura,2018,1.3063719,103.829475,Singapore,Singapore,N/A,Sushi,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura
+Iggy's,2018,1.305986,103.8294,Singapore,Singapore,N/A,European contemporary,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s
+Braci,2018,1.287212,103.84949,Singapore,Singapore,N/A,Italian contemporary,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci
+Jiang-Nan Chun,2018,1.304937,103.8288,Singapore,Singapore,N/A,Cantonese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun
+Bacchanalia,2018,1.2874758,103.8474,Singapore,Singapore,N/A,Innovative,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia
+Shinji (Tanglin Road),2018,1.305821,103.8257,Singapore,Singapore,N/A,Sushi,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road
+Corner House,2018,1.315121,103.8154,Singapore,Singapore,N/A,Innovative,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house
+Summer Palace,2018,1.3043848,103.825,Singapore,Singapore,N/A,Cantonese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658
+Cheek by Jowl,2018,1.281301,103.84849,Singapore,Singapore,N/A,Australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl
+Nouri,2018,1.28086,103.84713,Singapore,Singapore,N/A,Innovative,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri
+Liao Fan Hong Kong Soya Sauce Chicken Rice & Noodle,2018,1.28248,103.8439,Singapore,Singapore,N/A,Street Food,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle
+Burnt Ends,2018,1.280501,103.8418,Singapore,Singapore,N/A,Barbecue,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends
+Rhubarb,2018,1.279368,103.843,Singapore,Singapore,N/A,French contemporary,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb
+Meta,2018,1.279372,103.8415,Singapore,Singapore,N/A,Innovative,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta
+Ma Cuisine,2018,1.27826,103.842,Singapore,Singapore,N/A,French,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine
+Candlenut,2018,1.2797709,103.8402,Singapore,Singapore,N/A,Peranakan,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut
+Bhoga,2019,57.70618,11.9624,Göteborg,Sweden,411 14,Creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga
+Koka,2019,57.698406,11.965365,Göteborg,Sweden,411 25,Modern cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka
+28+,2019,57.69809,11.97377,Göteborg,Sweden,411 34,Modern cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28
+SK Mat & Människor,2019,57.69688,11.98338,Göteborg,Sweden,412 55,Modern cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor
+Thörnströms Kök,2019,57.69437,11.977609,Göteborg,Sweden,411 32,Classic cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok
+Upper House,2019,57.69745,11.99021,Göteborg,Sweden,402 26,Creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house
+Agrikultur,2019,59.34777,18.05863,Stockholm,Sweden,113 54,Modern cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur
+Sushi Sho,2019,59.341286,18.049892,Stockholm,Sweden,113 28,Japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho
+Volt,2019,59.33867,18.079683,Stockholm,Sweden,114 48,Creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt
+Ekstedt,2019,59.33674,18.07521,Stockholm,Sweden,11446,Meats and grills,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt
+Operakällaren,2019,59.329876,18.071363,Stockholm,Sweden,111 86,Classic cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren
+Mathias Dahlgren-Matbaren,2019,59.32935,18.07543,Stockholm,Sweden,103 27,Modern cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren
+Aloë,2019,59.2842,17.985748,Stockholm,Sweden,125 33,Creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe
+PM & Vänner,2019,56.87892,14.80387,Växjö,Sweden,352 31,Creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner
+Bloom in the Park,2019,55.59064,12.99794,Malmö,Sweden,214 66,Creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park
+SAV,2019,55.53376,13.00537,Malmö,Sweden,21875,Creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav
+Golden Formosa,2019,25.118216,121.53623,Taipei,Taipei,110,Taiwanese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa
+Danny's Steakhouse,2019,25.082657,121.552765,Taipei,Taipei,110,Steakhouse,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse
+L'Atelier de Joël Robuchon,2019,25.039188,121.56771,Taipei,Taipei,110,French contemporary,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759
+Kitcho,2019,25.04429,121.55076,Taipei,Taipei,110,Sushi,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho
+Tien Hsiang Lo,2019,25.062696,121.529976,Taipei,Taipei,110,Hang Zhou,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo
+Ya Ge,2019,25.06625,121.52533,Taipei,Taipei,110,Cantonese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge
+Da-Wan,2019,25.043018,121.55019,Taipei,Taipei,110,Barbecue,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan
+Sushi Nomura,2019,25.03618,121.55465,Taipei,Taipei,110,Sushi,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura
+logy,2019,25.03561,121.55323,Taipei,Taipei,110,Asian contemporary,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy
+Ming Fu,2019,25.06171,121.52399,Taipei,Taipei,110,Taiwanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu
+Ken An Ho,2019,25.034622,121.55281,Taipei,Taipei,110,Japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho
+Sushi Ryu,2019,25.055832,121.52724,Taipei,Taipei,110,Sushi,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu
+MUME,2019,25.036266,121.54809,Taipei,Taipei,110,European contemporary,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume
+Impromptu by Paul Lee,2019,25.05429,121.524254,Taipei,Taipei,110,Innovative,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee
+Longtail,2019,25.023922,121.54841,Taipei,Taipei,110,Innovative,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail
+Mountain and Sea House,2019,25.038046,121.531395,Taipei,Taipei,110,Taiwanese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house
+Da San Yuan,2019,25.042248,121.512215,Taipei,Taipei,110,Cantonese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan
+Tainan Tan Tsu Mien Seafood,2019,25.03799,121.4984,Taipei,Taipei,110,Seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood
+Suan Thip,2019,13.926962,100.502144,Bangkok,Thailand,N/A,Thai,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip
+Upstairs at Mikkeller,2019,13.727946,100.58844,Bangkok,Thailand,N/A,Innovative,$$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller
+Chim by Siam Wisdom,2019,13.740706,100.56789,Bangkok,Thailand,N/A,Thai,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom
+R-Haan,2019,13.731805,100.57972,Bangkok,Thailand,N/A,Thai,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan
+Canvas,2019,13.728886,100.580826,Bangkok,Thailand,N/A,Innovative,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292
+Bo.lan,2019,13.726389,100.57799,Bangkok,Thailand,N/A,Thai,$$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan
+Sra Bua by Kiin Kiin,2019,13.754137,100.53451,Bangkok,Thailand,N/A,Thai Contemporary,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin
+Elements,2019,13.742978,100.54798,Bangkok,Thailand,N/A,French contemporary,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements
+Paste,2019,13.744594,100.540565,Bangkok,Thailand,N/A,Thai,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste
+Ginza Sushi ichi,2019,13.744069,100.54092,Bangkok,Thailand,N/A,Sushi,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi
+Sorn,2019,13.723242,100.56848,Bangkok,Thailand,N/A,Southern Thai,$$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn
+Savelberg,2019,13.738216,100.54642,Bangkok,Thailand,N/A,French contemporary,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg
+Saneh Jaan,2019,13.735739,100.54605,Bangkok,Thailand,N/A,Thai,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan
+Gaa,2019,13.737806,100.54275,Bangkok,Thailand,N/A,Innovative,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa
+Methavalai Sorndaeng,2019,13.756321,100.502045,Bangkok,Thailand,N/A,Thai,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng
+Jay Fai,2019,13.752614,100.50466,Bangkok,Thailand,N/A,Street Food,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai
+Nahm,2019,13.724357,100.53926,Bangkok,Thailand,N/A,Thai,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm
+J'AIME by Jean-Michel Lorain,2019,13.718885,100.546326,Bangkok,Thailand,N/A,French contemporary,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain
+Saawaan,2019,13.721809,100.53741,Bangkok,Thailand,N/A,Thai Contemporary,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan
+Le Du,2019,13.725005,100.52939,Bangkok,Thailand,N/A,Thai,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du
+Ruean Panya,2019,13.558403,100.28264,Bangkok,Thailand,N/A,Thai,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya
+PRU,2019,8.034298,98.2764,Phuket,Thailand,N/A,Innovative,$$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru
+Blue Duck Tavern,2019,38.905373,-77.05128,"Washington, D.C.",Washington DC,20037,American,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern
+Tail Up Goat,2019,38.9235,-77.043274,"Washington, D.C.",Washington DC,20009,Contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat
+Komi,2019,38.910107,-77.03836,"Washington, D.C.",Washington DC,20036,Mediterranean,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi
+Sushi Taro,2019,38.909992,-77.03834,"Washington, D.C.",Washington DC,20036,Japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro
+Plume,2019,38.906,-77.03671,"Washington, D.C.",Washington DC,20036,European,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume
+Siren by RW,2019,38.907776,-77.035446,"Washington, D.C.",Washington DC,20005,Seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw
+Bresca,2019,38.91584,-77.0321,"Washington, D.C.",Washington DC,20000,Contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca
+The Dabney,2019,38.906292,-77.024666,"Washington, D.C.",Washington DC,20001,American,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney
+Métier,2019,38.903503,-77.0218,"Washington, D.C.",Washington DC,20001,Contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier
+Kinship,2019,38.903282,-77.0218,"Washington, D.C.",Washington DC,20001,Contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship
+Fiola,2019,38.89416,-77.02032,"Washington, D.C.",Washington DC,20004,Italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola
+Masseria,2019,38.909504,-76.99908,"Washington, D.C.",Washington DC,20002,Italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria
+Rose's Luxury,2019,38.88064,-76.99515,"Washington, D.C.",Washington DC,20003,Contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury
+Loch Bay,2019,57.51487,-6.57114,Waternish,United Kingdom,IV55 8GA,Modern cuisine,N/A,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay
+Braidwoods,2019,55.69474,-4.7425,Dalry,United Kingdom,KA24 4LN,Classic cuisine,N/A,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods
+Eipic,2019,54.595898,-5.9322424,Belfast,United Kingdom,BT1 6PF,Modern cuisine,N/A,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic
+OX,2019,54.59891,-5.92198,Belfast,United Kingdom,BT1 3LA,Modern British,N/A,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109
+The Peat Inn,2019,56.27861,-2.8845797,Peat Inn,United Kingdom,KY15 5LH,Classic cuisine,N/A,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn
+Kitchin,2019,55.976967,-3.172842,Leith,United Kingdom,EH6 6LX,Modern cuisine,N/A,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin
+Martin Wishart,2019,55.97552,-3.17019,Leith,United Kingdom,EH6 6RA,Modern cuisine,N/A,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart
+Number One,2019,55.95298,-3.18966,Edinburgh,United Kingdom,EH2 2EQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one
+21212,2019,55.95674,-3.18018,Edinburgh,United Kingdom,EH7 5AB,Creative,N/A,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212
+The Cellar,2019,56.222126,-2.696321,Anstruther,United Kingdom,KY10 3AA,Modern cuisine,N/A,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar
+Forest Side,2019,54.46396,-3.0157998,Grasmere,United Kingdom,LA22 9RN,Modern British,N/A,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side
+HRiSHi,2019,54.35013,-2.91172,Bowness-on-Windermere,United Kingdom,LA23 3NE,Modern cuisine,N/A,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi
+Rogan & Co,2019,54.20074,-2.95464,Cartmel,United Kingdom,LA11 6QD,Creative British,N/A,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co
+House of Tides,2019,54.96758,-1.61127,Newcastle upon Tyne,United Kingdom,NE1 3RF,Modern cuisine,N/A,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides
+Sosban & The Old Butchers,2019,53.225925,-4.163006,Menai Bridge/Porthaethwy,United Kingdom,LL59 5EE,Modern cuisine,N/A,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers
+Northcote,2019,53.80933,-2.4476,Langho,United Kingdom,BB6 8BE,Modern British,N/A,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote
+Yorke Arms,2019,54.13435,-1.8183095,Pateley Bridge,United Kingdom,HG3 5RL,Modern cuisine,N/A,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms
+Fraiche,2019,53.38183,-3.04307,Birkenhead,United Kingdom,CH43 5SG,Creative,N/A,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche
+White Swan,2019,53.83916,-2.25097,Fence,United Kingdom,BB12 9QA,Modern British,N/A,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan
+Tyddyn Llan,2019,52.92405,-3.44329,Llandrillo,United Kingdom,LL21 OST,Classic cuisine,N/A,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan
+Ynyshir,2019,52.54476,-3.94491,Machynlleth,United Kingdom,SY20 8TA,Creative,N/A,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir
+Black Swan,2019,54.21205,-1.1879199,Oldstead,United Kingdom,YO61 4BL,Modern British,N/A,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan
+Simon Radley at Chester Grosvenor,2019,53.19064,-2.88913,Chester,United Kingdom,CH1 1LT,Modern cuisine,N/A,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor
+Star Inn at Harome,2019,54.23101,-1.01023,Harome,United Kingdom,YO62 5JE,Modern British,N/A,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome
+The Man Behind The Curtain,2019,53.798286,-1.539937,Leeds,United Kingdom,LS1 7JH,Creative,N/A,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain
+The Checkers,2019,52.55568,-3.13769,Montgomery/Trefaldwyn,United Kingdom,SY15 6PN,French,N/A,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers
+Pipe and Glass,2019,53.8955,-0.53301,South Dalton,United Kingdom,HU17 7PN,Modern British,N/A,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass
+Fischer's at Baslow Hall,2019,53.25055,-1.6269798,Baslow,United Kingdom,DE45 1RR,Modern cuisine,N/A,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall
+Winteringham Fields,2019,53.68778,-0.59013,Winteringham,United Kingdom,DN15 9ND,Modern cuisine,N/A,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields
+Thomas Carr @ The Olive Room,2019,51.209476,-4.118807,Ilfracombe,United Kingdom,EX34 9DJ,Seafood,N/A,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room
+Walnut Tree,2019,51.84534,-2.95438,Llanddewi Skirrid,United Kingdom,NP7 8AW,Modern British,N/A,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree
+Paul Ainsworth at No.6,2019,50.541534,-4.9398518,Padstow,United Kingdom,PL28 8AP,Modern cuisine,N/A,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6
+Simpsons,2019,52.46925,-1.9238801,Birmingham,United Kingdom,B15 3DU,Modern cuisine,N/A,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons
+Purnell's,2019,52.48268,-1.9018,Birmingham,United Kingdom,B3 2DH,Modern cuisine,N/A,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s
+Adam's,2019,52.47964,-1.900286,Birmingham,United Kingdom,B2 5UG,Modern cuisine,N/A,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s
+Outlaw's Fish Kitchen,2019,50.59206,-4.832,Port Isaac,United Kingdom,PL29 3RH,Seafood,N/A,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen
+Carters of Moseley,2019,52.44588,-1.88384,Birmingham,United Kingdom,B13 9EZ,Modern British,N/A,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley
+Peel's,2019,52.4259,-1.70536,Hampton in Arden,United Kingdom,B92 ODQ,Creative British,N/A,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s
+John's House,2019,52.73295,-1.145851,Mountsorrel,United Kingdom,LE12 7AR,Modern cuisine,N/A,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house
+The Whitebrook,2019,51.759697,-2.686909,Whitebrook,United Kingdom,NP25 4TX,Modern British,N/A,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook
+James Sommerin,2019,51.434387,-3.167951,Penarth,United Kingdom,CF64 3AU,Modern cuisine,N/A,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin
+Driftwood,2019,50.1889,-4.97088,Portscatho,United Kingdom,TR2 5EW,Modern cuisine,N/A,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood
+The Cross at Kenilworth,2019,52.350315,-1.5801147,Kenilworth,United Kingdom,CV8 2EZ,Classic cuisine,N/A,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth
+Masons Arms,2019,50.99547,-3.67163,Knowstone,United Kingdom,EX36 4RY,Classic French,N/A,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms
+Salt,2019,52.18963,-1.7092043,Stratford-upon-Avon,United Kingdom,CV37 6HB,Modern British,N/A,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869
+Le Champignon Sauvage,2019,51.89146,-2.07912,Cheltenham,United Kingdom,GL50 2AQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage
+Gidleigh Park,2019,50.67658,-3.87354,Chagford,United Kingdom,TQ13 8HH,Modern cuisine,N/A,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park
+Hambleton Hall,2019,52.65737,-0.66812,Upper Hambleton,United Kingdom,LE15 8TH,Classic cuisine,N/A,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall
+wilks,2019,51.46737,-2.6069899,Bristol,United Kingdom,BS6 6PG,Modern British,N/A,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks
+Bulrush,2019,51.46278,-2.597758,Bristol,United Kingdom,BS6 5TZ,Modern British,N/A,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush
+Casamia,2019,51.447254,-2.5940728,Bristol,United Kingdom,BS1 6FU,Creative,N/A,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia
+Paco Tapas,2019,51.44647,-2.59358,Bristol,United Kingdom,BS1 6FU,Spanish,N/A,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas
+Pony & Trap,2019,51.34938,-2.5925994,Chew Magna,United Kingdom,BS40 8TQ,Modern British,N/A,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap
+The Dining Room,2019,51.58376,-2.14954,Malmesbury,United Kingdom,SN16 0RB,Asian influences,N/A,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454
+Bybrook,2019,51.49382,-2.23212,Castle Combe,United Kingdom,SN14 7HR,Modern British,N/A,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook
+Restaurant Hywel Jones by Lucknam Park,2019,51.45502,-2.25941,Colerne,United Kingdom,SN14 8AZ,Modern British,N/A,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park
+Olive Tree,2019,51.38707,-2.3627,Bath,United Kingdom,BA1 2QF,Modern cuisine,N/A,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree
+Lympstone Manor,2019,50.64069,-3.4186063,Lympstone,United Kingdom,EX8 3NZ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor
+The Neptune,2019,52.9511,0.50927,Hunstanton,United Kingdom,PE36 6HZ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune
+Elephant,2019,50.45853,-3.52438,Torquay,United Kingdom,TQ1 2BH,Modern British,N/A,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant
+Oxford Kitchen,2019,51.776733,-1.264794,Oxford,United Kingdom,OX2 7HQ,Modern British,N/A,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen
+Nut Tree,2019,51.83618,-1.1499,Murcott,United Kingdom,OX5 2RE,Traditional British,N/A,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree
+Morston Hall,2019,52.95473,0.98729,Morston,United Kingdom,NR25 7AA,Modern British,N/A,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall
+Red Lion Freehouse,2019,51.2736,-1.8007996,East Chisenbury,United Kingdom,SN9 6AQ,Classic cuisine,N/A,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse
+Blackbird,2019,51.420887,-1.3493549,Newbury,United Kingdom,RG20 8AQ,Classic cuisine,N/A,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018
+Woodspeen,2019,51.415802,-1.353532,Newbury,United Kingdom,RG20 8BN,Modern cuisine,N/A,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen
+The Coach,2019,51.571712,-0.7772678,Marlow,United Kingdom,SL7 2LS,Modern British,N/A,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach
+Crown,2019,51.524807,-0.7915291,Burchett's Green,United Kingdom,SL6 6QZ,Regional cuisine,N/A,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549
+L'Ortolan,2019,51.40833,-0.9542898,Shinfield,United Kingdom,RG2 9BY,French,N/A,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan
+Hinds Head,2019,51.51265,-0.69641,Bray,United Kingdom,SL6 2AB,Traditional British,N/A,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head
+Black Rat,2019,51.05796,-1.3069497,Winchester,United Kingdom,SO23 0HX,Modern cuisine,N/A,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat
+Matt Worswick at The Latymer,2019,51.35764,-0.7086128,Bagshot,United Kingdom,GU19 5EU,Modern cuisine,N/A,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer
+Coworth Park,2019,51.40463,-0.616715,Ascot,United Kingdom,SL5 7SE,Modern cuisine,N/A,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park
+Tudor Room,2019,51.41759,-0.545847,Egham,United Kingdom,TW20 9UR,Modern cuisine,N/A,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room
+The Glasshouse,2019,51.4771,-0.28574,Kew,United Kingdom,TW9 3PZ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse
+La Trompette,2019,51.49222,-0.25619,Chiswick,United Kingdom,W4 2EU,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette
+Tim Allen's Flitch of Bacon,2019,51.86747,0.404134,Little Dunmow,United Kingdom,CM6 3HT,Modern British,N/A,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon
+River Café,2019,51.48395,-0.22423,Hammersmith,United Kingdom,W6 9HA,Italian,N/A,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe
+Kitchen W8,2019,51.49918,-0.19692,Kensington,United Kingdom,W8 6AH,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8
+Trishna,2019,51.51834,-0.153031,Marylebone,United Kingdom,W1U 3DG,Indian,N/A,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna
+Roganic,2019,51.518314,-0.1520875,Marylebone,United Kingdom,W1U 3DB,Creative British,N/A,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic
+Locanda Locatelli,2019,51.51514,-0.15675,Marylebone,United Kingdom,W1H 7JZ,Italian,N/A,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli
+Texture,2019,51.51488,-0.15664,Marylebone,United Kingdom,W1H 7BY,Creative,N/A,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture
+Portland,2019,51.519726,-0.142555,Regent's Park,United Kingdom,W1W 6QQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland
+Harwood Arms,2019,51.48291,-0.19651,Fulham,United Kingdom,SW6 1QP,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms
+Pied à Terre,2019,51.519,-0.13511,Bloomsbury,United Kingdom,W1T 2NH,Creative,N/A,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre
+The Ninth,2019,51.51875,-0.1350068,Bloomsbury,United Kingdom,W1T 2NB,Mediterranean cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth
+Kai,2019,51.50871,-0.15162,Mayfair,United Kingdom,W1K 2QU,Chinese,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638
+Pollen Street Social,2019,51.5133,-0.14234,Mayfair,United Kingdom,W1S 1NQ,Creative,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social
+Hakkasan Mayfair,2019,51.51037,-0.14475,Mayfair,United Kingdom,W1J 6QB,Chinese,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair
+The Square,2019,51.51069,-0.14401,Mayfair,United Kingdom,W1J 6PU,Creative French,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391
+Alyn Williams at The Westbury,2019,51.51118,-0.14303,Mayfair,United Kingdom,W1S 2YF,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury
+Benares,2019,51.50973,-0.14494,Mayfair,United Kingdom,W1J 6BS,Indian,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares
+Hakkasan Hanway Place,2019,51.5171,-0.13144,Bloomsbury,United Kingdom,W1T 1HD,Chinese,N/A,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place
+Social Eating House,2019,51.51383,-0.13673,Soho,United Kingdom,W1F 7NR,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house
+Galvin at Windows,2019,51.50539,-0.15039,Mayfair,United Kingdom,W1K 1BE,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows
+Murano,2019,51.50707,-0.14722,Mayfair,United Kingdom,W1J 5PP,Italian,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano
+Marcus,2019,51.50171,-0.15614,Belgravia,United Kingdom,SW1X 7RL,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus
+Sabor,2019,51.511368,-0.1396773,Mayfair,United Kingdom,W1B 4BR,Spanish,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor
+Clock House,2019,51.30044,-0.49287,Ripley,United Kingdom,GU23 6AQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house
+Yauatcha Soho,2019,51.51367,-0.1352,Soho,United Kingdom,W1F 0DL,Chinese,N/A,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho
+Céleste,2019,51.502422,-0.1522617,Belgravia,United Kingdom,SW1X 7TA,Creative French,N/A,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste
+Gymkhana,2019,51.50854,-0.14154,Mayfair,United Kingdom,W1S 4JH,Indian,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana
+Amaya,2019,51.49912,-0.15731,Belgravia,United Kingdom,SW1X 8JT,Indian,N/A,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya
+Pétrus,2019,51.49951,-0.15662,Belgravia,United Kingdom,SW1X 8EA,French,N/A,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688
+Veeraswamy,2019,51.51008,-0.13809,Mayfair,United Kingdom,W1B 4RS,Indian,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy
+Barrafina,2019,51.51375,-0.1318,Soho,United Kingdom,W1D 3LL,Spanish,N/A,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina
+Hide,2019,51.506077,-0.144289,Mayfair,United Kingdom,W1J 7NB,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide
+Ritz Restaurant,2019,51.50728,-0.14141,Westminster,United Kingdom,W1J 9BR,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant
+Elystan Street,2019,51.49124,-0.16724,Chelsea,United Kingdom,SW3 3NT,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street
+Seven Park Place,2019,51.50623,-0.13998,Saint James's,United Kingdom,SW1A 1LS,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place
+Ikoyi,2019,51.509212,-0.1334389,Saint James's,United Kingdom,SW1Y 4AH,Creative,N/A,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi
+Aquavit,2019,51.508713,-0.1334072,Saint James's,United Kingdom,SW1Y 4QQ,Scandinavian,N/A,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082
+Five Fields,2019,51.49168,-0.16124,Chelsea,United Kingdom,SW3 2SP,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields
+Dining Room at The Goring,2019,51.49763,-0.14556,Victoria,United Kingdom,SW1W 0JW,Traditional British,N/A,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring
+St John,2019,51.52064,-0.10133,Clerkenwell,United Kingdom,EC1M 4AY,Traditional British,N/A,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john
+Quilon,2019,51.49863,-0.13726,Victoria,United Kingdom,SW1E 6AF,Indian,N/A,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon
+Club Gascon,2019,51.51857,-0.10031,London,United Kingdom,EC1A 9DS,French,N/A,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon
+A. Wong,2019,51.49318,-0.14128,Victoria,United Kingdom,SW1V 1DE,Chinese,N/A,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong
+The Clove Club,2019,51.52711,-0.07927,Shoreditch,United Kingdom,EC1V 9LT,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club
+Leroy,2019,51.524086,-0.0815337,Shoreditch,United Kingdom,EC2A 4NU,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy
+Angler,2019,51.51894,-0.08625,Finsbury,United Kingdom,EC2M 2AF,Seafood,N/A,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235
+Brat,2019,51.52427,-0.0767681,Shoreditch,United Kingdom,E1 6JL,Traditional British,N/A,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat
+Lyle's,2019,51.52378,-0.07704,Shoreditch,United Kingdom,E1 6JJ,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s
+Galvin La Chapelle,2019,51.52032,-0.0781,Spitalfields,United Kingdom,E1 6DY,French,N/A,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle
+City Social,2019,51.51521,-0.08431,City of London,United Kingdom,EC2N 1HQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social
+La Dame de Pic,2019,51.510372,-0.0788981,City of London,United Kingdom,EC3N 4AJ,Modern French,N/A,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic
+Story,2019,51.50268,-0.07774,Bermondsey,United Kingdom,SE1 2JX,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story
+Trinity,2019,51.46336,-0.14148,Clapham Common,United Kingdom,SW4 0JG,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity
+Chez Bruce,2019,51.44607,-0.16553,Wandsworth,United Kingdom,SW17 7EG,French,N/A,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce
+Sorrel,2019,51.228825,-0.3346741,Dorking,United Kingdom,RH4 2JU,Modern British,N/A,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459
+Restaurant Tristan,2019,51.062347,-0.326034,Horsham,United Kingdom,RH12 1HU,Modern British,N/A,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan
+Gravetye Manor,2019,51.08932,-0.05691,Gravetye,United Kingdom,RH19 4LJ,Modern British,N/A,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor
+The Sportsman,2019,51.34392,0.95885,Seasalter,United Kingdom,CT5 4BP,Modern British,N/A,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman
+West House,2019,51.11521,0.64215,Biddenden,United Kingdom,TN27 8AH,Modern British,N/A,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house
+Fordwich Arms,2019,51.295284,1.1261871,Fordwich,United Kingdom,CT2 0DB,Modern cuisine,N/A,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms
+Samphire,2019,49.18488,-2.1057,Saint Helier/Saint-Hélier,United Kingdom,JE2 4TQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987
+Bohemia,2019,49.181225,-2.102417,Saint Helier/Saint-Hélier,United Kingdom,JE2 4UH,Modern cuisine,N/A,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia
diff --git a/data/raw/archive/three-stars-michelin-restaurants.csv b/data/raw/archive/three-stars-michelin-restaurants.csv
new file mode 100644
index 00000000..2fc15b04
--- /dev/null
+++ b/data/raw/archive/three-stars-michelin-restaurants.csv
@@ -0,0 +1,37 @@
+name,year,latitude,longitude,city,region,zipCode,cuisine,price,url
+Amador,2019,48.25406,16.35915,Wien,Austria,1190,Creative,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador
+Manresa,2019,37.22761,-121.98071,South San Francisco,California,95030,Contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa
+Benu,2019,37.78521,-122.39876,San Francisco,California,94105,Asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu
+Quince,2019,37.79762,-122.40337,San Francisco,California,94133,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince
+Atelier Crenn,2019,37.79835,-122.43586,San Francisco,California,94123,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn
+The French Laundry,2019,38.40443,-122.36474,San Francisco,California,94599,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry
+The Restaurant at Meadowood,2019,38.52025,-122.46479,San Francisco,California,94574,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood
+SingleThread,2019,38.612164,-122.869705,San Francisco,California,95448,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread
+Alinea,2019,41.91328,-87.64798,Chicago,Chicago,60614,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea
+Geranium,2019,55.70393,12.57197,København,Denmark,2100 Ø,Creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium
+T'ang Court,2019,22.296572,114.1698,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court
+Bo Innovation,2019,22.27614,114.171,Hong Kong,Hong Kong,N/A,Innovative,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation
+Lung King Heen,2019,22.28665,114.15674,Hong Kong,Hong Kong,N/A,Cantonese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen
+Caprice,2019,22.286646,114.15662,Hong Kong,Hong Kong,N/A,French,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice
+8½ Otto e Mezzo - Bombana,2019,22.28189,114.15839,Hong Kong,Hong Kong,N/A,Italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana
+L'Atelier de Joël Robuchon,2019,22.281199,114.15816,Hong Kong,Hong Kong,N/A,French contemporary,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon
+Sushi Shikon,2019,22.28511,114.15208,Hong Kong,Hong Kong,N/A,Sushi,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon
+The Eight,2019,22.190475,113.54341,Macau,Macau,N/A,Chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight
+Robuchon au Dôme,2019,22.189949,113.54395,Macau,Macau,N/A,French contemporary,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome
+Jade Dragon,2019,22.149137,113.56715,Macau,Macau,N/A,Cantonese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon
+Maaemo,2019,59.910477,10.760408,Oslo,Norway,0191,Modern cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo
+Masa,2019,40.76855,-73.98335,New York,New York City,10019,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa
+Per Se,2019,40.76828,-73.98292,New York,New York City,10019,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se
+Le Bernardin,2019,40.76177,-73.98223,New York,New York City,10019,Seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin
+Eleven Madison Park,2019,40.7417,-73.98712,New York,New York City,10010,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park
+Chef's Table at Brooklyn Fare,2019,40.68872,-73.98581,New York,New York City,10018,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare
+La Yeon,2019,37.555813,127.00517,Seoul,South Korea,N/A,Korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon
+Gaon,2019,37.52265,127.03595,Seoul,South Korea,N/A,Korean,$$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon
+Frantzén,2019,59.33418,18.05812,Stockholm,Sweden,111 22,Modern cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen
+Le Palais,2019,25.049496,121.51674,Taipei,Taipei,110,Cantonese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais
+The Inn at Little Washington,2019,38.713623,-78.16208,"Washington, D.C.",Washington DC,22747,American,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington
+Fat Duck,2019,51.50828,-0.70232,Bray,United Kingdom,SL6 2AQ,Creative,N/A,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck
+Waterside Inn,2019,51.50773,-0.70121,Bray,United Kingdom,SL6 2AT,Classic French,N/A,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn
+Alain Ducasse at The Dorchester,2019,51.50712,-0.1525201,Mayfair,United Kingdom,W1K 1QA,French,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester
+The Araki,2019,51.511826,-0.140389,Mayfair,United Kingdom,W1S 3BF,Japanese,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki
+Gordon Ramsay,2019,51.48546,-0.1620201,Chelsea,United Kingdom,SW3 4HP,French,N/A,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay
diff --git a/data/raw/archive/two-stars-michelin-restaurants.csv b/data/raw/archive/two-stars-michelin-restaurants.csv
new file mode 100644
index 00000000..c12d2a64
--- /dev/null
+++ b/data/raw/archive/two-stars-michelin-restaurants.csv
@@ -0,0 +1,111 @@
+name,year,latitude,longitude,city,region,zipCode,cuisine,price,url
+SENNS.Restaurant,2019,47.83636,13.06389,Salzburg,Austria,5020,Creative,$$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant
+Ikarus,2019,47.79536,13.00695,Salzburg,Austria,5020,Creative,$$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus
+Mraz & Sohn,2019,48.23129,16.37637,Wien,Austria,1200,Creative,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn
+Konstantin Filippou,2019,48.21056,16.37996,Wien,Austria,1010,Modern cuisine,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou
+Silvio Nickol Gourmet Restaurant,2019,48.20558,16.37693,Wien,Austria,1010,Modern cuisine,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant
+Steirereck im Stadtpark,2019,48.20229,16.3805,Wien,Austria,1030,Creative,$$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark
+Baumé,2019,37.4285,-122.14272,South San Francisco,California,94306,Contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume
+Commis,2019,37.82476,-122.25505,San Francisco,California,94601,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis
+Californios,2019,37.75555,-122.417145,San Francisco,California,94110,Mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios
+Lazy Bear,2019,37.760204,-122.41969,San Francisco,California,94110,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear
+Saison,2019,37.76327,-122.41543,San Francisco,California,94107,Californian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison
+Campton Place,2019,37.78923,-122.40665,San Francisco,California,94108,Indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place
+Coi,2019,37.79812,-122.40331,San Francisco,California,94133,Contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi
+Acquerello,2019,37.79167,-122.42131,San Francisco,California,94109,Italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello
+Urasawa,2019,34.067352,-118.40067,Los Angeles,California,90210,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa
+Sushi Ginza Onodera,2019,34.08238,-118.37654,Los Angeles,California,N/A,Japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850
+Somni,2019,34.070095,-118.37649,Los Angeles,California,90048,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni
+Providence,2019,34.083523,-118.33019,Los Angeles,California,90028,Seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence
+n/naka,2019,34.02526,-118.41206,Los Angeles,California,90034,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka
+Vespertine,2019,34.024204,-118.38166,Los Angeles,California,90232,Contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine
+Smyth,2019,41.885,-87.660736,Chicago,Chicago,60607,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth
+Oriole,2019,41.886196,-87.64513,Chicago,Chicago,60661,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole
+Acadia,2019,41.85904,-87.62562,Chicago,Chicago,60616,Contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia
+KOKS,2019,62.13667,-7.022025,Leynar,Denmark,335,Creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780
+Henne Kirkeby Kro,2019,55.72682,8.242021,Henne,Denmark,6854,Classic cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro
+a‚o‚c,2019,55.68305,12.58921,København,Denmark,1302 K,Modern cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac
+noma,2019,55.68332,12.61006,København,Denmark,1432 K,Creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma
+Kadeau Copenhagen,2019,55.67219,12.58848,København,Denmark,1408 K,Modern cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen
+Spondi,2019,37.96525,23.742971,Athína,Greece,116 36,French,$$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi
+Sun Tung Lok,2019,22.30099,114.17221,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok
+Yan Toh Heen,2019,22.293337,114.17395,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen
+Tin Lung Heen,2019,22.30357,114.16007,Hong Kong,Hong Kong,N/A,Cantonese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen
+Tenku RyuGin,2019,22.30331,114.16021,Hong Kong,Hong Kong,N/A,Japanese,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin
+Forum,2019,22.281616,114.1824,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum
+Sushi Saito,2019,22.286467,114.15677,Hong Kong,Hong Kong,N/A,Sushi,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito
+Pierre,2019,22.282127,114.15948,Hong Kong,Hong Kong,N/A,French contemporary,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre
+Ying Jee Club,2019,22.284338,114.156654,Hong Kong,Hong Kong,N/A,Cantonese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club
+Écriture,2019,22.28315,114.1554,Hong Kong,Hong Kong,N/A,French contemporary,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture
+Ta Vie,2019,22.282766,114.15529,Hong Kong,Hong Kong,N/A,Innovative,$$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie
+Amber,2019,22.280596,114.15764,Hong Kong,Hong Kong,N/A,French contemporary,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032
+Kashiwaya,2019,22.280643,114.15674,Hong Kong,Hong Kong,N/A,Japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya
+Onyx,2019,47.49712,19.05067,Budapest,Hungary,1051,Modern cuisine,$$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx
+Feng Wei Ju,2019,22.18996,113.54794,Macau,Macau,N/A,Hunanese and Sichuan,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju
+Golden Flower,2019,22.18753,113.54806,Macau,Macau,N/A,Chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower
+Mizumi (Macau),2019,22.188755,113.54488,Macau,Macau,N/A,Japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau
+The Tasting Room,2019,22.14958,113.56505,Macau,Macau,N/A,French contemporary,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room
+Alain Ducasse at Morpheus,2019,22.14293,113.563774,Macau,Macau,N/A,French contemporary,$$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus
+L'Atelier de Joël Robuchon,2019,40.742905,-74.00769,New York,New York City,N/A,French,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505
+Jungsik,2019,40.718685,-74.00911,New York,New York City,10013,Korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik
+Atera,2019,40.716797,-74.00565,New York,New York City,10013,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera
+Jean-Georges,2019,40.76907,-73.98155,New York,New York City,10023,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges
+Marea,2019,40.76749,-73.98114,New York,New York City,10019,Seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea
+Gabriel Kreuther,2019,40.75397,-73.982025,New York,New York City,10036,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther
+Ichimura at Uchū,2019,40.721832,-73.99012,New York,New York City,10002,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu
+Sushi Ginza Onodera,2019,40.752396,-73.981575,New York,New York City,10017,Japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera
+Ko,2019,40.72909,-73.98448,New York,New York City,10003,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko
+The Modern,2019,40.76106,-73.97628,New York,New York City,10019,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern
+Aquavit,2019,40.76078,-73.97214,New York,New York City,10022,Scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit
+Daniel,2019,40.7666,-73.96745,New York,New York City,10065,French,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel
+Aska,2019,40.71222,-73.96642,New York,New York City,11249,Scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska
+Blanca,2019,40.70492,-73.93354,New York,New York City,11206,Contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca
+Patrick Guilbaud,2019,53.33893,-6.25283,City Centre,Ireland,D2,Modern French,N/A,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud
+Oro,2019,-22.98601,-43.22471,Rio de Janeiro - 22441,Rio de Janeiro,015,creative,$$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro
+Tuju,2019,-23.55653,-46.69083,São Paulo - 05416,Sao Paulo,001,creative,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju
+D.O.M.,2019,-23.56618,-46.66742,São Paulo - 01411,Sao Paulo,011,creative,$$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m
+Kojima,2019,37.52559,127.04193,Seoul,South Korea,N/A,Sushi,$$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima
+Jungsik,2019,37.525585,127.04107,Seoul,South Korea,N/A,Korean contemporary,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965
+Kwonsooksoo,2019,37.526752,127.03571,Seoul,South Korea,N/A,Korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo
+Mingles,2019,37.52219,127.0392,Seoul,South Korea,N/A,Korean contemporary,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles
+Alla Prima,2019,37.51844,127.03008,Seoul,South Korea,N/A,Innovative,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima
+Waku Ghin,2018,1.283175,103.8598,Singapore,Singapore,N/A,Japanese contemporary,$$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin
+Odette,2018,1.2896459,103.8516,Singapore,Singapore,N/A,French contemporary,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette
+Shoukouwa,2018,1.2863698,103.854,Singapore,Singapore,N/A,Sushi,$$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa
+Shisen Hanten,2018,1.302395,103.8365,Singapore,Singapore,N/A,Chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten
+Les Amis,2018,1.3066648,103.8314,Singapore,Singapore,N/A,French,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis
+Fäviken Magasinet,2019,63.43626,13.29337,Järpen,Sweden,837 94,Creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet
+Gastrologik,2019,59.33365,18.0807,Stockholm,Sweden,114 51,Creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik
+Oaxen Krog,2019,59.322342,18.101831,Stockholm,Sweden,11521,Creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog
+Vollmers,2019,55.604427,12.995848,Malmö,Sweden,21133,Creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers
+Daniel Berlin,2019,55.61246,13.99265,Skåne-Tranås,Sweden,273 92,Creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin
+RAW,2019,25.082666,121.559525,Taipei,Taipei,110,Innovative,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw
+Shoun RyuGin,2019,25.08258,121.5595,Taipei,Taipei,110,Japanese contemporary,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin
+Taïrroir,2019,25.082718,121.559296,Taipei,Taipei,110,Innovative,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir
+Sushi Amamoto,2019,25.037945,121.55487,Taipei,Taipei,110,Sushi,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto
+The Guest House,2019,25.04486,121.52198,Taipei,Taipei,110,Sichuan-Huai Yang,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house
+Gaggan,2019,13.737679,100.542145,Bangkok,Thailand,N/A,Innovative,$$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan
+Sühring,2019,13.710947,100.545654,Bangkok,Thailand,N/A,European contemporary,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring
+Mezzaluna,2019,13.722148,100.51691,Bangkok,Thailand,N/A,Innovative,$$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna
+Le Normandie,2019,13.723197,100.513855,Bangkok,Thailand,N/A,French contemporary,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie
+minibar,2019,38.896294,-77.02386,"Washington, D.C.",Washington DC,20004,Contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar
+Pineapple and Pearls,2019,38.880707,-76.99514,"Washington, D.C.",Washington DC,20003,Contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls
+Andrew Fairlie at Gleneagles,2019,56.28339,-3.75149,Auchterarder,United Kingdom,PH3 1NF,Creative French,N/A,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles
+L'Enclume,2019,54.201885,-2.953807,Cartmel,United Kingdom,LA11 6PZ,Creative,N/A,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume
+Raby Hunt,2019,54.56647,-1.689258,Summerhouse,United Kingdom,DL2 3UD,Modern British,N/A,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt
+Moor Hall,2019,53.54162,-2.8895495,Aughton,United Kingdom,L39 6RT,Modern British,N/A,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall
+Restaurant Sat Bains,2019,52.92438,-1.1733198,Nottingham,United Kingdom,NG7 2SA,Creative,N/A,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains
+Restaurant Nathan Outlaw,2019,50.594086,-4.82854,Port Isaac,United Kingdom,PL29 3SB,Seafood,N/A,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw
+Belmond Le Manoir aux Quat' Saisons,2019,51.71674,-1.09187,Great Milton,United Kingdom,OX44 7PD,French,N/A,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons
+Midsummer House,2019,52.21243,0.12779,Cambridge,United Kingdom,CB4 1HA,Modern cuisine,N/A,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house
+Hand and Flowers,2019,51.57031,-0.78113,Marlow,United Kingdom,SL7 2BP,Modern British,N/A,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers
+Ledbury,2019,51.51674,-0.20007,North Kensington,United Kingdom,W11 2AQ,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury
+CORE by Clare Smyth,2019,51.51261,-0.2030516,North Kensington,United Kingdom,W11 2PN,Modern British,N/A,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth
+Le Gavroche,2019,51.51127,-0.15517,Mayfair,United Kingdom,W1K 7QR,French,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche
+Kitchen Table at Bubbledogs,2019,51.52041,-0.13647,Bloomsbury,United Kingdom,W1T 4QG,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs
+Hélène Darroze at The Connaught,2019,51.5101,-0.1495701,Mayfair,United Kingdom,W1K 2AL,Modern cuisine,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught
+Dinner by Heston Blumenthal,2019,51.50208,-0.16011,Hyde Park,United Kingdom,SW1X 7LA,Traditional British,N/A,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal
+Umu,2019,51.5113,-0.14455,Mayfair,United Kingdom,W1J 6LX,Japanese,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu
+Sketch (The Lecture Room & Library),2019,51.51287,-0.14136,Mayfair,United Kingdom,W1S 2XG,Modern French,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library
+Greenhouse,2019,51.50769,-0.14926,Mayfair,United Kingdom,W1J 5NY,Creative,N/A,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393
+Claude Bosi at Bibendum,2019,51.49341,-0.1690893,Chelsea,United Kingdom,SW3 6RD,French,N/A,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum
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-#
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv(\"/Users/ZINA/Desktop/one-star-michelin-restaurants.csv\")\n",
+    "star2_df = pd.read_csv(\"/Users/ZINA/Desktop/two-stars-michelin-restaurants.csv\")\n",
+    "star3_df = pd.read_csv(\"/Users/ZINA/Desktop/three-stars-michelin-restaurants.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Drop unwanted columns\n",
+    "star1_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6a101a8-74e5-4007-98cb-2cdd0d310fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e8cec71f-c813-40d9-92a7-2ea32ada988b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f6ea2440-dbc3-4c55-98b7-051971568e95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a star column\n",
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a0424c0-8c00-4736-bb30-bcf33f8ab101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Concatenating the 3 datasets \n",
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ec4465cb-7e32-4c70-a6b5-6f6179a31f21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace $$$$$ with $$$$\n",
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ff99db1d-7bb4-40ee-b8fb-70a4f192e228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean weird characters\n",
+    "stars_df['price'] = stars_df['price'].str.strip()\n",
+    "stars_df['price'] = stars_df['price'].str.replace(r'\\s+', '', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3fc94f5b-6f23-4f4d-86df-cc6a53b36e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert $ to ordinal numbers\n",
+    "stars_df['price_ordinal'] = stars_df['price'].str.count(r'\\$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5354f300-4299-46bd-a61e-ab56f935e334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute median ordinal per star group (1 star, 2 stars, 3 stars)\n",
+    "median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f2bcea8c-3e8c-458b-9898-caf5e14bec74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace missing ordinal values using matching star median\n",
+    "stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d8afb6f3-10d9-4775-82a8-f49852a2766c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now convert back to $ string after filling\n",
+    "stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d5a1e8ae-c0fe-4281-9755-32a87caa105d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping\n",
+    "price_mean_map = {\n",
+    "    \"$\": 20,\n",
+    "    \"$$\": 37.5,\n",
+    "    \"$$$\": 62.5,\n",
+    "    \"$$$$\": 100\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d22c338b-3d05-4de2-b79f-34d13b6b3746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new column with the mean price\n",
+    "stars_df['price_mean'] = stars_df['price'].map(price_mean_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8876ecd1-48b9-4678-aa1b-32935c85d3ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name             0\n",
+       "year             0\n",
+       "latitude         0\n",
+       "longitude        0\n",
+       "city             2\n",
+       "region           0\n",
+       "cuisine          0\n",
+       "price            0\n",
+       "url              0\n",
+       "stars            0\n",
+       "price_ordinal    0\n",
+       "price_mean       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking if there are no null values in all price columns \n",
+    "stars_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>price_ordinal</th>\n",
+       "      <th>price_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kilian Stuba</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Kleinwalsertal</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vorarlberg/kl...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pfefferschiff</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Hallwang</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Classic cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esszimmer</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Carpe Diem</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Market cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Edvard</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vienna/wien/r...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year            city   region          cuisine price  \\\n",
+       "0   Kilian Stuba  2019  Kleinwalsertal  Austria         Creative  $$$$   \n",
+       "1  Pfefferschiff  2019        Hallwang  Austria  Classic cuisine  $$$$   \n",
+       "2      Esszimmer  2019        Salzburg  Austria         Creative  $$$$   \n",
+       "3     Carpe Diem  2019        Salzburg  Austria   Market cuisine  $$$$   \n",
+       "4         Edvard  2019            Wien  Austria   Modern cuisine  $$$$   \n",
+       "\n",
+       "                                                 url   stars  price_ordinal  \\\n",
+       "0  https://guide.michelin.com/at/en/vorarlberg/kl...  1 star              4   \n",
+       "1  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "2  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "3  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "4  https://guide.michelin.com/at/en/vienna/wien/r...  1 star              4   \n",
+       "\n",
+       "   price_mean  \n",
+       "0       100.0  \n",
+       "1       100.0  \n",
+       "2       100.0  \n",
+       "3       100.0  \n",
+       "4       100.0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop columns\n",
+    "stars_df.drop(columns=['latitude', 'longitude'], inplace=True)\n",
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#put in lower for joining for example 'creative' with 'Creative'\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].str.strip().str.lower() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2f2deecb-3847-4c17-8b99-bb9a5b120344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "international_types = [\"modern cuisine\", \"classic cuisine\",\n",
+    "                       \"street food\", \"meats and grills\", \"international\", \"innovative\"]\n",
+    "\n",
+    "stars_df[\"cuisine_original\"] = stars_df[\"cuisine\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(\n",
+    "    international_types, \"international cuisine\")\n",
+    "\n",
+    "international_sub = stars_df[\n",
+    "    stars_df[\"cuisine_original\"].isin(international_types)]\n",
+    "\n",
+    "ax = international_sub[\"cuisine_original\"].value_counts().plot(kind=\"bar\")\n",
+    "\n",
+    "plt.title(\"Distribution of subtypes within international cuisine\")\n",
+    "plt.ylabel(\"Number of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "note = (\"Modern cuisine combines global flavours with local and seasonal ingredients, \"\"while innovative refers to more experimental concepts such as molecular \"\"gastronomy or 3D‑printed food.\")\n",
+    "\n",
+    "plt.subplots_adjust(bottom=0.3)\n",
+    "\n",
+    "plt.figtext(\n",
+    "    0.5,\n",
+    "    0.02,\n",
+    "    note,\n",
+    "    ha=\"center\",\n",
+    "    va=\"bottom\",\n",
+    "    wrap=True,\n",
+    "    fontsize=9\n",
+    ")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "53a35045-f5f6-4110-b174-5f88bd9093ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "70648bad-2bfc-4a30-8721-5d31abc18443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute, in this case \"Chinese food\" \n",
+    "chinese_type = [\"chinese\", \n",
+    "    \"cantonese\",\n",
+    "    \"cantonese roast meats\",\n",
+    "    \"dim sum\",\n",
+    "    \"shanghainese\",\n",
+    "    \"sichuan\",\n",
+    "    \"hunanese and sichuan\",\n",
+    "    \"sichuan-huai yang\",\n",
+    "    \"fujian\",\n",
+    "    \"taizhou\",\n",
+    "    \"hang zhou\",\n",
+    "    \"noodles and congee\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(chinese_type, \"chinese\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "18eff245-8e00-40d4-a6cb-4215aea72c6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "01836bf8-034e-4cb5-aa25-679c373a6155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute\n",
+    "korean_types = ['korean',\n",
+    "    'korean contemporary',\n",
+    "    'temple cuisine']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "33116181-30c8-4b33-a28a-5f770935da02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e33b6a97-f1f2-4cfa-bc73-3065516a4100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thai_types = ['thai',\n",
+    "    'thai contemporary',\n",
+    "    'southern thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9a198e63-0358-4d15-91e3-3ab25070f5c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1f39652c-5412-47c0-b0bc-4f1e8ca36a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "american_types = ['american',\n",
+    "    'californian',\n",
+    "    'barbecue',\n",
+    "    'steakhouse']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6d1d5eca-2061-44dd-8a89-513fa6d5116d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "46"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "07f0bf7e-e580-4c53-85b5-112004625e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "french_types = ['french',\n",
+    "    'classic french',\n",
+    "    'french contemporary',\n",
+    "    'modern french',\n",
+    "    'creative french']\n",
+    "\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d995f47c-7f09-4a1d-b8b0-af2639174996",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "90196101-a614-4d9e-903d-0ddd2136bc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "japanese_types = ['japanese',\n",
+    "    'sushi',\n",
+    "    'teppanyaki',\n",
+    "    'japanese contemporary']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "b5d63dac-f061-4e69-af34-1913465539ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f7c979b4-e012-4a6e-a95f-cd9a32b31f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_asian_types = ['asian',\n",
+    "    'asian influences',\n",
+    "    'asian contemporary',\n",
+    "    'fusion','taiwanese','peranakan','thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "65715bf6-d434-4c3a-8e7b-e3666faf90a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "197594f3-4e30-4614-826b-050ca5de00d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "british_types = ['modern british',\n",
+    "    'traditional british',\n",
+    "    'creative british']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "112b202f-5d66-43f9-a999-62856ed29d1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c0d2074a-bf80-4057-90b5-e035eb5b4a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_types = ['modern cuisine',\n",
+    "    'modern','modern food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8270bdaf-1ae0-469f-be7d-9197ce9b5e28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "75ce0e4a-0c0f-40d9-bb71-384562cfbdef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'market cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'mediterranean',\n",
+       "       'seafood', 'european contemporary', 'scandinavian', 'austrian',\n",
+       "       'spanish', 'british', 'modern cuisine', 'australian',\n",
+       "       'italian contemporary', 'european', 'regional cuisine',\n",
+       "       'mediterranean cuisine'], dtype=object)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "9ca6b75d-eedc-434e-891d-5d171615f124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market_types = ['classic cuisine','market cuisine', 'regional cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "461da05b-22f3-4242-ade1-69072bf55cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c1b5284a-59cf-4aaa-8c44-1b21594cec3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mediterranean_types = ['mediterranean', 'mediterranean cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9fbc1139-5470-4594-8382-53236b9e62c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'classic cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'mediterranean food',\n",
+       "       'seafood', 'european contemporary', 'scandinavian', 'austrian',\n",
+       "       'spanish', 'british', 'modern cuisine', 'australian',\n",
+       "       'italian contemporary', 'european'], dtype=object)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "42b900bb-af39-442f-94d2-abcab9710dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_european_types = ['european', 'european contemporary','mediterranean food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ec17fcff-6605-477c-a4e6-90b92835266e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "88adeee8-713c-4ce2-8100-7754993de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "italian_types = ['italian', 'italian contemporary']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "aa1fc4be-87bd-4a6f-8cae-c95cc96b2598",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "56b5bfdd-81f6-4b3f-865c-eff95f23a7a7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0b09fd01-fbfc-48f6-98d2-57c2f5bf1b64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'classic cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'other european',\n",
+       "       'seafood', 'scandinavian', 'austrian', 'spanish', 'british',\n",
+       "       'modern cuisine', 'australian'], dtype=object)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e540377f-e185-48a3-b3a4-24b480f13ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "868eea3a-d9d9-49ff-a24d-2ae6e36a8e7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scandinavian_types = ['danish','finnish', 'scandinavian']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "62a2e925-3ccb-4339-89f4-fdeb960ea0c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'japanese', 'vegetarian',\n",
+       "       'contemporary', 'indian', 'korean', 'american', 'moroccan',\n",
+       "       'other asian', 'chinese', 'italian', 'french', 'mexican',\n",
+       "       'gastropub', 'danish', 'finnish', 'other european', 'seafood',\n",
+       "       'scandinavian', 'austrian', 'spanish', 'british', 'australian'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "34edec4d-5578-4197-bba9-65cb44ebf311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "84f3890b-2733-47b9-a271-08f9dc272006",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['$', '$$', '$$$', '$$$$'], dtype=object)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b9ff2db0-a438-4594-8851-21ad6ec96cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "684"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['name'].nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "cb5c0a0b-205e-47c2-99e5-160d2e9b94a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'price_ordinal', 'price_mean', 'cuisine_original'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "4b36c09a-8535-49df-b11d-82c5fae0875a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306                            jin jin\n",
+      "374                        da san yuan\n",
+      "169                            yat lok\n",
+      "308    hill street tai hwa pork noodle\n",
+      "309            putien (kitchener road)\n",
+      "                    ...               \n",
+      "229                    gramercy tavern\n",
+      "228                               noda\n",
+      "225                              nomad\n",
+      "242                              agern\n",
+      "694                      gordon ramsay\n",
+      "Name: name, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Restaurant name standardization - lower case\n",
+    "\n",
+    "stars_df['name']= stars_df['name'].str.lower()\n",
+    "print (stars_df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "30eb95b6-64bf-4412-be57-e3db0536f906",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                             name  year                    city  \\\n",
+      "275                         lasai  2019  Rio de Janeiro - 22271   \n",
+      "309       putien (kitchener road)  2018               Singapore   \n",
+      "194  8 1/2 otto e mezzo - bombana  2019                   Macau   \n",
+      "377         upstairs at mikkeller  2019                 Bangkok   \n",
+      "331         shinji (tanglin road)  2018               Singapore   \n",
+      "\n",
+      "             region                cuisine price  \\\n",
+      "275  Rio de Janeiro  international cuisine  $$$$   \n",
+      "309       Singapore                chinese     $   \n",
+      "194           Macau                italian   $$$   \n",
+      "377        Thailand  international cuisine  $$$$   \n",
+      "331       Singapore               japanese  $$$$   \n",
+      "\n",
+      "                                                   url   stars  price_ordinal  \\\n",
+      "275  https://guide.michelin.com/br/en/rio-de-janeir...  1 star              4   \n",
+      "309  https://guide.michelin.com/sg/en/singapore-reg...  1 star              1   \n",
+      "194  https://guide.michelin.com/mo/en/macau-region/...  1 star              3   \n",
+      "377  https://guide.michelin.com/th/en/bangkok-regio...  1 star              4   \n",
+      "331  https://guide.michelin.com/sg/en/singapore-reg...  1 star              4   \n",
+      "\n",
+      "     price_mean cuisine_original  \n",
+      "275       100.0           modern  \n",
+      "309        20.0           fujian  \n",
+      "194        62.5          italian  \n",
+      "377       100.0       innovative  \n",
+      "331       100.0            sushi  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Trim Excessive Whitespaces:\n",
+    "\n",
+    "stars_df['name'] = stars_df['name'].astype(str)\n",
+    "stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))\n",
+    "\n",
+    "print(stars_df.sample(5)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b6eb2f6a-93b2-47d0-8262-10e3dde375d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# year check\n",
+    "\n",
+    "stars_df['year'].nunique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "6bf643cd-63d0-4762-a772-e99dffa5256a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306    2019\n",
+      "374    2019\n",
+      "169    2019\n",
+      "308    2018\n",
+      "309    2018\n",
+      "       ... \n",
+      "229    2019\n",
+      "228    2019\n",
+      "225    2019\n",
+      "242    2019\n",
+      "694    2019\n",
+      "Name: year, Length: 695, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stars_df['year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "caa32e9d-be69-4ede-80a5-47c40dbbe909",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "179"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# city names check\n",
+    "stars_df['city'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8e50bafb-9f38-4b8a-9575-a65bffc186d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Seoul', 'Taipei', 'Hong Kong', 'Singapore', 'Bangkok', 'Macau',\n",
+       "       'Chicago', 'New York', 'København', 'Los Angeles',\n",
+       "       'Washington, D.C.', 'San Francisco', 'Malmö', 'Stockholm',\n",
+       "       'Blackrock', 'Leith', 'Aird Mhór/Ardmore', 'Belfast',\n",
+       "       'Pateley Bridge', 'Baile Mhic Andáin/Thomastown', 'Baltimore',\n",
+       "       'Edinburgh', 'Peat Inn', 'Westminster', 'Anstruther', 'Grasmere',\n",
+       "       'Bowness-on-Windermere', 'Henne', 'Cill Chainnigh/Kilkenny',\n",
+       "       'Cartmel', 'Gaillimh/Galway', 'Menai Bridge/Porthaethwy',\n",
+       "       'Lios Dúin Bhearna/Lisdoonvarna', 'Ballydehob', 'Corcaigh/Cork',\n",
+       "       'City Centre', 'Langho', 'Newcastle upon Tyne', 'Dalry',\n",
+       "       'City of London', 'Bermondsey', 'Clapham Common', 'Wandsworth',\n",
+       "       'Växjö', 'Dorking', 'Horsham', 'Gravetye', 'Clerkenwell',\n",
+       "       'Victoria', 'London', 'Shoreditch', 'Finsbury', 'Spitalfields',\n",
+       "       'Seasalter', 'Göteborg', 'Chelsea', \"Saint James's\",\n",
+       "       'São Paulo - 04538', 'Saint Helier/Saint-Hélier', 'Biddenden',\n",
+       "       'Fordwich', 'Waternish', 'Birkenhead', 'Bray', 'Winchester',\n",
+       "       'Bagshot', 'Ascot', 'Egham', 'Kew', 'Chiswick', 'Little Dunmow',\n",
+       "       'Hammersmith', 'Kensington', 'Marylebone', 'Shinfield',\n",
+       "       \"Burchett's Green\", 'Colerne', 'Bath', 'Lympstone', 'Hunstanton',\n",
+       "       'Oxford', 'Murcott', 'Morston', 'Costa Mesa', 'East Chisenbury',\n",
+       "       'Newbury', 'Marlow', 'Torquay', \"Regent's Park\", 'Fulham', 'Soho',\n",
+       "       'Mayfair', 'South San Francisco', 'Belgravia', 'Ripley',\n",
+       "       'Bloomsbury', 'Castle Combe', 'Malmesbury', 'Birmingham',\n",
+       "       'Budapest', 'Port Isaac', 'Hampton in Arden', 'Whitebrook',\n",
+       "       'Penarth', 'Portscatho', 'Mountsorrel', nan, 'Padstow',\n",
+       "       'Llandrillo', 'Machynlleth', 'Oldstead', 'Chester',\n",
+       "       'Llanddewi Skirrid', 'Harome', 'Leeds', 'Montgomery/Trefaldwyn',\n",
+       "       'South Dalton', 'Baslow', 'Oslo', 'Winteringham', 'Fence',\n",
+       "       'Kenilworth', 'Chagford', 'Fredericia', 'Vejle', 'Upper Hambleton',\n",
+       "       'Aarhus', 'Bristol', 'Chew Magna', 'Cheltenham', 'Præstø',\n",
+       "       'Knowstone', 'Stratford-upon-Avon', 'Helsingfors / Helsinki',\n",
+       "       'Ilfracombe', 'Kleinwalsertal', 'Salzburg', 'Hyde Park',\n",
+       "       'North Kensington', 'Cambridge', 'Wien', 'Great Milton',\n",
+       "       'Nottingham', 'Aughton', 'Summerhouse', 'Auchterarder',\n",
+       "       'Skåne-Tranås', 'Athína', 'Leynar', 'Järpen', 'São Paulo - 01411',\n",
+       "       'São Paulo - 05416', 'Rio de Janeiro - 22441', 'Rovinj',\n",
+       "       'San Diego', 'Lovran', 'Zagreb', 'Šibenik', 'Dubrovnik',\n",
+       "       'Pedersker', 'Hørve', 'Praha', 'Hallwang', 'Sacramento',\n",
+       "       'Monterey', 'São Paulo - 04080', 'São Paulo - 01401',\n",
+       "       'São Paulo - 04509', 'São Paulo - 05706', 'São Paulo - 04531',\n",
+       "       'São Paulo - 01426', 'São Paulo - 05415', 'São Paulo - 05413',\n",
+       "       'Rio de Janeiro - 22021', 'Rio de Janeiro - 22271', 'Phuket',\n",
+       "       'Rio de Janeiro - 22470', 'Warszawa', 'Stavanger', 'Trondheim'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3fad5dfe-be46-45e6-9609-414aa470e0db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306        Seoul\n",
+      "374       Taipei\n",
+      "169    Hong Kong\n",
+      "308    Singapore\n",
+      "309    Singapore\n",
+      "         ...    \n",
+      "229     New York\n",
+      "228     New York\n",
+      "225     New York\n",
+      "242     New York\n",
+      "694      Chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Remove Leading/Trailing Spaces\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.strip()\n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e8e7151e-b4db-4076-835e-cd82139b24e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306        seoul\n",
+      "374       taipei\n",
+      "169    hong kong\n",
+      "308    singapore\n",
+      "309    singapore\n",
+      "         ...    \n",
+      "229     new york\n",
+      "228     new york\n",
+      "225     new york\n",
+      "242     new york\n",
+      "694      chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#convert to lower case\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.lower() \n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "654fc3c3-545b-4115-8f8b-a2514b44e138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                name  year       city          region  \\\n",
+      "306                          jin jin  2019      seoul     South Korea   \n",
+      "374                      da san yuan  2019     taipei          Taipei   \n",
+      "169                          yat lok  2019  hong kong       Hong Kong   \n",
+      "308  hill street tai hwa pork noodle  2018  singapore       Singapore   \n",
+      "309          putien (kitchener road)  2018  singapore       Singapore   \n",
+      "..                               ...   ...        ...             ...   \n",
+      "229                  gramercy tavern  2019   new york   New York City   \n",
+      "228                             noda  2019   new york   New York City   \n",
+      "225                            nomad  2019   new york   New York City   \n",
+      "242                            agern  2019   new york   New York City   \n",
+      "694                    gordon ramsay  2019    chelsea  United Kingdom   \n",
+      "\n",
+      "                   cuisine price  \\\n",
+      "306                chinese     $   \n",
+      "374                chinese     $   \n",
+      "169                chinese     $   \n",
+      "308  international cuisine     $   \n",
+      "309                chinese     $   \n",
+      "..                     ...   ...   \n",
+      "229           contemporary  $$$$   \n",
+      "228               japanese  $$$$   \n",
+      "225           contemporary  $$$$   \n",
+      "242           scandinavian  $$$$   \n",
+      "694                 french  $$$$   \n",
+      "\n",
+      "                                                   url    stars  \\\n",
+      "306  https://guide.michelin.com/kr/en/seoul-capital...   1 star   \n",
+      "374  https://guide.michelin.com/tw/en/taipei-region...   1 star   \n",
+      "169  https://guide.michelin.com/hk/en/hong-kong-reg...   1 star   \n",
+      "308  https://guide.michelin.com/sg/en/singapore-reg...   1 star   \n",
+      "309  https://guide.michelin.com/sg/en/singapore-reg...   1 star   \n",
+      "..                                                 ...      ...   \n",
+      "229  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "228  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "225  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "242  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "694  https://guide.michelin.com/gb/en/greater-londo...  3 stars   \n",
+      "\n",
+      "     price_ordinal  price_mean       cuisine_original  \n",
+      "306              1        20.0                chinese  \n",
+      "374              1        20.0              cantonese  \n",
+      "169              1        20.0  cantonese roast meats  \n",
+      "308              1        20.0            street food  \n",
+      "309              1        20.0                 fujian  \n",
+      "..             ...         ...                    ...  \n",
+      "229              4       100.0           contemporary  \n",
+      "228              4       100.0               japanese  \n",
+      "225              4       100.0           contemporary  \n",
+      "242              4       100.0           scandinavian  \n",
+      "694              4       100.0                 french  \n",
+      "\n",
+      "[571 rows x 11 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#check for duplicates\n",
+    "\n",
+    "duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ee3f59d0-5adb-468b-86db-bd6ea87e1463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# order A–Z\n",
+    "\n",
+    "#stars_df = stars_df.sort_values(by=\"city\")  \n",
+    "#stars_df['city'].unique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "9a70b42b-accf-46fc-a32a-2377e25b0024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove numbers and zip codes\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def clean_city_name(city_name):\n",
+    "    if isinstance(city_name, str):  # Check if the input is a string\n",
+    "        # Use regex to remove \" - numbers\" at the end of the string\n",
+    "        return re.sub(r'\\s-\\s\\d+$', '', city_name).strip()\n",
+    "    return city_name  # Return as is if it's not a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "bb772e24-05ae-41ac-9d33-521afbb9068c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].apply(clean_city_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "565e3036-376f-42ae-ab1a-262ae8a046de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['seoul' 'taipei' 'hong kong' 'singapore' 'bangkok' 'macau' 'chicago'\n",
+      " 'new york' 'københavn' 'los angeles' 'washington, d.c.' 'san francisco'\n",
+      " 'malmö' 'stockholm' 'blackrock' 'leith' 'aird mhór/ardmore' 'belfast'\n",
+      " 'pateley bridge' 'baile mhic andáin/thomastown' 'baltimore' 'edinburgh'\n",
+      " 'peat inn' 'westminster' 'anstruther' 'grasmere' 'bowness-on-windermere'\n",
+      " 'henne' 'cill chainnigh/kilkenny' 'cartmel' 'gaillimh/galway'\n",
+      " 'menai bridge/porthaethwy' 'lios dúin bhearna/lisdoonvarna' 'ballydehob'\n",
+      " 'corcaigh/cork' 'city centre' 'langho' 'newcastle upon tyne' 'dalry'\n",
+      " 'city of london' 'bermondsey' 'clapham common' 'wandsworth' 'växjö'\n",
+      " 'dorking' 'horsham' 'gravetye' 'clerkenwell' 'victoria' 'london'\n",
+      " 'shoreditch' 'finsbury' 'spitalfields' 'seasalter' 'göteborg' 'chelsea'\n",
+      " \"saint james's\" 'são paulo' 'saint helier/saint-hélier' 'biddenden'\n",
+      " 'fordwich' 'waternish' 'birkenhead' 'bray' 'winchester' 'bagshot' 'ascot'\n",
+      " 'egham' 'kew' 'chiswick' 'little dunmow' 'hammersmith' 'kensington'\n",
+      " 'marylebone' 'shinfield' \"burchett's green\" 'colerne' 'bath' 'lympstone'\n",
+      " 'hunstanton' 'oxford' 'murcott' 'morston' 'costa mesa' 'east chisenbury'\n",
+      " 'newbury' 'marlow' 'torquay' \"regent's park\" 'fulham' 'soho' 'mayfair'\n",
+      " 'south san francisco' 'belgravia' 'ripley' 'bloomsbury' 'castle combe'\n",
+      " 'malmesbury' 'birmingham' 'budapest' 'port isaac' 'hampton in arden'\n",
+      " 'whitebrook' 'penarth' 'portscatho' 'mountsorrel' nan 'padstow'\n",
+      " 'llandrillo' 'machynlleth' 'oldstead' 'chester' 'llanddewi skirrid'\n",
+      " 'harome' 'leeds' 'montgomery/trefaldwyn' 'south dalton' 'baslow' 'oslo'\n",
+      " 'winteringham' 'fence' 'kenilworth' 'chagford' 'fredericia' 'vejle'\n",
+      " 'upper hambleton' 'aarhus' 'bristol' 'chew magna' 'cheltenham' 'præstø'\n",
+      " 'knowstone' 'stratford-upon-avon' 'helsingfors / helsinki' 'ilfracombe'\n",
+      " 'kleinwalsertal' 'salzburg' 'hyde park' 'north kensington' 'cambridge'\n",
+      " 'wien' 'great milton' 'nottingham' 'aughton' 'summerhouse' 'auchterarder'\n",
+      " 'skåne-tranås' 'athína' 'leynar' 'järpen' 'rio de janeiro' 'rovinj'\n",
+      " 'san diego' 'lovran' 'zagreb' 'šibenik' 'dubrovnik' 'pedersker' 'hørve'\n",
+      " 'praha' 'hallwang' 'sacramento' 'monterey' 'phuket' 'warszawa'\n",
+      " 'stavanger' 'trondheim']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verify results\n",
+    "\n",
+    "print(stars_df['city'].unique())  # Display unique city names to verify the cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "7ca0d239-d0d6-4bf6-a80b-52abfe117f87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove special characters \n",
+    "stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "ebe148d4-da9d-49bf-95cb-7dc791e9dbe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bb93350d-af11-4757-b2ea-fe2ac99ca466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "efa0705f-be86-4f5d-b89a-9831265b29e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'unidecode'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[72]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m#the city column are stripped of accents and are presented in ASCII format.\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01munidecode\u001b[39;00m\n\u001b[32m      4\u001b[39m stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m] = stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m].astype(\u001b[38;5;28mstr\u001b[39m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: unidecode.unidecode(x))\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'unidecode'"
+     ]
+    }
+   ],
+   "source": [
+    "#the city column are stripped of accents and are presented in ASCII format.\n",
+    "\n",
+    "import unidecode\n",
+    "stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "94bb2635-47d6-47df-aa22-d0d1d1d86463",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Seoul' 'Taipei' 'Hong Kong' 'Singapore' 'Bangkok' 'Macau' 'Chicago'\n",
+      " 'New York' 'København' 'Los Angeles' 'Washington, D.C.' 'San Francisco'\n",
+      " 'Malmö' 'Stockholm' 'Blackrock' 'Leith' 'Aird Mhór Ardmore' 'Belfast'\n",
+      " 'Pateley Bridge' 'Baile Mhic Andáin Thomastown' 'Baltimore' 'Edinburgh'\n",
+      " 'Peat Inn' 'Westminster' 'Anstruther' 'Grasmere' 'Bowness On Windermere'\n",
+      " 'Henne' 'Cill Chainnigh Kilkenny' 'Cartmel' 'Gaillimh Galway'\n",
+      " 'Menai Bridge Porthaethwy' 'Lios Dúin Bhearna Lisdoonvarna' 'Ballydehob'\n",
+      " 'Corcaigh Cork' 'City Centre' 'Langho' 'Newcastle Upon Tyne' 'Dalry'\n",
+      " 'City Of London' 'Bermondsey' 'Clapham Common' 'Wandsworth' 'Växjö'\n",
+      " 'Dorking' 'Horsham' 'Gravetye' 'Clerkenwell' 'Victoria' 'London'\n",
+      " 'Shoreditch' 'Finsbury' 'Spitalfields' 'Seasalter' 'Göteborg' 'Chelsea'\n",
+      " \"Saint James'S\" 'São Paulo' 'Saint Helier Saint Hélier' 'Biddenden'\n",
+      " 'Fordwich' 'Waternish' 'Birkenhead' 'Bray' 'Winchester' 'Bagshot' 'Ascot'\n",
+      " 'Egham' 'Kew' 'Chiswick' 'Little Dunmow' 'Hammersmith' 'Kensington'\n",
+      " 'Marylebone' 'Shinfield' \"Burchett'S Green\" 'Colerne' 'Bath' 'Lympstone'\n",
+      " 'Hunstanton' 'Oxford' 'Murcott' 'Morston' 'Costa Mesa' 'East Chisenbury'\n",
+      " 'Newbury' 'Marlow' 'Torquay' \"Regent'S Park\" 'Fulham' 'Soho' 'Mayfair'\n",
+      " 'South San Francisco' 'Belgravia' 'Ripley' 'Bloomsbury' 'Castle Combe'\n",
+      " 'Malmesbury' 'Birmingham' 'Budapest' 'Port Isaac' 'Hampton In Arden'\n",
+      " 'Whitebrook' 'Penarth' 'Portscatho' 'Mountsorrel' nan 'Padstow'\n",
+      " 'Llandrillo' 'Machynlleth' 'Oldstead' 'Chester' 'Llanddewi Skirrid'\n",
+      " 'Harome' 'Leeds' 'Montgomery Trefaldwyn' 'South Dalton' 'Baslow' 'Oslo'\n",
+      " 'Winteringham' 'Fence' 'Kenilworth' 'Chagford' 'Fredericia' 'Vejle'\n",
+      " 'Upper Hambleton' 'Aarhus' 'Bristol' 'Chew Magna' 'Cheltenham' 'Præstø'\n",
+      " 'Knowstone' 'Stratford Upon Avon' 'Helsingfors   Helsinki' 'Ilfracombe'\n",
+      " 'Kleinwalsertal' 'Salzburg' 'Hyde Park' 'North Kensington' 'Cambridge'\n",
+      " 'Wien' 'Great Milton' 'Nottingham' 'Aughton' 'Summerhouse' 'Auchterarder'\n",
+      " 'Skåne Tranås' 'Athína' 'Leynar' 'Järpen' 'Rio De Janeiro' 'Rovinj'\n",
+      " 'San Diego' 'Lovran' 'Zagreb' 'Šibenik' 'Dubrovnik' 'Pedersker' 'Hørve'\n",
+      " 'Praha' 'Hallwang' 'Sacramento' 'Monterey' 'Phuket' 'Warszawa'\n",
+      " 'Stavanger' 'Trondheim']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verify the results\n",
+    "print(stars_df['city'].unique()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "33b41649-7526-40c2-9a6a-9cd76700334a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grouping suburbs into major city and add info in a new column \n",
+    "\n",
+    "#create a dictionary \n",
+    "\n",
+    "location_map = {\n",
+    "    # London + neighborhoods\n",
+    "    'north kensington': 'London',\n",
+    "    'kensington': 'London',\n",
+    "    'westminster': 'London',\n",
+    "    'soho': 'London',\n",
+    "    'mayfair': 'London',\n",
+    "    'marylebone': 'London',\n",
+    "    'chelsea': 'London',\n",
+    "    'clapham common': 'London',\n",
+    "    \"regent's park\": 'London',\n",
+    "    'shoreditch': 'London',\n",
+    "    'spitalfields': 'London',\n",
+    "    'belgravia': 'London',\n",
+    "    'bloomsbury': 'London',\n",
+    "    'finsbury': 'London',\n",
+    "    'fulham': 'London',\n",
+    "    'chiswick': 'London',\n",
+    "    'city centre': 'London',\n",
+    "    'city of london': 'London',\n",
+    "    'hyde park': 'London',\n",
+    "    # San Francisco\n",
+    "    'south san francisco': 'San Francisco',\n",
+    "    # Ireland\n",
+    "    'baile mhic andáin/thomastown': 'Thomastown',\n",
+    "    'gaillimh/galway': 'Galway',\n",
+    "    'cill chainnigh/kilkenny': 'Kilkenny',\n",
+    "    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',\n",
+    "    'athína': 'Athens',\n",
+    "    'ballydehob': 'Ballydehob',\n",
+    "    # Finland\n",
+    "    'helsingfors / helsinki': 'Helsinki',\n",
+    "    # Czech Republic\n",
+    "    'praha': 'Prague',\n",
+    "    # Austria\n",
+    "    'wien': 'Vienna',\n",
+    "    'salzburg': 'Salzburg',\n",
+    "    # Menai Bridge\n",
+    "    'menai bridge/porthaethwy': 'Menai Bridge',\n",
+    "    # USA cities\n",
+    "    'los angeles': 'Los Angeles',\n",
+    "    'san diego': 'San Diego',\n",
+    "    'sacramento': 'Sacramento',\n",
+    "    'new york': 'New York',\n",
+    "    'chicago': 'Chicago',\n",
+    "    'costa mesa': 'Costa Mesa',\n",
+    "    'monterey': 'Monterey',\n",
+    "    'washington, d.c.': 'Washington D.C.',\n",
+    "    'south dalton': 'Dalton',\n",
+    "    # Asia\n",
+    "    'bangkok': 'Bangkok',\n",
+    "    'phuket': 'Phuket',\n",
+    "    'hong kong': 'Hong Kong',\n",
+    "    'taipei': 'Taipei',\n",
+    "    'seoul': 'Seoul',\n",
+    "    'singapore': 'Singapore',\n",
+    "    'macau': 'Macau',\n",
+    "    # Croatia\n",
+    "    'lovran': 'Lovran',\n",
+    "    'rovinj': 'Rovinj',\n",
+    "    'zagreb': 'Zagreb',\n",
+    "    'šibenik': 'Sibenik',\n",
+    "    # Norway / Scandinavia\n",
+    "    'stavanger': 'Stavanger',\n",
+    "    'trondheim': 'Trondheim',\n",
+    "    'oslo': 'Oslo',\n",
+    "    'göteborg': 'Gothenburg',\n",
+    "    'växjö': 'Vaxjo',\n",
+    "    'skåne-tranås': 'Skane-Tranas',\n",
+    "    'vejle': 'Vejle',\n",
+    "    # Denmark\n",
+    "    'fredericia': 'Fredericia',\n",
+    "    'pedersker': 'Pedersker',\n",
+    "    'præstø': 'Praesto',\n",
+    "    # Sweden\n",
+    "    'malmö': 'Malmo',\n",
+    "    'stockholm': 'Stockholm',\n",
+    "    # Portugal / Ireland / UK misc\n",
+    "    'bath': 'Bath',\n",
+    "    'bristol': 'Bristol',\n",
+    "    'cambridge': 'Cambridge',\n",
+    "    'cheltenham': 'Cheltenham',\n",
+    "    'chester': 'Chester',\n",
+    "    'birmingham': 'Birmingham',\n",
+    "    'edinburgh': 'Edinburgh',\n",
+    "    'leeds': 'Leeds',\n",
+    "    'oxford': 'Oxford',\n",
+    "    'stratford-upon-avon': 'Stratford-Upon-Avon',\n",
+    "    'padstow': 'Padstow',\n",
+    "    'torquay': 'Torquay',\n",
+    "    'newcastle upon tyne': 'Newcastle upon Tyne',\n",
+    "    'nottingham': 'Nottingham',\n",
+    "    'bray': 'Bray',\n",
+    "    'bowness-on-windermere': 'Bowness-on-Windermere',\n",
+    "    'cartmel': 'Cartmel',\n",
+    "    'castle combe': 'Castle Combe',\n",
+    "    'chagford': 'Chagford',\n",
+    "    'chew magna': 'Chew Magna',\n",
+    "    'dalry': 'Dalry',\n",
+    "    'dorking': 'Dorking',\n",
+    "    'egham': 'Egham',\n",
+    "    'fence': 'Fence',\n",
+    "    'fordwich': 'Fordwich',\n",
+    "    'grasmere': 'Grasmere',\n",
+    "    'gravetye': 'Gravetye',\n",
+    "    'great milton': 'Great Milton',\n",
+    "    'hallwang': 'Hallwang',\n",
+    "    'hampton in arden': 'Hampton in Arden',\n",
+    "    'harome': 'Harome',\n",
+    "    'henne': 'Henne',\n",
+    "    'horsham': 'Horsham',\n",
+    "    'hunstanton': 'Hunstanton',\n",
+    "    'ilfracombe': 'Ilfracombe',\n",
+    "    'järpen': 'Jarpen',\n",
+    "    'kenilworth': 'Kenilworth',\n",
+    "    'kew': 'Kew',\n",
+    "    'kleinwalsertal': 'Kleinwalsertal',\n",
+    "    'knowstone': 'Knowstone',\n",
+    "    'langho': 'Langho',\n",
+    "    'leith': 'Leith',\n",
+    "    'leynar': 'Leynar',\n",
+    "    'little dunmow': 'Little Dunmow',\n",
+    "    'llanddewi skirrid': 'Llanddewi Skirrid',\n",
+    "    'llandrillo': 'Llandrillo',\n",
+    "    'lovran': 'Lovran',\n",
+    "    'lympstone': 'Lympstone',\n",
+    "    'machynlleth': 'Machynlleth',\n",
+    "    'malmesbury': 'Malmesbury',\n",
+    "    'marlow': 'Marlow',\n",
+    "    'morston': 'Morston',\n",
+    "    'mountsorrel': 'Mountsorrel',\n",
+    "    'murcott': 'Murcott',\n",
+    "    'newbury': 'Newbury',\n",
+    "    'oldstead': 'Oldstead',\n",
+    "    'peat inn': 'Peat Inn',\n",
+    "    'penarth': 'Penarth',\n",
+    "    'port isaac': 'Port Isaac',\n",
+    "    'portscatho': 'Portscatho',\n",
+    "    'ripley': 'Ripley',\n",
+    "    'saint helier/saint-hélier': 'Saint Helier',\n",
+    "    \"saint james's\": 'Saint James',\n",
+    "    'seasalter': 'Seasalter',\n",
+    "    'shinfield': 'Shinfield',\n",
+    "    'summerhouse': 'Summerhouse',\n",
+    "    'upper hambleton': 'Hambleton',\n",
+    "    'victoria': 'Victoria',\n",
+    "    'wandsworth': 'London',\n",
+    "    'whitebrook': 'Whitebrook',\n",
+    "    'winchester': 'Winchester',\n",
+    "    'winteringham': 'Winteringham'\n",
+    "}\n",
+    "\n",
+    "stars_df['major_city'] = stars_df['city'].replace(location_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d936e600-5eaf-4813-a493-029cd4447b44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              city     major_city\n",
+      "15   San Francisco  San Francisco\n",
+      "568    Los Angeles    Los Angeles\n",
+      "672      Hong Kong      Hong Kong\n",
+      "558  San Francisco  San Francisco\n",
+      "19   San Francisco  San Francisco\n",
+      "46   San Francisco  San Francisco\n",
+      "597       New York       New York\n",
+      "30   San Francisco  San Francisco\n",
+      "546       Fordwich       Fordwich\n",
+      "544      Seasalter      Seasalter\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stars_df[['city', 'major_city']].sample(10))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "781e121d-126d-491e-871d-526563dc4d7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method Series.unique of 306                            jin jin\n",
+       "374                        da san yuan\n",
+       "169                            yat lok\n",
+       "308    hill street tai hwa pork noodle\n",
+       "309            putien (kitchener road)\n",
+       "                    ...               \n",
+       "229                    gramercy tavern\n",
+       "228                               noda\n",
+       "225                              nomad\n",
+       "242                              agern\n",
+       "694                      gordon ramsay\n",
+       "Name: name, Length: 695, dtype: object>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['name'].unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "7256d411-b0f0-4aec-8e92-4888ba57806c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>price_ordinal</th>\n",
+       "      <th>price_mean</th>\n",
+       "      <th>cuisine_original</th>\n",
+       "      <th>major_city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>odette</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>french</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/sg/en/singapore-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>french contemporary</td>\n",
+       "      <td>Singapore</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>waku ghin</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/sg/en/singapore-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>japanese contemporary</td>\n",
+       "      <td>Singapore</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>kojima</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Seoul</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/kr/en/seoul-capital...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>sushi</td>\n",
+       "      <td>Seoul</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>d.o.m.</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>Sao Paulo</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/br/en/sao-paulo-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>São Paulo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>tuju</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>Sao Paulo</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/br/en/sao-paulo-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>São Paulo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>gramercy tavern</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>noda</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>nomad</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>agern</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>scandinavian</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>scandinavian</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>gordon ramsay</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Chelsea</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>french</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/gb/en/greater-londo...</td>\n",
+       "      <td>3 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>french</td>\n",
+       "      <td>Chelsea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                name  year       city          region       cuisine price  \\\n",
+       "620           odette  2018  Singapore       Singapore        french  $$$$   \n",
+       "619        waku ghin  2018  Singapore       Singapore      japanese  $$$$   \n",
+       "614           kojima  2019      Seoul     South Korea      japanese  $$$$   \n",
+       "613           d.o.m.  2019  São Paulo       Sao Paulo      creative  $$$$   \n",
+       "612             tuju  2019  São Paulo       Sao Paulo      creative  $$$$   \n",
+       "..               ...   ...        ...             ...           ...   ...   \n",
+       "229  gramercy tavern  2019   New York   New York City  contemporary  $$$$   \n",
+       "228             noda  2019   New York   New York City      japanese  $$$$   \n",
+       "225            nomad  2019   New York   New York City  contemporary  $$$$   \n",
+       "242            agern  2019   New York   New York City  scandinavian  $$$$   \n",
+       "694    gordon ramsay  2019    Chelsea  United Kingdom        french  $$$$   \n",
+       "\n",
+       "                                                   url    stars  \\\n",
+       "620  https://guide.michelin.com/sg/en/singapore-reg...  2 stars   \n",
+       "619  https://guide.michelin.com/sg/en/singapore-reg...  2 stars   \n",
+       "614  https://guide.michelin.com/kr/en/seoul-capital...  2 stars   \n",
+       "613  https://guide.michelin.com/br/en/sao-paulo-reg...  2 stars   \n",
+       "612  https://guide.michelin.com/br/en/sao-paulo-reg...  2 stars   \n",
+       "..                                                 ...      ...   \n",
+       "229  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "228  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "225  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "242  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "694  https://guide.michelin.com/gb/en/greater-londo...  3 stars   \n",
+       "\n",
+       "     price_ordinal  price_mean       cuisine_original major_city  \n",
+       "620              4       100.0    french contemporary  Singapore  \n",
+       "619              4       100.0  japanese contemporary  Singapore  \n",
+       "614              4       100.0                  sushi      Seoul  \n",
+       "613              4       100.0               creative  São Paulo  \n",
+       "612              4       100.0               creative  São Paulo  \n",
+       "..             ...         ...                    ...        ...  \n",
+       "229              4       100.0           contemporary   New York  \n",
+       "228              4       100.0               japanese   New York  \n",
+       "225              4       100.0           contemporary   New York  \n",
+       "242              4       100.0           scandinavian   New York  \n",
+       "694              4       100.0                 french    Chelsea  \n",
+       "\n",
+       "[200 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.tail(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "b27be073-e390-4f35-874b-f82d3b6f75f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(695, 12)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "92c35efa-5040-43ab-870b-7e196664e5cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "4bf9e732-a173-44a0-9ad1-467b769dfd7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stars_df[\"cuisine\"].value_counts().head(10).plot(kind=\"bar\")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Cuisine\")\n",
+    "plt.ylabel(\"Nº of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "9c163a3c-a403-4bb4-a9f1-0c6a6a56abdd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_cuisines = (\n",
+    "    stars_df[\"cuisine\"].value_counts()\n",
+    "    .head(10)\n",
+    "    .index\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.countplot(\n",
+    "    data=stars_df[stars_df[\"cuisine\"].isin(top_cuisines)],\n",
+    "    y=\"cuisine\",\n",
+    "    order=top_cuisines\n",
+    ")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Nº of restaurants\")\n",
+    "plt.ylabel(\"Cuisine\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb894fca-55db-4cf2-b4df-f73dda35806a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"stars_n\"] = stars_df[\"stars\"].str[0].astype(int)\n",
+    "\n",
+    "stars_df.groupby(\"region\")[\"stars_n\"].mean().sort_values().plot(kind=\"bar\")\n",
+    "plt.title(\"Mean of Stars per region\")\n",
+    "plt.xlabel(\"Region\")\n",
+    "plt.ylabel(\"Mean of stars\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3acfdf4-815b-4471-8fe9-a1eca7c437d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visuals: \n",
+    "#which are the cities with more restaurants? \n",
+    "#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?\n",
+    "# which are the cuisine dominating a city /region \n",
+    "# avg price point in a specific city based on restaurants?\n",
+    "# time series 2018 vs 2019 any trend? any star restautant grew over past year? \n",
+    "# cheapest vs most expensive cuisine? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "3ec45bca-a08c-475c-824e-f2422a6a70ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SEARCH: chef's table at brooklyn fare, New York -> OK\n",
+      "DETAILS: ChIJiVVSAE1awokRzdvqVP_z3aA -> OK\n",
+      "SEARCH: eleven madison park, New York -> OK\n",
+      "DETAILS: ChIJEWbXz6ZZwokRLKmKrtPfVFY -> OK\n",
+      "SEARCH: per se, New York -> OK\n",
+      "DETAILS: ChIJp3PsL_ZYwokRZYqs_40RJF4 -> OK\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "API_KEY = \"AIzaSyC8VCFpZ1WhNUegfd5ziwZPVRCe1pi35lo\"\n",
+    "\n",
+    "def get_place_rating(name, city, api_key=API_KEY, sleep_sec=0.2):\n",
+    "    query = f\"{name}, {city}\"\n",
+    "    url_search = \"https://maps.googleapis.com/maps/api/place/findplacefromtext/json\"\n",
+    "    params_search = {\n",
+    "        \"input\": query,\n",
+    "        \"inputtype\": \"textquery\",\n",
+    "        \"fields\": \"place_id\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    r = requests.get(url_search, params=params_search)\n",
+    "    data = r.json()\n",
+    "    status = data.get(\"status\")\n",
+    "    print(\"SEARCH:\", query, \"->\", status)\n",
+    "    if status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    place_id = data[\"candidates\"][0][\"place_id\"]\n",
+    "\n",
+    "    url_details = \"https://maps.googleapis.com/maps/api/place/details/json\"\n",
+    "    params_details = {\n",
+    "        \"place_id\": place_id,\n",
+    "        \"fields\": \"rating,user_ratings_total\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    d = requests.get(url_details, params=params_details)\n",
+    "    det = d.json()\n",
+    "    d_status = det.get(\"status\")\n",
+    "    print(\"DETAILS:\", place_id, \"->\", d_status)\n",
+    "    if d_status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    time.sleep(sleep_sec)\n",
+    "    result = det.get(\"result\", {})\n",
+    "    return result.get(\"rating\"), result.get(\"user_ratings_total\")\n",
+    "\n",
+    "\n",
+    "# inicializar colunas (se ainda não existirem)\n",
+    "if \"Review_rating\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_rating\"] = np.nan\n",
+    "if \"Review_count\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_count\"] = np.nan\n",
+    "\n",
+    "# lista de restaurantes (ou partes do nome) que queres atualizar\n",
+    "target_names = [\"eleven madison park\", \"per se\", \"chef's table at brooklyn fare\"]  # podes editar/expandir\n",
+    "\n",
+    "for idx, row in stars_df.iterrows():\n",
+    "    name_lower = str(row[\"name\"]).lower()\n",
+    "\n",
+    "    # verifica se algum dos nomes alvo aparece no name da linha\n",
+    "    if any(tn.lower() in name_lower for tn in target_names):\n",
+    "        rating, count = get_place_rating(row[\"name\"], row[\"city\"])\n",
+    "        stars_df.at[idx, \"Review_rating\"] = rating\n",
+    "        stars_df.at[idx, \"Review_count\"] = count\n",
+    "    # os outros restaurantes ficam como estão (NaN ou valores antigos)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33a9bff2-d6da-40b1-b099-8a4b2e0689f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77fb715e-886d-4644-b619-ba96e9baa884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)\n",
+    "stars_df['name'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d04b692-dd56-4962-bc0b-e9805b8924ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rest_names = [\"per se\", \"eleven madison park\",\"chef's table at brooklyn fare\"]\n",
+    "\n",
+    "mask = stars_df[\"name\"].str.strip().str.lower().isin(\n",
+    "    [n.strip().lower() for n in rest_names]\n",
+    ")\n",
+    "linha_restaurante = stars_df.loc[mask, :]\n",
+    "\n",
+    "print(linha_restaurante)\n",
+    "print(stars_df.loc[mask, \"Review_rating\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e5e741-e192-4855-9334-4e1ba9e51180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "8e1c6981-0553-4b40-b680-c63804b95c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'price_ordinal', 'price_mean', 'cuisine_original', 'major_city',\n",
+       "       'Review_rating', 'Review_count'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a970e70-c0af-4c09-a7d0-b405e1511c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", None)  # mostra todas as colunas\n",
+    "stars_df.head()                            # ou stars_df.sample(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f3b61ea-927d-4a74-867a-9b237c78820c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/final_Chiara.ipynb b/notebooks/final_Chiara.ipynb
new file mode 100644
index 00000000..fce597c4
--- /dev/null
+++ b/notebooks/final_Chiara.ipynb
@@ -0,0 +1,2215 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            name  year   latitude  longitude            city   region zipCode  \\\n",
+      "0   Kilian Stuba  2019  47.348580   10.17114  Kleinwalsertal  Austria   87568   \n",
+      "1  Pfefferschiff  2019  47.837870   13.07917        Hallwang  Austria    5300   \n",
+      "2      Esszimmer  2019  47.806850   13.03409        Salzburg  Austria    5020   \n",
+      "3     Carpe Diem  2019  47.800010   13.04006        Salzburg  Austria    5020   \n",
+      "4         Edvard  2019  48.216503   16.36852            Wien  Austria    1010   \n",
+      "\n",
+      "           cuisine  price                                                url  \n",
+      "0         Creative  $$$$$  https://guide.michelin.com/at/en/vorarlberg/kl...  \n",
+      "1  Classic cuisine  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "2         Creative  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "3   Market cuisine  $$$$$  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "4   Modern cuisine   $$$$  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "                               name  year  latitude  longitude      city  \\\n",
+      "0                  SENNS.Restaurant  2019  47.83636   13.06389  Salzburg   \n",
+      "1                            Ikarus  2019  47.79536   13.00695  Salzburg   \n",
+      "2                       Mraz & Sohn  2019  48.23129   16.37637      Wien   \n",
+      "3               Konstantin Filippou  2019  48.21056   16.37996      Wien   \n",
+      "4  Silvio Nickol Gourmet Restaurant  2019  48.20558   16.37693      Wien   \n",
+      "\n",
+      "    region zipCode         cuisine  price  \\\n",
+      "0  Austria    5020        Creative  $$$$$   \n",
+      "1  Austria    5020        Creative  $$$$$   \n",
+      "2  Austria    1200        Creative  $$$$$   \n",
+      "3  Austria    1010  Modern cuisine  $$$$$   \n",
+      "4  Austria    1010  Modern cuisine  $$$$$   \n",
+      "\n",
+      "                                                 url  \n",
+      "0  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "1  https://guide.michelin.com/at/en/salzburg-regi...  \n",
+      "2  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "3  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "4  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "            name  year  latitude  longitude                 city      region  \\\n",
+      "0         Amador  2019  48.25406   16.35915                 Wien     Austria   \n",
+      "1        Manresa  2019  37.22761 -121.98071  South San Francisco  California   \n",
+      "2           Benu  2019  37.78521 -122.39876        San Francisco  California   \n",
+      "3         Quince  2019  37.79762 -122.40337        San Francisco  California   \n",
+      "4  Atelier Crenn  2019  37.79835 -122.43586        San Francisco  California   \n",
+      "\n",
+      "  zipCode       cuisine  price  \\\n",
+      "0    1190      Creative  $$$$$   \n",
+      "1   95030  Contemporary   $$$$   \n",
+      "2   94105         Asian   $$$$   \n",
+      "3   94133  Contemporary   $$$$   \n",
+      "4   94123  Contemporary   $$$$   \n",
+      "\n",
+      "                                                 url  \n",
+      "0  https://guide.michelin.com/at/en/vienna/wien/r...  \n",
+      "1  https://guide.michelin.com/us/en/california/so...  \n",
+      "2  https://guide.michelin.com/us/en/california/sa...  \n",
+      "3  https://guide.michelin.com/us/en/california/sa...  \n",
+      "4  https://guide.michelin.com/us/en/california/sa...  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/raw/archive/one-star-michelin-restaurants.csv')\n",
+    "star2_df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/raw/archive/two-stars-michelin-restaurants.csv')\n",
+    "star3_df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/raw/archive/three-stars-michelin-restaurants.csv')\n",
+    "\n",
+    "\n",
+    "print(star1_df.head())\n",
+    "print(star2_df.head())\n",
+    "print(star3_df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Drop unwanted columns\n",
+    "star1_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6a101a8-74e5-4007-98cb-2cdd0d310fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e8cec71f-c813-40d9-92a7-2ea32ada988b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f6ea2440-dbc3-4c55-98b7-051971568e95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a star column\n",
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a0424c0-8c00-4736-bb30-bcf33f8ab101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Concatenating the 3 datasets \n",
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ec4465cb-7e32-4c70-a6b5-6f6179a31f21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace $$$$$ with $$$$\n",
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ff99db1d-7bb4-40ee-b8fb-70a4f192e228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean weird characters\n",
+    "stars_df['price'] = stars_df['price'].str.strip()\n",
+    "stars_df['price'] = stars_df['price'].str.replace(r'\\s+', '', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3fc94f5b-6f23-4f4d-86df-cc6a53b36e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert $ to ordinal numbers\n",
+    "stars_df['price_ordinal'] = stars_df['price'].str.count(r'\\$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5354f300-4299-46bd-a61e-ab56f935e334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute median ordinal per star group (1 star, 2 stars, 3 stars)\n",
+    "median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f2bcea8c-3e8c-458b-9898-caf5e14bec74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace missing ordinal values using matching star median\n",
+    "stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d8afb6f3-10d9-4775-82a8-f49852a2766c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now convert back to $ string after filling\n",
+    "stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d5a1e8ae-c0fe-4281-9755-32a87caa105d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping\n",
+    "price_mean_map = {\n",
+    "    \"$\": 20,\n",
+    "    \"$$\": 37.5,\n",
+    "    \"$$$\": 62.5,\n",
+    "    \"$$$$\": 100\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d22c338b-3d05-4de2-b79f-34d13b6b3746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new column with the mean price\n",
+    "stars_df['price_mean'] = stars_df['price'].map(price_mean_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8876ecd1-48b9-4678-aa1b-32935c85d3ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name             0\n",
+       "year             0\n",
+       "latitude         0\n",
+       "longitude        0\n",
+       "city             2\n",
+       "region           0\n",
+       "cuisine          0\n",
+       "price            0\n",
+       "url              0\n",
+       "stars            0\n",
+       "price_ordinal    0\n",
+       "price_mean       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking if there are no null values in all price columns \n",
+    "stars_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>price_ordinal</th>\n",
+       "      <th>price_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kilian Stuba</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Kleinwalsertal</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vorarlberg/kl...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pfefferschiff</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Hallwang</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Classic cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esszimmer</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Carpe Diem</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Market cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Edvard</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vienna/wien/r...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year            city   region          cuisine price  \\\n",
+       "0   Kilian Stuba  2019  Kleinwalsertal  Austria         Creative  $$$$   \n",
+       "1  Pfefferschiff  2019        Hallwang  Austria  Classic cuisine  $$$$   \n",
+       "2      Esszimmer  2019        Salzburg  Austria         Creative  $$$$   \n",
+       "3     Carpe Diem  2019        Salzburg  Austria   Market cuisine  $$$$   \n",
+       "4         Edvard  2019            Wien  Austria   Modern cuisine  $$$$   \n",
+       "\n",
+       "                                                 url   stars  price_ordinal  \\\n",
+       "0  https://guide.michelin.com/at/en/vorarlberg/kl...  1 star              4   \n",
+       "1  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "2  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "3  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "4  https://guide.michelin.com/at/en/vienna/wien/r...  1 star              4   \n",
+       "\n",
+       "   price_mean  \n",
+       "0       100.0  \n",
+       "1       100.0  \n",
+       "2       100.0  \n",
+       "3       100.0  \n",
+       "4       100.0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop columns\n",
+    "stars_df.drop(columns=['latitude', 'longitude'], inplace=True)\n",
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#put in lower for joining for example 'creative' with 'Creative'\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].str.strip().str.lower() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2f2deecb-3847-4c17-8b99-bb9a5b120344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "international_types = [\"modern cuisine\", \"classic cuisine\",\n",
+    "                       \"street food\", \"meats and grills\", \"international\", \"innovative\"]\n",
+    "\n",
+    "stars_df[\"cuisine_original\"] = stars_df[\"cuisine\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(\n",
+    "    international_types, \"international cuisine\")\n",
+    "\n",
+    "international_sub = stars_df[\n",
+    "    stars_df[\"cuisine_original\"].isin(international_types)]\n",
+    "\n",
+    "ax = international_sub[\"cuisine_original\"].value_counts().plot(kind=\"bar\")\n",
+    "\n",
+    "plt.title(\"Distribution of subtypes within international cuisine\")\n",
+    "plt.ylabel(\"Number of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "note = (\"Modern cuisine combines global flavours with local and seasonal ingredients, \"\"while innovative refers to more experimental concepts such as molecular \"\"gastronomy or 3D‑printed food.\")\n",
+    "\n",
+    "plt.subplots_adjust(bottom=0.3)\n",
+    "\n",
+    "plt.figtext(\n",
+    "    0.5,\n",
+    "    0.02,\n",
+    "    note,\n",
+    "    ha=\"center\",\n",
+    "    va=\"bottom\",\n",
+    "    wrap=True,\n",
+    "    fontsize=9\n",
+    ")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "53a35045-f5f6-4110-b174-5f88bd9093ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "70648bad-2bfc-4a30-8721-5d31abc18443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute, in this case \"Chinese food\" \n",
+    "chinese_type = [\"chinese\", \n",
+    "    \"cantonese\",\n",
+    "    \"cantonese roast meats\",\n",
+    "    \"dim sum\",\n",
+    "    \"shanghainese\",\n",
+    "    \"sichuan\",\n",
+    "    \"hunanese and sichuan\",\n",
+    "    \"sichuan-huai yang\",\n",
+    "    \"fujian\",\n",
+    "    \"taizhou\",\n",
+    "    \"hang zhou\",\n",
+    "    \"noodles and congee\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(chinese_type, \"chinese\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "18eff245-8e00-40d4-a6cb-4215aea72c6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "01836bf8-034e-4cb5-aa25-679c373a6155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute\n",
+    "korean_types = ['korean',\n",
+    "    'korean contemporary',\n",
+    "    'temple cuisine']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "33116181-30c8-4b33-a28a-5f770935da02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e33b6a97-f1f2-4cfa-bc73-3065516a4100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thai_types = ['thai',\n",
+    "    'thai contemporary',\n",
+    "    'southern thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9a198e63-0358-4d15-91e3-3ab25070f5c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1f39652c-5412-47c0-b0bc-4f1e8ca36a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "american_types = ['american',\n",
+    "    'californian',\n",
+    "    'barbecue',\n",
+    "    'steakhouse']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6d1d5eca-2061-44dd-8a89-513fa6d5116d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "46"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "07f0bf7e-e580-4c53-85b5-112004625e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "french_types = ['french',\n",
+    "    'classic french',\n",
+    "    'french contemporary',\n",
+    "    'modern french',\n",
+    "    'creative french']\n",
+    "\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d995f47c-7f09-4a1d-b8b0-af2639174996",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "90196101-a614-4d9e-903d-0ddd2136bc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "japanese_types = ['japanese',\n",
+    "    'sushi',\n",
+    "    'teppanyaki',\n",
+    "    'japanese contemporary']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "b5d63dac-f061-4e69-af34-1913465539ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f7c979b4-e012-4a6e-a95f-cd9a32b31f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_asian_types = ['asian',\n",
+    "    'asian influences',\n",
+    "    'asian contemporary',\n",
+    "    'fusion','taiwanese','peranakan','thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "65715bf6-d434-4c3a-8e7b-e3666faf90a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "197594f3-4e30-4614-826b-050ca5de00d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "british_types = ['modern british',\n",
+    "    'traditional british',\n",
+    "    'creative british']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "112b202f-5d66-43f9-a999-62856ed29d1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c0d2074a-bf80-4057-90b5-e035eb5b4a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_types = ['modern cuisine',\n",
+    "    'modern','modern food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8270bdaf-1ae0-469f-be7d-9197ce9b5e28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "75ce0e4a-0c0f-40d9-bb71-384562cfbdef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'market cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'mediterranean',\n",
+       "       'seafood', 'european contemporary', 'scandinavian', 'austrian',\n",
+       "       'spanish', 'british', 'modern cuisine', 'australian',\n",
+       "       'italian contemporary', 'european', 'regional cuisine',\n",
+       "       'mediterranean cuisine'], dtype=object)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "9ca6b75d-eedc-434e-891d-5d171615f124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market_types = ['classic cuisine','market cuisine', 'regional cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "461da05b-22f3-4242-ade1-69072bf55cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c1b5284a-59cf-4aaa-8c44-1b21594cec3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mediterranean_types = ['mediterranean', 'mediterranean cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9fbc1139-5470-4594-8382-53236b9e62c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'classic cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'mediterranean food',\n",
+       "       'seafood', 'european contemporary', 'scandinavian', 'austrian',\n",
+       "       'spanish', 'british', 'modern cuisine', 'australian',\n",
+       "       'italian contemporary', 'european'], dtype=object)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "42b900bb-af39-442f-94d2-abcab9710dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_european_types = ['european', 'european contemporary','mediterranean food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ec17fcff-6605-477c-a4e6-90b92835266e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "88adeee8-713c-4ce2-8100-7754993de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "italian_types = ['italian', 'italian contemporary']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "aa1fc4be-87bd-4a6f-8cae-c95cc96b2598",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "56b5bfdd-81f6-4b3f-865c-eff95f23a7a7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0b09fd01-fbfc-48f6-98d2-57c2f5bf1b64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'classic cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'other european',\n",
+       "       'seafood', 'scandinavian', 'austrian', 'spanish', 'british',\n",
+       "       'modern cuisine', 'australian'], dtype=object)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e540377f-e185-48a3-b3a4-24b480f13ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "868eea3a-d9d9-49ff-a24d-2ae6e36a8e7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scandinavian_types = ['danish','finnish', 'scandinavian']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "62a2e925-3ccb-4339-89f4-fdeb960ea0c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'japanese', 'vegetarian',\n",
+       "       'contemporary', 'indian', 'korean', 'american', 'moroccan',\n",
+       "       'other asian', 'chinese', 'italian', 'french', 'mexican',\n",
+       "       'gastropub', 'danish', 'finnish', 'other european', 'seafood',\n",
+       "       'scandinavian', 'austrian', 'spanish', 'british', 'australian'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "34edec4d-5578-4197-bba9-65cb44ebf311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "84f3890b-2733-47b9-a271-08f9dc272006",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['$', '$$', '$$$', '$$$$'], dtype=object)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b9ff2db0-a438-4594-8851-21ad6ec96cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "684"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['name'].nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "cb5c0a0b-205e-47c2-99e5-160d2e9b94a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'price_ordinal', 'price_mean', 'cuisine_original'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "4b36c09a-8535-49df-b11d-82c5fae0875a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306                            jin jin\n",
+      "374                        da san yuan\n",
+      "169                            yat lok\n",
+      "308    hill street tai hwa pork noodle\n",
+      "309            putien (kitchener road)\n",
+      "                    ...               \n",
+      "229                    gramercy tavern\n",
+      "228                               noda\n",
+      "225                              nomad\n",
+      "242                              agern\n",
+      "694                      gordon ramsay\n",
+      "Name: name, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Restaurant name standardization - lower case\n",
+    "\n",
+    "stars_df['name']= stars_df['name'].str.lower()\n",
+    "print (stars_df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "30eb95b6-64bf-4412-be57-e3db0536f906",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                           name  year              city         region  \\\n",
+      "31                   rich table  2019     San Francisco     California   \n",
+      "603         sushi ginza onodera  2019          New York  New York City   \n",
+      "391                     jay fai  2019           Bangkok       Thailand   \n",
+      "408                       fiola  2019  Washington, D.C.  Washington DC   \n",
+      "360  l'atelier de joël robuchon  2019            Taipei         Taipei   \n",
+      "\n",
+      "                   cuisine price  \\\n",
+      "31            contemporary   $$$   \n",
+      "603               japanese  $$$$   \n",
+      "391  international cuisine    $$   \n",
+      "408                italian  $$$$   \n",
+      "360                 french   $$$   \n",
+      "\n",
+      "                                                   url    stars  \\\n",
+      "31   https://guide.michelin.com/us/en/california/sa...   1 star   \n",
+      "603  https://guide.michelin.com/us/en/new-york-stat...  2 stars   \n",
+      "391  https://guide.michelin.com/th/en/bangkok-regio...   1 star   \n",
+      "408  https://guide.michelin.com/us/en/washington/wa...   1 star   \n",
+      "360  https://guide.michelin.com/tw/en/taipei-region...   1 star   \n",
+      "\n",
+      "     price_ordinal  price_mean     cuisine_original  \n",
+      "31               3        62.5         contemporary  \n",
+      "603              4       100.0             japanese  \n",
+      "391              2        37.5          street food  \n",
+      "408              4       100.0              italian  \n",
+      "360              3        62.5  french contemporary  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Trim Excessive Whitespaces:\n",
+    "\n",
+    "stars_df['name'] = stars_df['name'].astype(str)\n",
+    "stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))\n",
+    "\n",
+    "print(stars_df.sample(5)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b6eb2f6a-93b2-47d0-8262-10e3dde375d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# year check\n",
+    "\n",
+    "stars_df['year'].nunique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "6bf643cd-63d0-4762-a772-e99dffa5256a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306    2019\n",
+      "374    2019\n",
+      "169    2019\n",
+      "308    2018\n",
+      "309    2018\n",
+      "       ... \n",
+      "229    2019\n",
+      "228    2019\n",
+      "225    2019\n",
+      "242    2019\n",
+      "694    2019\n",
+      "Name: year, Length: 695, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stars_df['year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "caa32e9d-be69-4ede-80a5-47c40dbbe909",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "179"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# city names check\n",
+    "stars_df['city'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8e50bafb-9f38-4b8a-9575-a65bffc186d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Seoul', 'Taipei', 'Hong Kong', 'Singapore', 'Bangkok', 'Macau',\n",
+       "       'Chicago', 'New York', 'København', 'Los Angeles',\n",
+       "       'Washington, D.C.', 'San Francisco', 'Malmö', 'Stockholm',\n",
+       "       'Blackrock', 'Leith', 'Aird Mhór/Ardmore', 'Belfast',\n",
+       "       'Pateley Bridge', 'Baile Mhic Andáin/Thomastown', 'Baltimore',\n",
+       "       'Edinburgh', 'Peat Inn', 'Westminster', 'Anstruther', 'Grasmere',\n",
+       "       'Bowness-on-Windermere', 'Henne', 'Cill Chainnigh/Kilkenny',\n",
+       "       'Cartmel', 'Gaillimh/Galway', 'Menai Bridge/Porthaethwy',\n",
+       "       'Lios Dúin Bhearna/Lisdoonvarna', 'Ballydehob', 'Corcaigh/Cork',\n",
+       "       'City Centre', 'Langho', 'Newcastle upon Tyne', 'Dalry',\n",
+       "       'City of London', 'Bermondsey', 'Clapham Common', 'Wandsworth',\n",
+       "       'Växjö', 'Dorking', 'Horsham', 'Gravetye', 'Clerkenwell',\n",
+       "       'Victoria', 'London', 'Shoreditch', 'Finsbury', 'Spitalfields',\n",
+       "       'Seasalter', 'Göteborg', 'Chelsea', \"Saint James's\",\n",
+       "       'São Paulo - 04538', 'Saint Helier/Saint-Hélier', 'Biddenden',\n",
+       "       'Fordwich', 'Waternish', 'Birkenhead', 'Bray', 'Winchester',\n",
+       "       'Bagshot', 'Ascot', 'Egham', 'Kew', 'Chiswick', 'Little Dunmow',\n",
+       "       'Hammersmith', 'Kensington', 'Marylebone', 'Shinfield',\n",
+       "       \"Burchett's Green\", 'Colerne', 'Bath', 'Lympstone', 'Hunstanton',\n",
+       "       'Oxford', 'Murcott', 'Morston', 'Costa Mesa', 'East Chisenbury',\n",
+       "       'Newbury', 'Marlow', 'Torquay', \"Regent's Park\", 'Fulham', 'Soho',\n",
+       "       'Mayfair', 'South San Francisco', 'Belgravia', 'Ripley',\n",
+       "       'Bloomsbury', 'Castle Combe', 'Malmesbury', 'Birmingham',\n",
+       "       'Budapest', 'Port Isaac', 'Hampton in Arden', 'Whitebrook',\n",
+       "       'Penarth', 'Portscatho', 'Mountsorrel', nan, 'Padstow',\n",
+       "       'Llandrillo', 'Machynlleth', 'Oldstead', 'Chester',\n",
+       "       'Llanddewi Skirrid', 'Harome', 'Leeds', 'Montgomery/Trefaldwyn',\n",
+       "       'South Dalton', 'Baslow', 'Oslo', 'Winteringham', 'Fence',\n",
+       "       'Kenilworth', 'Chagford', 'Fredericia', 'Vejle', 'Upper Hambleton',\n",
+       "       'Aarhus', 'Bristol', 'Chew Magna', 'Cheltenham', 'Præstø',\n",
+       "       'Knowstone', 'Stratford-upon-Avon', 'Helsingfors / Helsinki',\n",
+       "       'Ilfracombe', 'Kleinwalsertal', 'Salzburg', 'Hyde Park',\n",
+       "       'North Kensington', 'Cambridge', 'Wien', 'Great Milton',\n",
+       "       'Nottingham', 'Aughton', 'Summerhouse', 'Auchterarder',\n",
+       "       'Skåne-Tranås', 'Athína', 'Leynar', 'Järpen', 'São Paulo - 01411',\n",
+       "       'São Paulo - 05416', 'Rio de Janeiro - 22441', 'Rovinj',\n",
+       "       'San Diego', 'Lovran', 'Zagreb', 'Šibenik', 'Dubrovnik',\n",
+       "       'Pedersker', 'Hørve', 'Praha', 'Hallwang', 'Sacramento',\n",
+       "       'Monterey', 'São Paulo - 04080', 'São Paulo - 01401',\n",
+       "       'São Paulo - 04509', 'São Paulo - 05706', 'São Paulo - 04531',\n",
+       "       'São Paulo - 01426', 'São Paulo - 05415', 'São Paulo - 05413',\n",
+       "       'Rio de Janeiro - 22021', 'Rio de Janeiro - 22271', 'Phuket',\n",
+       "       'Rio de Janeiro - 22470', 'Warszawa', 'Stavanger', 'Trondheim'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3fad5dfe-be46-45e6-9609-414aa470e0db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306        Seoul\n",
+      "374       Taipei\n",
+      "169    Hong Kong\n",
+      "308    Singapore\n",
+      "309    Singapore\n",
+      "         ...    \n",
+      "229     New York\n",
+      "228     New York\n",
+      "225     New York\n",
+      "242     New York\n",
+      "694      Chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Remove Leading/Trailing Spaces\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.strip()\n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e8e7151e-b4db-4076-835e-cd82139b24e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306        seoul\n",
+      "374       taipei\n",
+      "169    hong kong\n",
+      "308    singapore\n",
+      "309    singapore\n",
+      "         ...    \n",
+      "229     new york\n",
+      "228     new york\n",
+      "225     new york\n",
+      "242     new york\n",
+      "694      chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#convert to lower case\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.lower() \n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "654fc3c3-545b-4115-8f8b-a2514b44e138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                name  year       city          region  \\\n",
+      "306                          jin jin  2019      seoul     South Korea   \n",
+      "374                      da san yuan  2019     taipei          Taipei   \n",
+      "169                          yat lok  2019  hong kong       Hong Kong   \n",
+      "308  hill street tai hwa pork noodle  2018  singapore       Singapore   \n",
+      "309          putien (kitchener road)  2018  singapore       Singapore   \n",
+      "..                               ...   ...        ...             ...   \n",
+      "229                  gramercy tavern  2019   new york   New York City   \n",
+      "228                             noda  2019   new york   New York City   \n",
+      "225                            nomad  2019   new york   New York City   \n",
+      "242                            agern  2019   new york   New York City   \n",
+      "694                    gordon ramsay  2019    chelsea  United Kingdom   \n",
+      "\n",
+      "                   cuisine price  \\\n",
+      "306                chinese     $   \n",
+      "374                chinese     $   \n",
+      "169                chinese     $   \n",
+      "308  international cuisine     $   \n",
+      "309                chinese     $   \n",
+      "..                     ...   ...   \n",
+      "229           contemporary  $$$$   \n",
+      "228               japanese  $$$$   \n",
+      "225           contemporary  $$$$   \n",
+      "242           scandinavian  $$$$   \n",
+      "694                 french  $$$$   \n",
+      "\n",
+      "                                                   url    stars  \\\n",
+      "306  https://guide.michelin.com/kr/en/seoul-capital...   1 star   \n",
+      "374  https://guide.michelin.com/tw/en/taipei-region...   1 star   \n",
+      "169  https://guide.michelin.com/hk/en/hong-kong-reg...   1 star   \n",
+      "308  https://guide.michelin.com/sg/en/singapore-reg...   1 star   \n",
+      "309  https://guide.michelin.com/sg/en/singapore-reg...   1 star   \n",
+      "..                                                 ...      ...   \n",
+      "229  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "228  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "225  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "242  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "694  https://guide.michelin.com/gb/en/greater-londo...  3 stars   \n",
+      "\n",
+      "     price_ordinal  price_mean       cuisine_original  \n",
+      "306              1        20.0                chinese  \n",
+      "374              1        20.0              cantonese  \n",
+      "169              1        20.0  cantonese roast meats  \n",
+      "308              1        20.0            street food  \n",
+      "309              1        20.0                 fujian  \n",
+      "..             ...         ...                    ...  \n",
+      "229              4       100.0           contemporary  \n",
+      "228              4       100.0               japanese  \n",
+      "225              4       100.0           contemporary  \n",
+      "242              4       100.0           scandinavian  \n",
+      "694              4       100.0                 french  \n",
+      "\n",
+      "[571 rows x 11 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#check for duplicates\n",
+    "\n",
+    "duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ee3f59d0-5adb-468b-86db-bd6ea87e1463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# order A–Z\n",
+    "\n",
+    "#stars_df = stars_df.sort_values(by=\"city\")  \n",
+    "#stars_df['city'].unique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "9a70b42b-accf-46fc-a32a-2377e25b0024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove numbers and zip codes\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def clean_city_name(city_name):\n",
+    "    if isinstance(city_name, str):  # Check if the input is a string\n",
+    "        # Use regex to remove \" - numbers\" at the end of the string\n",
+    "        return re.sub(r'\\s-\\s\\d+$', '', city_name).strip()\n",
+    "    return city_name  # Return as is if it's not a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "bb772e24-05ae-41ac-9d33-521afbb9068c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].apply(clean_city_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "565e3036-376f-42ae-ab1a-262ae8a046de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['seoul' 'taipei' 'hong kong' 'singapore' 'bangkok' 'macau' 'chicago'\n",
+      " 'new york' 'københavn' 'los angeles' 'washington, d.c.' 'san francisco'\n",
+      " 'malmö' 'stockholm' 'blackrock' 'leith' 'aird mhór/ardmore' 'belfast'\n",
+      " 'pateley bridge' 'baile mhic andáin/thomastown' 'baltimore' 'edinburgh'\n",
+      " 'peat inn' 'westminster' 'anstruther' 'grasmere' 'bowness-on-windermere'\n",
+      " 'henne' 'cill chainnigh/kilkenny' 'cartmel' 'gaillimh/galway'\n",
+      " 'menai bridge/porthaethwy' 'lios dúin bhearna/lisdoonvarna' 'ballydehob'\n",
+      " 'corcaigh/cork' 'city centre' 'langho' 'newcastle upon tyne' 'dalry'\n",
+      " 'city of london' 'bermondsey' 'clapham common' 'wandsworth' 'växjö'\n",
+      " 'dorking' 'horsham' 'gravetye' 'clerkenwell' 'victoria' 'london'\n",
+      " 'shoreditch' 'finsbury' 'spitalfields' 'seasalter' 'göteborg' 'chelsea'\n",
+      " \"saint james's\" 'são paulo' 'saint helier/saint-hélier' 'biddenden'\n",
+      " 'fordwich' 'waternish' 'birkenhead' 'bray' 'winchester' 'bagshot' 'ascot'\n",
+      " 'egham' 'kew' 'chiswick' 'little dunmow' 'hammersmith' 'kensington'\n",
+      " 'marylebone' 'shinfield' \"burchett's green\" 'colerne' 'bath' 'lympstone'\n",
+      " 'hunstanton' 'oxford' 'murcott' 'morston' 'costa mesa' 'east chisenbury'\n",
+      " 'newbury' 'marlow' 'torquay' \"regent's park\" 'fulham' 'soho' 'mayfair'\n",
+      " 'south san francisco' 'belgravia' 'ripley' 'bloomsbury' 'castle combe'\n",
+      " 'malmesbury' 'birmingham' 'budapest' 'port isaac' 'hampton in arden'\n",
+      " 'whitebrook' 'penarth' 'portscatho' 'mountsorrel' nan 'padstow'\n",
+      " 'llandrillo' 'machynlleth' 'oldstead' 'chester' 'llanddewi skirrid'\n",
+      " 'harome' 'leeds' 'montgomery/trefaldwyn' 'south dalton' 'baslow' 'oslo'\n",
+      " 'winteringham' 'fence' 'kenilworth' 'chagford' 'fredericia' 'vejle'\n",
+      " 'upper hambleton' 'aarhus' 'bristol' 'chew magna' 'cheltenham' 'præstø'\n",
+      " 'knowstone' 'stratford-upon-avon' 'helsingfors / helsinki' 'ilfracombe'\n",
+      " 'kleinwalsertal' 'salzburg' 'hyde park' 'north kensington' 'cambridge'\n",
+      " 'wien' 'great milton' 'nottingham' 'aughton' 'summerhouse' 'auchterarder'\n",
+      " 'skåne-tranås' 'athína' 'leynar' 'järpen' 'rio de janeiro' 'rovinj'\n",
+      " 'san diego' 'lovran' 'zagreb' 'šibenik' 'dubrovnik' 'pedersker' 'hørve'\n",
+      " 'praha' 'hallwang' 'sacramento' 'monterey' 'phuket' 'warszawa'\n",
+      " 'stavanger' 'trondheim']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verify results\n",
+    "\n",
+    "print(stars_df['city'].unique())  # Display unique city names to verify the cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "7ca0d239-d0d6-4bf6-a80b-52abfe117f87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove special characters \n",
+    "stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "ebe148d4-da9d-49bf-95cb-7dc791e9dbe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bb93350d-af11-4757-b2ea-fe2ac99ca466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "efa0705f-be86-4f5d-b89a-9831265b29e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'unidecode'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[72]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m#the city column are stripped of accents and are presented in ASCII format.\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01munidecode\u001b[39;00m\n\u001b[32m      4\u001b[39m stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m] = stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m].astype(\u001b[38;5;28mstr\u001b[39m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: unidecode.unidecode(x))\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'unidecode'"
+     ]
+    }
+   ],
+   "source": [
+    "#the city column are stripped of accents and are presented in ASCII format.\n",
+    "\n",
+    "import unidecode\n",
+    "stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94bb2635-47d6-47df-aa22-d0d1d1d86463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#verify the results\n",
+    "print(stars_df['city'].unique()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33b41649-7526-40c2-9a6a-9cd76700334a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grouping suburbs into major city and add info in a new column \n",
+    "\n",
+    "#create a dictionary \n",
+    "\n",
+    "location_map = {\n",
+    "    # London + neighborhoods\n",
+    "    'north kensington': 'London',\n",
+    "    'kensington': 'London',\n",
+    "    'westminster': 'London',\n",
+    "    'soho': 'London',\n",
+    "    'mayfair': 'London',\n",
+    "    'marylebone': 'London',\n",
+    "    'chelsea': 'London',\n",
+    "    'clapham common': 'London',\n",
+    "    \"regent's park\": 'London',\n",
+    "    'shoreditch': 'London',\n",
+    "    'spitalfields': 'London',\n",
+    "    'belgravia': 'London',\n",
+    "    'bloomsbury': 'London',\n",
+    "    'finsbury': 'London',\n",
+    "    'fulham': 'London',\n",
+    "    'chiswick': 'London',\n",
+    "    'city centre': 'London',\n",
+    "    'city of london': 'London',\n",
+    "    'hyde park': 'London',\n",
+    "    # San Francisco\n",
+    "    'south san francisco': 'San Francisco',\n",
+    "    # Ireland\n",
+    "    'baile mhic andáin/thomastown': 'Thomastown',\n",
+    "    'gaillimh/galway': 'Galway',\n",
+    "    'cill chainnigh/kilkenny': 'Kilkenny',\n",
+    "    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',\n",
+    "    'athína': 'Athens',\n",
+    "    'ballydehob': 'Ballydehob',\n",
+    "    # Finland\n",
+    "    'helsingfors / helsinki': 'Helsinki',\n",
+    "    # Czech Republic\n",
+    "    'praha': 'Prague',\n",
+    "    # Austria\n",
+    "    'wien': 'Vienna',\n",
+    "    'salzburg': 'Salzburg',\n",
+    "    # Menai Bridge\n",
+    "    'menai bridge/porthaethwy': 'Menai Bridge',\n",
+    "    # USA cities\n",
+    "    'los angeles': 'Los Angeles',\n",
+    "    'san diego': 'San Diego',\n",
+    "    'sacramento': 'Sacramento',\n",
+    "    'new york': 'New York',\n",
+    "    'chicago': 'Chicago',\n",
+    "    'costa mesa': 'Costa Mesa',\n",
+    "    'monterey': 'Monterey',\n",
+    "    'washington, d.c.': 'Washington D.C.',\n",
+    "    'south dalton': 'Dalton',\n",
+    "    # Asia\n",
+    "    'bangkok': 'Bangkok',\n",
+    "    'phuket': 'Phuket',\n",
+    "    'hong kong': 'Hong Kong',\n",
+    "    'taipei': 'Taipei',\n",
+    "    'seoul': 'Seoul',\n",
+    "    'singapore': 'Singapore',\n",
+    "    'macau': 'Macau',\n",
+    "    # Croatia\n",
+    "    'lovran': 'Lovran',\n",
+    "    'rovinj': 'Rovinj',\n",
+    "    'zagreb': 'Zagreb',\n",
+    "    'šibenik': 'Sibenik',\n",
+    "    # Norway / Scandinavia\n",
+    "    'stavanger': 'Stavanger',\n",
+    "    'trondheim': 'Trondheim',\n",
+    "    'oslo': 'Oslo',\n",
+    "    'göteborg': 'Gothenburg',\n",
+    "    'växjö': 'Vaxjo',\n",
+    "    'skåne-tranås': 'Skane-Tranas',\n",
+    "    'vejle': 'Vejle',\n",
+    "    # Denmark\n",
+    "    'fredericia': 'Fredericia',\n",
+    "    'pedersker': 'Pedersker',\n",
+    "    'præstø': 'Praesto',\n",
+    "    # Sweden\n",
+    "    'malmö': 'Malmo',\n",
+    "    'stockholm': 'Stockholm',\n",
+    "    # Portugal / Ireland / UK misc\n",
+    "    'bath': 'Bath',\n",
+    "    'bristol': 'Bristol',\n",
+    "    'cambridge': 'Cambridge',\n",
+    "    'cheltenham': 'Cheltenham',\n",
+    "    'chester': 'Chester',\n",
+    "    'birmingham': 'Birmingham',\n",
+    "    'edinburgh': 'Edinburgh',\n",
+    "    'leeds': 'Leeds',\n",
+    "    'oxford': 'Oxford',\n",
+    "    'stratford-upon-avon': 'Stratford-Upon-Avon',\n",
+    "    'padstow': 'Padstow',\n",
+    "    'torquay': 'Torquay',\n",
+    "    'newcastle upon tyne': 'Newcastle upon Tyne',\n",
+    "    'nottingham': 'Nottingham',\n",
+    "    'bray': 'Bray',\n",
+    "    'bowness-on-windermere': 'Bowness-on-Windermere',\n",
+    "    'cartmel': 'Cartmel',\n",
+    "    'castle combe': 'Castle Combe',\n",
+    "    'chagford': 'Chagford',\n",
+    "    'chew magna': 'Chew Magna',\n",
+    "    'dalry': 'Dalry',\n",
+    "    'dorking': 'Dorking',\n",
+    "    'egham': 'Egham',\n",
+    "    'fence': 'Fence',\n",
+    "    'fordwich': 'Fordwich',\n",
+    "    'grasmere': 'Grasmere',\n",
+    "    'gravetye': 'Gravetye',\n",
+    "    'great milton': 'Great Milton',\n",
+    "    'hallwang': 'Hallwang',\n",
+    "    'hampton in arden': 'Hampton in Arden',\n",
+    "    'harome': 'Harome',\n",
+    "    'henne': 'Henne',\n",
+    "    'horsham': 'Horsham',\n",
+    "    'hunstanton': 'Hunstanton',\n",
+    "    'ilfracombe': 'Ilfracombe',\n",
+    "    'järpen': 'Jarpen',\n",
+    "    'kenilworth': 'Kenilworth',\n",
+    "    'kew': 'Kew',\n",
+    "    'kleinwalsertal': 'Kleinwalsertal',\n",
+    "    'knowstone': 'Knowstone',\n",
+    "    'langho': 'Langho',\n",
+    "    'leith': 'Leith',\n",
+    "    'leynar': 'Leynar',\n",
+    "    'little dunmow': 'Little Dunmow',\n",
+    "    'llanddewi skirrid': 'Llanddewi Skirrid',\n",
+    "    'llandrillo': 'Llandrillo',\n",
+    "    'lovran': 'Lovran',\n",
+    "    'lympstone': 'Lympstone',\n",
+    "    'machynlleth': 'Machynlleth',\n",
+    "    'malmesbury': 'Malmesbury',\n",
+    "    'marlow': 'Marlow',\n",
+    "    'morston': 'Morston',\n",
+    "    'mountsorrel': 'Mountsorrel',\n",
+    "    'murcott': 'Murcott',\n",
+    "    'newbury': 'Newbury',\n",
+    "    'oldstead': 'Oldstead',\n",
+    "    'peat inn': 'Peat Inn',\n",
+    "    'penarth': 'Penarth',\n",
+    "    'port isaac': 'Port Isaac',\n",
+    "    'portscatho': 'Portscatho',\n",
+    "    'ripley': 'Ripley',\n",
+    "    'saint helier/saint-hélier': 'Saint Helier',\n",
+    "    \"saint james's\": 'Saint James',\n",
+    "    'seasalter': 'Seasalter',\n",
+    "    'shinfield': 'Shinfield',\n",
+    "    'summerhouse': 'Summerhouse',\n",
+    "    'upper hambleton': 'Hambleton',\n",
+    "    'victoria': 'Victoria',\n",
+    "    'wandsworth': 'London',\n",
+    "    'whitebrook': 'Whitebrook',\n",
+    "    'winchester': 'Winchester',\n",
+    "    'winteringham': 'Winteringham'\n",
+    "}\n",
+    "\n",
+    "stars_df['major_city'] = stars_df['city'].replace(location_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d936e600-5eaf-4813-a493-029cd4447b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stars_df[['city', 'major_city']].sample(10))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "781e121d-126d-491e-871d-526563dc4d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['name'].unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7256d411-b0f0-4aec-8e92-4888ba57806c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b27be073-e390-4f35-874b-f82d3b6f75f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92c35efa-5040-43ab-870b-7e196664e5cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bf9e732-a173-44a0-9ad1-467b769dfd7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"cuisine\"].value_counts().head(10).plot(kind=\"bar\")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Cuisine\")\n",
+    "plt.ylabel(\"Nº of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c163a3c-a403-4bb4-a9f1-0c6a6a56abdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_cuisines = (\n",
+    "    stars_df[\"cuisine\"].value_counts()\n",
+    "    .head(10)\n",
+    "    .index\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.countplot(\n",
+    "    data=stars_df[stars_df[\"cuisine\"].isin(top_cuisines)],\n",
+    "    y=\"cuisine\",\n",
+    "    order=top_cuisines\n",
+    ")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Nº of restaurants\")\n",
+    "plt.ylabel(\"Cuisine\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb894fca-55db-4cf2-b4df-f73dda35806a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"stars_n\"] = stars_df[\"stars\"].str[0].astype(int)\n",
+    "\n",
+    "stars_df.groupby(\"region\")[\"stars_n\"].mean().sort_values().plot(kind=\"bar\")\n",
+    "plt.title(\"Mean of Stars per region\")\n",
+    "plt.xlabel(\"Region\")\n",
+    "plt.ylabel(\"Mean of stars\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3acfdf4-815b-4471-8fe9-a1eca7c437d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visuals: \n",
+    "#which are the cities with more restaurants? \n",
+    "#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?\n",
+    "# which are the cuisine dominating a city /region \n",
+    "# avg price point in a specific city based on restaurants?\n",
+    "# time series 2018 vs 2019 any trend? any star restautant grew over past year? \n",
+    "# cheapest vs most expensive cuisine? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ec45bca-a08c-475c-824e-f2422a6a70ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#web scraping / API from google reviews \n",
+    "\n",
+    "\n",
+    "import requests\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "API_KEY = \"AIzaSyC8VCFpZ1WhNUegfd5ziwZPVRCe1pi35lo\"\n",
+    "\n",
+    "def get_place_rating(name, city, api_key=API_KEY, sleep_sec=0.2):\n",
+    "    query = f\"{name}, {city}\"\n",
+    "    url_search = \"https://maps.googleapis.com/maps/api/place/findplacefromtext/json\"\n",
+    "    params_search = {\n",
+    "        \"input\": query,\n",
+    "        \"inputtype\": \"textquery\",\n",
+    "        \"fields\": \"place_id\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    r = requests.get(url_search, params=params_search)\n",
+    "    data = r.json()\n",
+    "    status = data.get(\"status\")\n",
+    "    print(\"SEARCH:\", query, \"->\", status)\n",
+    "    if status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    place_id = data[\"candidates\"][0][\"place_id\"]\n",
+    "\n",
+    "    url_details = \"https://maps.googleapis.com/maps/api/place/details/json\"\n",
+    "    params_details = {\n",
+    "        \"place_id\": place_id,\n",
+    "        \"fields\": \"rating,user_ratings_total\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    d = requests.get(url_details, params=params_details)\n",
+    "    det = d.json()\n",
+    "    d_status = det.get(\"status\")\n",
+    "    print(\"DETAILS:\", place_id, \"->\", d_status)\n",
+    "    if d_status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    time.sleep(sleep_sec)\n",
+    "    result = det.get(\"result\", {})\n",
+    "    return result.get(\"rating\"), result.get(\"user_ratings_total\")\n",
+    "\n",
+    "\n",
+    "# inicializar colunas (se ainda não existirem)\n",
+    "if \"Review_rating\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_rating\"] = np.nan\n",
+    "if \"Review_count\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_count\"] = np.nan\n",
+    "\n",
+    "# lista de restaurantes (ou partes do nome) que queres atualizar\n",
+    "target_names = [\"eleven madison park\", \"per se\", \"chef's table at brooklyn fare\"]  # podes editar/expandir\n",
+    "\n",
+    "for idx, row in stars_df.iterrows():\n",
+    "    name_lower = str(row[\"name\"]).lower()\n",
+    "\n",
+    "    # verifica se algum dos nomes alvo aparece no name da linha\n",
+    "    if any(tn.lower() in name_lower for tn in target_names):\n",
+    "        rating, count = get_place_rating(row[\"name\"], row[\"city\"])\n",
+    "        stars_df.at[idx, \"Review_rating\"] = rating\n",
+    "        stars_df.at[idx, \"Review_count\"] = count\n",
+    "    # os outros restaurantes ficam como estão (NaN ou valores antigos)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b721bf07-7b33-492e-90df-4b710d2d5bf0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alma_rows = stars_df[stars_df['name'].str.lower() == 'alma']\n",
+    "print(alma_rows)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33a9bff2-d6da-40b1-b099-8a4b2e0689f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77fb715e-886d-4644-b619-ba96e9baa884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)\n",
+    "stars_df['name'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d04b692-dd56-4962-bc0b-e9805b8924ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rest_names = [\"per se\", \"eleven madison park\",\"chef's table at brooklyn fare\"]\n",
+    "\n",
+    "mask = stars_df[\"name\"].str.strip().str.lower().isin(\n",
+    "    [n.strip().lower() for n in rest_names]\n",
+    ")\n",
+    "linha_restaurante = stars_df.loc[mask, :]\n",
+    "\n",
+    "print(linha_restaurante)\n",
+    "print(stars_df.loc[mask, \"Review_rating\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e5e741-e192-4855-9334-4e1ba9e51180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "8e1c6981-0553-4b40-b680-c63804b95c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'price_ordinal', 'price_mean', 'cuisine_original'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a970e70-c0af-4c09-a7d0-b405e1511c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", None)  # mostra todas as colunas\n",
+    "stars_df.head()                            # ou stars_df.sample(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f3b61ea-927d-4a74-867a-9b237c78820c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# SQL export \n",
+    "# OPEN SQL: click on create new schema, select a new name for the project. Now we have a new tables but we need to import the value with IMPORT WIZARD from jupyter, otherwise the columns of the tables are empty. \n",
+    "# after export the code with DRAWDB (relationshipy model with primary and foreign keys) and paste it into SQL and run the SQL codes. The ordered tables will show up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "09b67b66-d861-4bee-ad60-9ad1df7ab090",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import pandas as pd\n",
+    "#import numpy as np\n",
+    "#import pymysql\n",
+    "#from sqlalchemy import create_engine\n",
+    "#import getpass  # To get the password without showing the input\n",
+    "\n",
+    "#bd = \"bank\"\n",
+    "#connection_string = 'mysql+pymysql://root:' + password + '@localhost/'+bd\n",
+    "#engine = create_engine(connection_string)\n",
+    "#enginepassword = getpass.getpass()\n",
+    "\n",
+    "# Note that when you use _SQLAlchemy_ and establish the connection, you do not even need to be logged in Sequel Pro or MySQL Workbench."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92436260-c8c1-4463-be94-e3dd6d66a166",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/final_Zina.ipynb b/notebooks/final_Zina.ipynb
new file mode 100644
index 00000000..3c08d063
--- /dev/null
+++ b/notebooks/final_Zina.ipynb
@@ -0,0 +1,1362 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv(\"/Users/ZINA/Desktop/one-star-michelin-restaurants.csv\")\n",
+    "star2_df = pd.read_csv(\"/Users/ZINA/Desktop/two-stars-michelin-restaurants.csv\")\n",
+    "star3_df = pd.read_csv(\"/Users/ZINA/Desktop/three-stars-michelin-restaurants.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Drop unwanted columns\n",
+    "star1_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6a101a8-74e5-4007-98cb-2cdd0d310fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8cec71f-c813-40d9-92a7-2ea32ada988b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6ea2440-dbc3-4c55-98b7-051971568e95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a star column\n",
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a0424c0-8c00-4736-bb30-bcf33f8ab101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Concatenating the 3 datasets \n",
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec4465cb-7e32-4c70-a6b5-6f6179a31f21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace $$$$$ with $$$$\n",
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff99db1d-7bb4-40ee-b8fb-70a4f192e228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean weird characters\n",
+    "stars_df['price'] = stars_df['price'].str.strip()\n",
+    "stars_df['price'] = stars_df['price'].str.replace(r'\\s+', '', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc94f5b-6f23-4f4d-86df-cc6a53b36e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert $ to ordinal numbers\n",
+    "stars_df['price_ordinal'] = stars_df['price'].str.count(r'\\$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5354f300-4299-46bd-a61e-ab56f935e334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute median ordinal per star group (1 star, 2 stars, 3 stars)\n",
+    "median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2bcea8c-3e8c-458b-9898-caf5e14bec74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace missing ordinal values using matching star median\n",
+    "stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8afb6f3-10d9-4775-82a8-f49852a2766c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now convert back to $ string after filling\n",
+    "stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5a1e8ae-c0fe-4281-9755-32a87caa105d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping\n",
+    "price_mean_map = {\n",
+    "    \"$\": 20,\n",
+    "    \"$$\": 37.5,\n",
+    "    \"$$$\": 62.5,\n",
+    "    \"$$$$\": 100\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d22c338b-3d05-4de2-b79f-34d13b6b3746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new column with the mean price\n",
+    "stars_df['price_mean'] = stars_df['price'].map(price_mean_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8876ecd1-48b9-4678-aa1b-32935c85d3ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# checking if there are no null values in all price columns \n",
+    "stars_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop columns\n",
+    "stars_df.drop(columns=['latitude', 'longitude'], inplace=True)\n",
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#put in lower for joining for example 'creative' with 'Creative'\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].str.strip().str.lower() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f2deecb-3847-4c17-8b99-bb9a5b120344",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "international_types = [\"modern cuisine\", \"classic cuisine\",\n",
+    "                       \"street food\", \"meats and grills\", \"international\", \"innovative\"]\n",
+    "\n",
+    "stars_df[\"cuisine_original\"] = stars_df[\"cuisine\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(\n",
+    "    international_types, \"international cuisine\")\n",
+    "\n",
+    "international_sub = stars_df[\n",
+    "    stars_df[\"cuisine_original\"].isin(international_types)]\n",
+    "\n",
+    "ax = international_sub[\"cuisine_original\"].value_counts().plot(kind=\"bar\")\n",
+    "\n",
+    "plt.title(\"Distribution of subtypes within international cuisine\")\n",
+    "plt.ylabel(\"Number of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "note = (\"Modern cuisine combines global flavours with local and seasonal ingredients, \"\"while innovative refers to more experimental concepts such as molecular \"\"gastronomy or 3D‑printed food.\")\n",
+    "\n",
+    "plt.subplots_adjust(bottom=0.3)\n",
+    "\n",
+    "plt.figtext(\n",
+    "    0.5,\n",
+    "    0.02,\n",
+    "    note,\n",
+    "    ha=\"center\",\n",
+    "    va=\"bottom\",\n",
+    "    wrap=True,\n",
+    "    fontsize=9\n",
+    ")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53a35045-f5f6-4110-b174-5f88bd9093ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70648bad-2bfc-4a30-8721-5d31abc18443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute, in this case \"Chinese food\" \n",
+    "chinese_type = [\"chinese\", \n",
+    "    \"cantonese\",\n",
+    "    \"cantonese roast meats\",\n",
+    "    \"dim sum\",\n",
+    "    \"shanghainese\",\n",
+    "    \"sichuan\",\n",
+    "    \"hunanese and sichuan\",\n",
+    "    \"sichuan-huai yang\",\n",
+    "    \"fujian\",\n",
+    "    \"taizhou\",\n",
+    "    \"hang zhou\",\n",
+    "    \"noodles and congee\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(chinese_type, \"chinese\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18eff245-8e00-40d4-a6cb-4215aea72c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01836bf8-034e-4cb5-aa25-679c373a6155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute\n",
+    "korean_types = ['korean',\n",
+    "    'korean contemporary',\n",
+    "    'temple cuisine']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33116181-30c8-4b33-a28a-5f770935da02",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e33b6a97-f1f2-4cfa-bc73-3065516a4100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thai_types = ['thai',\n",
+    "    'thai contemporary',\n",
+    "    'southern thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a198e63-0358-4d15-91e3-3ab25070f5c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f39652c-5412-47c0-b0bc-4f1e8ca36a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "american_types = ['american',\n",
+    "    'californian',\n",
+    "    'barbecue',\n",
+    "    'steakhouse']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d1d5eca-2061-44dd-8a89-513fa6d5116d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f0bf7e-e580-4c53-85b5-112004625e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "french_types = ['french',\n",
+    "    'classic french',\n",
+    "    'french contemporary',\n",
+    "    'modern french',\n",
+    "    'creative french']\n",
+    "\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d995f47c-7f09-4a1d-b8b0-af2639174996",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90196101-a614-4d9e-903d-0ddd2136bc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "japanese_types = ['japanese',\n",
+    "    'sushi',\n",
+    "    'teppanyaki',\n",
+    "    'japanese contemporary']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5d63dac-f061-4e69-af34-1913465539ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7c979b4-e012-4a6e-a95f-cd9a32b31f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_asian_types = ['asian',\n",
+    "    'asian influences',\n",
+    "    'asian contemporary',\n",
+    "    'fusion','taiwanese','peranakan','thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65715bf6-d434-4c3a-8e7b-e3666faf90a6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "197594f3-4e30-4614-826b-050ca5de00d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "british_types = ['modern british',\n",
+    "    'traditional british',\n",
+    "    'creative british']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "112b202f-5d66-43f9-a999-62856ed29d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0d2074a-bf80-4057-90b5-e035eb5b4a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_types = ['modern cuisine',\n",
+    "    'modern','modern food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8270bdaf-1ae0-469f-be7d-9197ce9b5e28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75ce0e4a-0c0f-40d9-bb71-384562cfbdef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca6b75d-eedc-434e-891d-5d171615f124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market_types = ['classic cuisine','market cuisine', 'regional cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "461da05b-22f3-4242-ade1-69072bf55cf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1b5284a-59cf-4aaa-8c44-1b21594cec3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mediterranean_types = ['mediterranean', 'mediterranean cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fbc1139-5470-4594-8382-53236b9e62c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42b900bb-af39-442f-94d2-abcab9710dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_european_types = ['european', 'european contemporary','mediterranean food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec17fcff-6605-477c-a4e6-90b92835266e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88adeee8-713c-4ce2-8100-7754993de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "italian_types = ['italian', 'italian contemporary']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa1fc4be-87bd-4a6f-8cae-c95cc96b2598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56b5bfdd-81f6-4b3f-865c-eff95f23a7a7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b09fd01-fbfc-48f6-98d2-57c2f5bf1b64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e540377f-e185-48a3-b3a4-24b480f13ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "868eea3a-d9d9-49ff-a24d-2ae6e36a8e7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scandinavian_types = ['danish','finnish', 'scandinavian']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62a2e925-3ccb-4339-89f4-fdeb960ea0c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34edec4d-5578-4197-bba9-65cb44ebf311",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['cuisine'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84f3890b-2733-47b9-a271-08f9dc272006",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b9ff2db0-a438-4594-8851-21ad6ec96cfb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['name'].nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb5c0a0b-205e-47c2-99e5-160d2e9b94a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b36c09a-8535-49df-b11d-82c5fae0875a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Restaurant name standardization - lower case\n",
+    "\n",
+    "stars_df['name']= stars_df['name'].str.lower()\n",
+    "print (stars_df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30eb95b6-64bf-4412-be57-e3db0536f906",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Trim Excessive Whitespaces:\n",
+    "\n",
+    "stars_df['name'] = stars_df['name'].astype(str)\n",
+    "stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))\n",
+    "\n",
+    "print(stars_df.sample(5)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6eb2f6a-93b2-47d0-8262-10e3dde375d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# year check\n",
+    "\n",
+    "stars_df['year'].nunique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bf643cd-63d0-4762-a772-e99dffa5256a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stars_df['year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "caa32e9d-be69-4ede-80a5-47c40dbbe909",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# city names check\n",
+    "stars_df['city'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e50bafb-9f38-4b8a-9575-a65bffc186d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fad5dfe-be46-45e6-9609-414aa470e0db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Remove Leading/Trailing Spaces\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.strip()\n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8e7151e-b4db-4076-835e-cd82139b24e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#convert to lower case\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.lower() \n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "654fc3c3-545b-4115-8f8b-a2514b44e138",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#check for duplicates\n",
+    "\n",
+    "duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee3f59d0-5adb-468b-86db-bd6ea87e1463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# order A–Z\n",
+    "\n",
+    "#stars_df = stars_df.sort_values(by=\"city\")  \n",
+    "#stars_df['city'].unique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a70b42b-accf-46fc-a32a-2377e25b0024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove numbers and zip codes\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def clean_city_name(city_name):\n",
+    "    if isinstance(city_name, str):  # Check if the input is a string\n",
+    "        # Use regex to remove \" - numbers\" at the end of the string\n",
+    "        return re.sub(r'\\s-\\s\\d+$', '', city_name).strip()\n",
+    "    return city_name  # Return as is if it's not a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb772e24-05ae-41ac-9d33-521afbb9068c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].apply(clean_city_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "565e3036-376f-42ae-ab1a-262ae8a046de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#verify results\n",
+    "\n",
+    "print(stars_df['city'].unique())  # Display unique city names to verify the cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ca0d239-d0d6-4bf6-a80b-52abfe117f87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove special characters \n",
+    "stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ebe148d4-da9d-49bf-95cb-7dc791e9dbe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb93350d-af11-4757-b2ea-fe2ac99ca466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efa0705f-be86-4f5d-b89a-9831265b29e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#the city column are stripped of accents and are presented in ASCII format.\n",
+    "\n",
+    "import unidecode\n",
+    "stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94bb2635-47d6-47df-aa22-d0d1d1d86463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#verify the results\n",
+    "print(stars_df['city'].unique()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33b41649-7526-40c2-9a6a-9cd76700334a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grouping suburbs into major city and add info in a new column \n",
+    "\n",
+    "#create a dictionary \n",
+    "\n",
+    "location_map = {\n",
+    "    # London + neighborhoods\n",
+    "    'north kensington': 'London',\n",
+    "    'kensington': 'London',\n",
+    "    'westminster': 'London',\n",
+    "    'soho': 'London',\n",
+    "    'mayfair': 'London',\n",
+    "    'marylebone': 'London',\n",
+    "    'chelsea': 'London',\n",
+    "    'clapham common': 'London',\n",
+    "    \"regent's park\": 'London',\n",
+    "    'shoreditch': 'London',\n",
+    "    'spitalfields': 'London',\n",
+    "    'belgravia': 'London',\n",
+    "    'bloomsbury': 'London',\n",
+    "    'finsbury': 'London',\n",
+    "    'fulham': 'London',\n",
+    "    'chiswick': 'London',\n",
+    "    'city centre': 'London',\n",
+    "    'city of london': 'London',\n",
+    "    'hyde park': 'London',\n",
+    "    # San Francisco\n",
+    "    'south san francisco': 'San Francisco',\n",
+    "    # Ireland\n",
+    "    'baile mhic andáin/thomastown': 'Thomastown',\n",
+    "    'gaillimh/galway': 'Galway',\n",
+    "    'cill chainnigh/kilkenny': 'Kilkenny',\n",
+    "    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',\n",
+    "    'athína': 'Athens',\n",
+    "    'ballydehob': 'Ballydehob',\n",
+    "    # Finland\n",
+    "    'helsingfors / helsinki': 'Helsinki',\n",
+    "    # Czech Republic\n",
+    "    'praha': 'Prague',\n",
+    "    # Austria\n",
+    "    'wien': 'Vienna',\n",
+    "    'salzburg': 'Salzburg',\n",
+    "    # Menai Bridge\n",
+    "    'menai bridge/porthaethwy': 'Menai Bridge',\n",
+    "    # USA cities\n",
+    "    'los angeles': 'Los Angeles',\n",
+    "    'san diego': 'San Diego',\n",
+    "    'sacramento': 'Sacramento',\n",
+    "    'new york': 'New York',\n",
+    "    'chicago': 'Chicago',\n",
+    "    'costa mesa': 'Costa Mesa',\n",
+    "    'monterey': 'Monterey',\n",
+    "    'washington, d.c.': 'Washington D.C.',\n",
+    "    'south dalton': 'Dalton',\n",
+    "    # Asia\n",
+    "    'bangkok': 'Bangkok',\n",
+    "    'phuket': 'Phuket',\n",
+    "    'hong kong': 'Hong Kong',\n",
+    "    'taipei': 'Taipei',\n",
+    "    'seoul': 'Seoul',\n",
+    "    'singapore': 'Singapore',\n",
+    "    'macau': 'Macau',\n",
+    "    # Croatia\n",
+    "    'lovran': 'Lovran',\n",
+    "    'rovinj': 'Rovinj',\n",
+    "    'zagreb': 'Zagreb',\n",
+    "    'šibenik': 'Sibenik',\n",
+    "    # Norway / Scandinavia\n",
+    "    'stavanger': 'Stavanger',\n",
+    "    'trondheim': 'Trondheim',\n",
+    "    'oslo': 'Oslo',\n",
+    "    'göteborg': 'Gothenburg',\n",
+    "    'växjö': 'Vaxjo',\n",
+    "    'skåne-tranås': 'Skane-Tranas',\n",
+    "    'vejle': 'Vejle',\n",
+    "    # Denmark\n",
+    "    'fredericia': 'Fredericia',\n",
+    "    'pedersker': 'Pedersker',\n",
+    "    'præstø': 'Praesto',\n",
+    "    # Sweden\n",
+    "    'malmö': 'Malmo',\n",
+    "    'stockholm': 'Stockholm',\n",
+    "    # Portugal / Ireland / UK misc\n",
+    "    'bath': 'Bath',\n",
+    "    'bristol': 'Bristol',\n",
+    "    'cambridge': 'Cambridge',\n",
+    "    'cheltenham': 'Cheltenham',\n",
+    "    'chester': 'Chester',\n",
+    "    'birmingham': 'Birmingham',\n",
+    "    'edinburgh': 'Edinburgh',\n",
+    "    'leeds': 'Leeds',\n",
+    "    'oxford': 'Oxford',\n",
+    "    'stratford-upon-avon': 'Stratford-Upon-Avon',\n",
+    "    'padstow': 'Padstow',\n",
+    "    'torquay': 'Torquay',\n",
+    "    'newcastle upon tyne': 'Newcastle upon Tyne',\n",
+    "    'nottingham': 'Nottingham',\n",
+    "    'bray': 'Bray',\n",
+    "    'bowness-on-windermere': 'Bowness-on-Windermere',\n",
+    "    'cartmel': 'Cartmel',\n",
+    "    'castle combe': 'Castle Combe',\n",
+    "    'chagford': 'Chagford',\n",
+    "    'chew magna': 'Chew Magna',\n",
+    "    'dalry': 'Dalry',\n",
+    "    'dorking': 'Dorking',\n",
+    "    'egham': 'Egham',\n",
+    "    'fence': 'Fence',\n",
+    "    'fordwich': 'Fordwich',\n",
+    "    'grasmere': 'Grasmere',\n",
+    "    'gravetye': 'Gravetye',\n",
+    "    'great milton': 'Great Milton',\n",
+    "    'hallwang': 'Hallwang',\n",
+    "    'hampton in arden': 'Hampton in Arden',\n",
+    "    'harome': 'Harome',\n",
+    "    'henne': 'Henne',\n",
+    "    'horsham': 'Horsham',\n",
+    "    'hunstanton': 'Hunstanton',\n",
+    "    'ilfracombe': 'Ilfracombe',\n",
+    "    'järpen': 'Jarpen',\n",
+    "    'kenilworth': 'Kenilworth',\n",
+    "    'kew': 'Kew',\n",
+    "    'kleinwalsertal': 'Kleinwalsertal',\n",
+    "    'knowstone': 'Knowstone',\n",
+    "    'langho': 'Langho',\n",
+    "    'leith': 'Leith',\n",
+    "    'leynar': 'Leynar',\n",
+    "    'little dunmow': 'Little Dunmow',\n",
+    "    'llanddewi skirrid': 'Llanddewi Skirrid',\n",
+    "    'llandrillo': 'Llandrillo',\n",
+    "    'lovran': 'Lovran',\n",
+    "    'lympstone': 'Lympstone',\n",
+    "    'machynlleth': 'Machynlleth',\n",
+    "    'malmesbury': 'Malmesbury',\n",
+    "    'marlow': 'Marlow',\n",
+    "    'morston': 'Morston',\n",
+    "    'mountsorrel': 'Mountsorrel',\n",
+    "    'murcott': 'Murcott',\n",
+    "    'newbury': 'Newbury',\n",
+    "    'oldstead': 'Oldstead',\n",
+    "    'peat inn': 'Peat Inn',\n",
+    "    'penarth': 'Penarth',\n",
+    "    'port isaac': 'Port Isaac',\n",
+    "    'portscatho': 'Portscatho',\n",
+    "    'ripley': 'Ripley',\n",
+    "    'saint helier/saint-hélier': 'Saint Helier',\n",
+    "    \"saint james's\": 'Saint James',\n",
+    "    'seasalter': 'Seasalter',\n",
+    "    'shinfield': 'Shinfield',\n",
+    "    'summerhouse': 'Summerhouse',\n",
+    "    'upper hambleton': 'Hambleton',\n",
+    "    'victoria': 'Victoria',\n",
+    "    'wandsworth': 'London',\n",
+    "    'whitebrook': 'Whitebrook',\n",
+    "    'winchester': 'Winchester',\n",
+    "    'winteringham': 'Winteringham'\n",
+    "}\n",
+    "\n",
+    "stars_df['major_city'] = stars_df['city'].replace(location_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d936e600-5eaf-4813-a493-029cd4447b44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stars_df[['city', 'major_city']].sample(10))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "781e121d-126d-491e-871d-526563dc4d7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['name'].unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7256d411-b0f0-4aec-8e92-4888ba57806c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.tail(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b27be073-e390-4f35-874b-f82d3b6f75f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92c35efa-5040-43ab-870b-7e196664e5cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4bf9e732-a173-44a0-9ad1-467b769dfd7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"cuisine\"].value_counts().head(10).plot(kind=\"bar\")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Cuisine\")\n",
+    "plt.ylabel(\"Nº of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c163a3c-a403-4bb4-a9f1-0c6a6a56abdd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "top_cuisines = (\n",
+    "    stars_df[\"cuisine\"].value_counts()\n",
+    "    .head(10)\n",
+    "    .index\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.countplot(\n",
+    "    data=stars_df[stars_df[\"cuisine\"].isin(top_cuisines)],\n",
+    "    y=\"cuisine\",\n",
+    "    order=top_cuisines\n",
+    ")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Nº of restaurants\")\n",
+    "plt.ylabel(\"Cuisine\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb894fca-55db-4cf2-b4df-f73dda35806a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"stars_n\"] = stars_df[\"stars\"].str[0].astype(int)\n",
+    "\n",
+    "stars_df.groupby(\"region\")[\"stars_n\"].mean().sort_values().plot(kind=\"bar\")\n",
+    "plt.title(\"Mean of Stars per region\")\n",
+    "plt.xlabel(\"Region\")\n",
+    "plt.ylabel(\"Mean of stars\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3acfdf4-815b-4471-8fe9-a1eca7c437d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visuals: \n",
+    "#which are the cities with more restaurants? \n",
+    "#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?\n",
+    "# which are the cuisine dominating a city /region \n",
+    "# avg price point in a specific city based on restaurants?\n",
+    "# time series 2018 vs 2019 any trend? any star restautant grew over past year? \n",
+    "# cheapest vs most expensive cuisine? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ec45bca-a08c-475c-824e-f2422a6a70ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "API_KEY = \"AIzaSyC8VCFpZ1WhNUegfd5ziwZPVRCe1pi35lo\"\n",
+    "\n",
+    "def get_place_rating(name, city, api_key=API_KEY, sleep_sec=0.2):\n",
+    "    query = f\"{name}, {city}\"\n",
+    "    url_search = \"https://maps.googleapis.com/maps/api/place/findplacefromtext/json\"\n",
+    "    params_search = {\n",
+    "        \"input\": query,\n",
+    "        \"inputtype\": \"textquery\",\n",
+    "        \"fields\": \"place_id\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    r = requests.get(url_search, params=params_search)\n",
+    "    data = r.json()\n",
+    "    status = data.get(\"status\")\n",
+    "    print(\"SEARCH:\", query, \"->\", status)\n",
+    "    if status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    place_id = data[\"candidates\"][0][\"place_id\"]\n",
+    "\n",
+    "    url_details = \"https://maps.googleapis.com/maps/api/place/details/json\"\n",
+    "    params_details = {\n",
+    "        \"place_id\": place_id,\n",
+    "        \"fields\": \"rating,user_ratings_total\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    d = requests.get(url_details, params=params_details)\n",
+    "    det = d.json()\n",
+    "    d_status = det.get(\"status\")\n",
+    "    print(\"DETAILS:\", place_id, \"->\", d_status)\n",
+    "    if d_status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    time.sleep(sleep_sec)\n",
+    "    result = det.get(\"result\", {})\n",
+    "    return result.get(\"rating\"), result.get(\"user_ratings_total\")\n",
+    "\n",
+    "\n",
+    "# inicializar colunas (se ainda não existirem)\n",
+    "if \"Review_rating\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_rating\"] = np.nan\n",
+    "if \"Review_count\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_count\"] = np.nan\n",
+    "\n",
+    "# lista de restaurantes (ou partes do nome) que queres atualizar\n",
+    "target_names = [\"eleven madison park\", \"per se\", \"chef's table at brooklyn fare\"]  # podes editar/expandir\n",
+    "\n",
+    "for idx, row in stars_df.iterrows():\n",
+    "    name_lower = str(row[\"name\"]).lower()\n",
+    "\n",
+    "    # verifica se algum dos nomes alvo aparece no name da linha\n",
+    "    if any(tn.lower() in name_lower for tn in target_names):\n",
+    "        rating, count = get_place_rating(row[\"name\"], row[\"city\"])\n",
+    "        stars_df.at[idx, \"Review_rating\"] = rating\n",
+    "        stars_df.at[idx, \"Review_count\"] = count\n",
+    "    # os outros restaurantes ficam como estão (NaN ou valores antigos)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33a9bff2-d6da-40b1-b099-8a4b2e0689f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77fb715e-886d-4644-b619-ba96e9baa884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)\n",
+    "stars_df['name'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d04b692-dd56-4962-bc0b-e9805b8924ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rest_names = [\"per se\", \"eleven madison park\",\"chef's table at brooklyn fare\"]\n",
+    "\n",
+    "mask = stars_df[\"name\"].str.strip().str.lower().isin(\n",
+    "    [n.strip().lower() for n in rest_names]\n",
+    ")\n",
+    "linha_restaurante = stars_df.loc[mask, :]\n",
+    "\n",
+    "print(linha_restaurante)\n",
+    "print(stars_df.loc[mask, \"Review_rating\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e5e741-e192-4855-9334-4e1ba9e51180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e1c6981-0553-4b40-b680-c63804b95c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a970e70-c0af-4c09-a7d0-b405e1511c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", None)  # mostra todas as colunas\n",
+    "stars_df.head()                            # ou stars_df.sample(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7427ec65-ddbb-4f13-9a2d-3fc9888f44b7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/final_pedro.ipynb b/notebooks/final_pedro.ipynb
new file mode 100644
index 00000000..76560b93
--- /dev/null
+++ b/notebooks/final_pedro.ipynb
@@ -0,0 +1,2482 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7873c26f-cc70-4b5d-b204-df1b84b68d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "star1_df = pd.read_csv(\"/Users/ZINA/Desktop/one-star-michelin-restaurants.csv\")\n",
+    "star2_df = pd.read_csv(\"/Users/ZINA/Desktop/two-stars-michelin-restaurants.csv\")\n",
+    "star3_df = pd.read_csv(\"/Users/ZINA/Desktop/three-stars-michelin-restaurants.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2e5d460d-7bd0-402d-851f-82ddc4f4ca7a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Drop unwanted columns\n",
+    "star1_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c6a101a8-74e5-4007-98cb-2cdd0d310fd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star2_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e8cec71f-c813-40d9-92a7-2ea32ada988b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "star3_df.drop(columns=['zipCode'], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "f6ea2440-dbc3-4c55-98b7-051971568e95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creating a star column\n",
+    "star1_df['stars'] = '1 star'\n",
+    "star2_df['stars'] = '2 stars'\n",
+    "star3_df['stars'] = '3 stars'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "6a0424c0-8c00-4736-bb30-bcf33f8ab101",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Concatenating the 3 datasets \n",
+    "stars_df = pd.concat([star1_df, star2_df, star3_df], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ec4465cb-7e32-4c70-a6b5-6f6179a31f21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace $$$$$ with $$$$\n",
+    "stars_df['price'] = stars_df['price'].str.replace('$$$$$', '$$$$', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ff99db1d-7bb4-40ee-b8fb-70a4f192e228",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean weird characters\n",
+    "stars_df['price'] = stars_df['price'].str.strip()\n",
+    "stars_df['price'] = stars_df['price'].str.replace(r'\\s+', '', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3fc94f5b-6f23-4f4d-86df-cc6a53b36e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert $ to ordinal numbers\n",
+    "stars_df['price_ordinal'] = stars_df['price'].str.count(r'\\$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5354f300-4299-46bd-a61e-ab56f935e334",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute median ordinal per star group (1 star, 2 stars, 3 stars)\n",
+    "median_by_star = stars_df.groupby('stars')['price_ordinal'].median().round()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "f2bcea8c-3e8c-458b-9898-caf5e14bec74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Replace missing ordinal values using matching star median\n",
+    "stars_df['price_ordinal'] = stars_df['price_ordinal'].fillna(stars_df['stars'].map(median_by_star)).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "d8afb6f3-10d9-4775-82a8-f49852a2766c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now convert back to $ string after filling\n",
+    "stars_df['price'] = stars_df['price_ordinal'].apply(lambda x: '$' * x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d5a1e8ae-c0fe-4281-9755-32a87caa105d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the mapping\n",
+    "price_mean_map = {\n",
+    "    \"$\": 20,\n",
+    "    \"$$\": 37.5,\n",
+    "    \"$$$\": 62.5,\n",
+    "    \"$$$$\": 100\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d22c338b-3d05-4de2-b79f-34d13b6b3746",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a new column with the mean price\n",
+    "stars_df['price_mean'] = stars_df['price'].map(price_mean_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8876ecd1-48b9-4678-aa1b-32935c85d3ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "name             0\n",
+       "year             0\n",
+       "latitude         0\n",
+       "longitude        0\n",
+       "city             2\n",
+       "region           0\n",
+       "cuisine          0\n",
+       "price            0\n",
+       "url              0\n",
+       "stars            0\n",
+       "price_ordinal    0\n",
+       "price_mean       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# checking if there are no null values in all price columns \n",
+    "stars_df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "debd9f44-c5ab-43ae-a8c3-89d9c95b5e30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>price_ordinal</th>\n",
+       "      <th>price_mean</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Kilian Stuba</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Kleinwalsertal</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vorarlberg/kl...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pfefferschiff</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Hallwang</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Classic cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Esszimmer</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Carpe Diem</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Market cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/salzburg-regi...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Edvard</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Wien</td>\n",
+       "      <td>Austria</td>\n",
+       "      <td>Modern cuisine</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/at/en/vienna/wien/r...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            name  year            city   region          cuisine price  \\\n",
+       "0   Kilian Stuba  2019  Kleinwalsertal  Austria         Creative  $$$$   \n",
+       "1  Pfefferschiff  2019        Hallwang  Austria  Classic cuisine  $$$$   \n",
+       "2      Esszimmer  2019        Salzburg  Austria         Creative  $$$$   \n",
+       "3     Carpe Diem  2019        Salzburg  Austria   Market cuisine  $$$$   \n",
+       "4         Edvard  2019            Wien  Austria   Modern cuisine  $$$$   \n",
+       "\n",
+       "                                                 url   stars  price_ordinal  \\\n",
+       "0  https://guide.michelin.com/at/en/vorarlberg/kl...  1 star              4   \n",
+       "1  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "2  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "3  https://guide.michelin.com/at/en/salzburg-regi...  1 star              4   \n",
+       "4  https://guide.michelin.com/at/en/vienna/wien/r...  1 star              4   \n",
+       "\n",
+       "   price_mean  \n",
+       "0       100.0  \n",
+       "1       100.0  \n",
+       "2       100.0  \n",
+       "3       100.0  \n",
+       "4       100.0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Drop columns\n",
+    "stars_df.drop(columns=['latitude', 'longitude'], inplace=True)\n",
+    "stars_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ca19b85e-c62c-43fb-a5d9-60d373dd7af2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#put in lower for joining for example 'creative' with 'Creative'\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].str.strip().str.lower() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2f2deecb-3847-4c17-8b99-bb9a5b120344",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "international_types = [\"modern cuisine\", \"classic cuisine\",\n",
+    "                       \"street food\", \"meats and grills\", \"international\", \"innovative\"]\n",
+    "\n",
+    "stars_df[\"cuisine_original\"] = stars_df[\"cuisine\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(\n",
+    "    international_types, \"international cuisine\")\n",
+    "\n",
+    "international_sub = stars_df[\n",
+    "    stars_df[\"cuisine_original\"].isin(international_types)]\n",
+    "\n",
+    "ax = international_sub[\"cuisine_original\"].value_counts().plot(kind=\"bar\")\n",
+    "\n",
+    "plt.title(\"Distribution of subtypes within international cuisine\")\n",
+    "plt.ylabel(\"Number of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "note = (\"Modern cuisine combines global flavours with local and seasonal ingredients, \"\"while innovative refers to more experimental concepts such as molecular \"\"gastronomy or 3D‑printed food.\")\n",
+    "\n",
+    "plt.subplots_adjust(bottom=0.3)\n",
+    "\n",
+    "plt.figtext(\n",
+    "    0.5,\n",
+    "    0.02,\n",
+    "    note,\n",
+    "    ha=\"center\",\n",
+    "    va=\"bottom\",\n",
+    "    wrap=True,\n",
+    "    fontsize=9\n",
+    ")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "53a35045-f5f6-4110-b174-5f88bd9093ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "70648bad-2bfc-4a30-8721-5d31abc18443",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute, in this case \"Chinese food\" \n",
+    "chinese_type = [\"chinese\", \n",
+    "    \"cantonese\",\n",
+    "    \"cantonese roast meats\",\n",
+    "    \"dim sum\",\n",
+    "    \"shanghainese\",\n",
+    "    \"sichuan\",\n",
+    "    \"hunanese and sichuan\",\n",
+    "    \"sichuan-huai yang\",\n",
+    "    \"fujian\",\n",
+    "    \"taizhou\",\n",
+    "    \"hang zhou\",\n",
+    "    \"noodles and congee\"]\n",
+    "\n",
+    "stars_df[\"cuisine\"] = stars_df[\"cuisine\"].replace(chinese_type, \"chinese\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "18eff245-8e00-40d4-a6cb-4215aea72c6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "53"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "01836bf8-034e-4cb5-aa25-679c373a6155",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#joining all the food subcategories into a main attribute\n",
+    "korean_types = ['korean',\n",
+    "    'korean contemporary',\n",
+    "    'temple cuisine']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(korean_types, 'korean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "33116181-30c8-4b33-a28a-5f770935da02",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e33b6a97-f1f2-4cfa-bc73-3065516a4100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thai_types = ['thai',\n",
+    "    'thai contemporary',\n",
+    "    'southern thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(thai_types, 'thai')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "9a198e63-0358-4d15-91e3-3ab25070f5c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1f39652c-5412-47c0-b0bc-4f1e8ca36a57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "american_types = ['american',\n",
+    "    'californian',\n",
+    "    'barbecue',\n",
+    "    'steakhouse']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(american_types, 'american')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "6d1d5eca-2061-44dd-8a89-513fa6d5116d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "46"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "07f0bf7e-e580-4c53-85b5-112004625e17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "french_types = ['french',\n",
+    "    'classic french',\n",
+    "    'french contemporary',\n",
+    "    'modern french',\n",
+    "    'creative french']\n",
+    "\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(french_types, 'french')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d995f47c-7f09-4a1d-b8b0-af2639174996",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "90196101-a614-4d9e-903d-0ddd2136bc56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "japanese_types = ['japanese',\n",
+    "    'sushi',\n",
+    "    'teppanyaki',\n",
+    "    'japanese contemporary']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(japanese_types, 'japanese')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "b5d63dac-f061-4e69-af34-1913465539ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "f7c979b4-e012-4a6e-a95f-cd9a32b31f5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_asian_types = ['asian',\n",
+    "    'asian influences',\n",
+    "    'asian contemporary',\n",
+    "    'fusion','taiwanese','peranakan','thai']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_asian_types, 'other asian')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "65715bf6-d434-4c3a-8e7b-e3666faf90a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "197594f3-4e30-4614-826b-050ca5de00d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "british_types = ['modern british',\n",
+    "    'traditional british',\n",
+    "    'creative british']\n",
+    "\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(british_types, 'british')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "112b202f-5d66-43f9-a999-62856ed29d1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c0d2074a-bf80-4057-90b5-e035eb5b4a8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "modern_types = ['modern cuisine',\n",
+    "    'modern','modern food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(modern_types, 'modern cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "8270bdaf-1ae0-469f-be7d-9197ce9b5e28",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "75ce0e4a-0c0f-40d9-bb71-384562cfbdef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'market cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'mediterranean',\n",
+       "       'seafood', 'european contemporary', 'scandinavian', 'austrian',\n",
+       "       'spanish', 'british', 'modern cuisine', 'australian',\n",
+       "       'italian contemporary', 'european', 'regional cuisine',\n",
+       "       'mediterranean cuisine'], dtype=object)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "9ca6b75d-eedc-434e-891d-5d171615f124",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "market_types = ['classic cuisine','market cuisine', 'regional cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(market_types, 'classic cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "461da05b-22f3-4242-ade1-69072bf55cf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "c1b5284a-59cf-4aaa-8c44-1b21594cec3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mediterranean_types = ['mediterranean', 'mediterranean cuisine']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(mediterranean_types, 'mediterranean food')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "9fbc1139-5470-4594-8382-53236b9e62c0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'classic cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'mediterranean food',\n",
+       "       'seafood', 'european contemporary', 'scandinavian', 'austrian',\n",
+       "       'spanish', 'british', 'modern cuisine', 'australian',\n",
+       "       'italian contemporary', 'european'], dtype=object)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "42b900bb-af39-442f-94d2-abcab9710dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "other_european_types = ['european', 'european contemporary','mediterranean food']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(other_european_types, 'other european')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ec17fcff-6605-477c-a4e6-90b92835266e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "88adeee8-713c-4ce2-8100-7754993de586",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "italian_types = ['italian', 'italian contemporary']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(italian_types, 'italian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "aa1fc4be-87bd-4a6f-8cae-c95cc96b2598",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "56b5bfdd-81f6-4b3f-865c-eff95f23a7a7",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique() #confirm if that values joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "0b09fd01-fbfc-48f6-98d2-57c2f5bf1b64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'classic cuisine', 'japanese',\n",
+       "       'vegetarian', 'contemporary', 'indian', 'korean', 'american',\n",
+       "       'moroccan', 'other asian', 'chinese', 'italian', 'french',\n",
+       "       'mexican', 'gastropub', 'danish', 'finnish', 'other european',\n",
+       "       'seafood', 'scandinavian', 'austrian', 'spanish', 'british',\n",
+       "       'modern cuisine', 'australian'], dtype=object)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "e540377f-e185-48a3-b3a4-24b480f13ac4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "international_types = ['modern cuisine','classic cuisine', 'street food','meats and grills','international','innovative']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'international cuisine')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "868eea3a-d9d9-49ff-a24d-2ae6e36a8e7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scandinavian_types = ['danish','finnish', 'scandinavian']\n",
+    "stars_df['cuisine'] = stars_df['cuisine'].replace(international_types, 'scandinavian')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "62a2e925-3ccb-4339-89f4-fdeb960ea0c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['creative', 'international cuisine', 'japanese', 'vegetarian',\n",
+       "       'contemporary', 'indian', 'korean', 'american', 'moroccan',\n",
+       "       'other asian', 'chinese', 'italian', 'french', 'mexican',\n",
+       "       'gastropub', 'danish', 'finnish', 'other european', 'seafood',\n",
+       "       'scandinavian', 'austrian', 'spanish', 'british', 'australian'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "34edec4d-5578-4197-bba9-65cb44ebf311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "24"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['cuisine'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "84f3890b-2733-47b9-a271-08f9dc272006",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['$', '$$', '$$$', '$$$$'], dtype=object)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"price\")  \n",
+    "stars_df['price'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b9ff2db0-a438-4594-8851-21ad6ec96cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "684"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['name'].nunique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "cb5c0a0b-205e-47c2-99e5-160d2e9b94a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'price_ordinal', 'price_mean', 'cuisine_original'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "4b36c09a-8535-49df-b11d-82c5fae0875a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306                            jin jin\n",
+      "374                        da san yuan\n",
+      "169                            yat lok\n",
+      "308    hill street tai hwa pork noodle\n",
+      "309            putien (kitchener road)\n",
+      "                    ...               \n",
+      "229                    gramercy tavern\n",
+      "228                               noda\n",
+      "225                              nomad\n",
+      "242                              agern\n",
+      "694                      gordon ramsay\n",
+      "Name: name, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Restaurant name standardization - lower case\n",
+    "\n",
+    "stars_df['name']= stars_df['name'].str.lower()\n",
+    "print (stars_df['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "30eb95b6-64bf-4412-be57-e3db0536f906",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                             name  year                    city  \\\n",
+      "275                         lasai  2019  Rio de Janeiro - 22271   \n",
+      "309       putien (kitchener road)  2018               Singapore   \n",
+      "194  8 1/2 otto e mezzo - bombana  2019                   Macau   \n",
+      "377         upstairs at mikkeller  2019                 Bangkok   \n",
+      "331         shinji (tanglin road)  2018               Singapore   \n",
+      "\n",
+      "             region                cuisine price  \\\n",
+      "275  Rio de Janeiro  international cuisine  $$$$   \n",
+      "309       Singapore                chinese     $   \n",
+      "194           Macau                italian   $$$   \n",
+      "377        Thailand  international cuisine  $$$$   \n",
+      "331       Singapore               japanese  $$$$   \n",
+      "\n",
+      "                                                   url   stars  price_ordinal  \\\n",
+      "275  https://guide.michelin.com/br/en/rio-de-janeir...  1 star              4   \n",
+      "309  https://guide.michelin.com/sg/en/singapore-reg...  1 star              1   \n",
+      "194  https://guide.michelin.com/mo/en/macau-region/...  1 star              3   \n",
+      "377  https://guide.michelin.com/th/en/bangkok-regio...  1 star              4   \n",
+      "331  https://guide.michelin.com/sg/en/singapore-reg...  1 star              4   \n",
+      "\n",
+      "     price_mean cuisine_original  \n",
+      "275       100.0           modern  \n",
+      "309        20.0           fujian  \n",
+      "194        62.5          italian  \n",
+      "377       100.0       innovative  \n",
+      "331       100.0            sushi  \n"
+     ]
+    }
+   ],
+   "source": [
+    "#Trim Excessive Whitespaces:\n",
+    "\n",
+    "stars_df['name'] = stars_df['name'].astype(str)\n",
+    "stars_df['name']= stars_df['name'].apply(lambda x: ' '.join(x.split()))\n",
+    "\n",
+    "print(stars_df.sample(5)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "b6eb2f6a-93b2-47d0-8262-10e3dde375d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# year check\n",
+    "\n",
+    "stars_df['year'].nunique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "6bf643cd-63d0-4762-a772-e99dffa5256a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306    2019\n",
+      "374    2019\n",
+      "169    2019\n",
+      "308    2018\n",
+      "309    2018\n",
+      "       ... \n",
+      "229    2019\n",
+      "228    2019\n",
+      "225    2019\n",
+      "242    2019\n",
+      "694    2019\n",
+      "Name: year, Length: 695, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stars_df['year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "caa32e9d-be69-4ede-80a5-47c40dbbe909",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "179"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# city names check\n",
+    "stars_df['city'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8e50bafb-9f38-4b8a-9575-a65bffc186d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Seoul', 'Taipei', 'Hong Kong', 'Singapore', 'Bangkok', 'Macau',\n",
+       "       'Chicago', 'New York', 'København', 'Los Angeles',\n",
+       "       'Washington, D.C.', 'San Francisco', 'Malmö', 'Stockholm',\n",
+       "       'Blackrock', 'Leith', 'Aird Mhór/Ardmore', 'Belfast',\n",
+       "       'Pateley Bridge', 'Baile Mhic Andáin/Thomastown', 'Baltimore',\n",
+       "       'Edinburgh', 'Peat Inn', 'Westminster', 'Anstruther', 'Grasmere',\n",
+       "       'Bowness-on-Windermere', 'Henne', 'Cill Chainnigh/Kilkenny',\n",
+       "       'Cartmel', 'Gaillimh/Galway', 'Menai Bridge/Porthaethwy',\n",
+       "       'Lios Dúin Bhearna/Lisdoonvarna', 'Ballydehob', 'Corcaigh/Cork',\n",
+       "       'City Centre', 'Langho', 'Newcastle upon Tyne', 'Dalry',\n",
+       "       'City of London', 'Bermondsey', 'Clapham Common', 'Wandsworth',\n",
+       "       'Växjö', 'Dorking', 'Horsham', 'Gravetye', 'Clerkenwell',\n",
+       "       'Victoria', 'London', 'Shoreditch', 'Finsbury', 'Spitalfields',\n",
+       "       'Seasalter', 'Göteborg', 'Chelsea', \"Saint James's\",\n",
+       "       'São Paulo - 04538', 'Saint Helier/Saint-Hélier', 'Biddenden',\n",
+       "       'Fordwich', 'Waternish', 'Birkenhead', 'Bray', 'Winchester',\n",
+       "       'Bagshot', 'Ascot', 'Egham', 'Kew', 'Chiswick', 'Little Dunmow',\n",
+       "       'Hammersmith', 'Kensington', 'Marylebone', 'Shinfield',\n",
+       "       \"Burchett's Green\", 'Colerne', 'Bath', 'Lympstone', 'Hunstanton',\n",
+       "       'Oxford', 'Murcott', 'Morston', 'Costa Mesa', 'East Chisenbury',\n",
+       "       'Newbury', 'Marlow', 'Torquay', \"Regent's Park\", 'Fulham', 'Soho',\n",
+       "       'Mayfair', 'South San Francisco', 'Belgravia', 'Ripley',\n",
+       "       'Bloomsbury', 'Castle Combe', 'Malmesbury', 'Birmingham',\n",
+       "       'Budapest', 'Port Isaac', 'Hampton in Arden', 'Whitebrook',\n",
+       "       'Penarth', 'Portscatho', 'Mountsorrel', nan, 'Padstow',\n",
+       "       'Llandrillo', 'Machynlleth', 'Oldstead', 'Chester',\n",
+       "       'Llanddewi Skirrid', 'Harome', 'Leeds', 'Montgomery/Trefaldwyn',\n",
+       "       'South Dalton', 'Baslow', 'Oslo', 'Winteringham', 'Fence',\n",
+       "       'Kenilworth', 'Chagford', 'Fredericia', 'Vejle', 'Upper Hambleton',\n",
+       "       'Aarhus', 'Bristol', 'Chew Magna', 'Cheltenham', 'Præstø',\n",
+       "       'Knowstone', 'Stratford-upon-Avon', 'Helsingfors / Helsinki',\n",
+       "       'Ilfracombe', 'Kleinwalsertal', 'Salzburg', 'Hyde Park',\n",
+       "       'North Kensington', 'Cambridge', 'Wien', 'Great Milton',\n",
+       "       'Nottingham', 'Aughton', 'Summerhouse', 'Auchterarder',\n",
+       "       'Skåne-Tranås', 'Athína', 'Leynar', 'Järpen', 'São Paulo - 01411',\n",
+       "       'São Paulo - 05416', 'Rio de Janeiro - 22441', 'Rovinj',\n",
+       "       'San Diego', 'Lovran', 'Zagreb', 'Šibenik', 'Dubrovnik',\n",
+       "       'Pedersker', 'Hørve', 'Praha', 'Hallwang', 'Sacramento',\n",
+       "       'Monterey', 'São Paulo - 04080', 'São Paulo - 01401',\n",
+       "       'São Paulo - 04509', 'São Paulo - 05706', 'São Paulo - 04531',\n",
+       "       'São Paulo - 01426', 'São Paulo - 05415', 'São Paulo - 05413',\n",
+       "       'Rio de Janeiro - 22021', 'Rio de Janeiro - 22271', 'Phuket',\n",
+       "       'Rio de Janeiro - 22470', 'Warszawa', 'Stavanger', 'Trondheim'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['city'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "3fad5dfe-be46-45e6-9609-414aa470e0db",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306        Seoul\n",
+      "374       Taipei\n",
+      "169    Hong Kong\n",
+      "308    Singapore\n",
+      "309    Singapore\n",
+      "         ...    \n",
+      "229     New York\n",
+      "228     New York\n",
+      "225     New York\n",
+      "242     New York\n",
+      "694      Chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Remove Leading/Trailing Spaces\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.strip()\n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e8e7151e-b4db-4076-835e-cd82139b24e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "306        seoul\n",
+      "374       taipei\n",
+      "169    hong kong\n",
+      "308    singapore\n",
+      "309    singapore\n",
+      "         ...    \n",
+      "229     new york\n",
+      "228     new york\n",
+      "225     new york\n",
+      "242     new york\n",
+      "694      chelsea\n",
+      "Name: city, Length: 695, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "#convert to lower case\n",
+    "\n",
+    "stars_df['city'] = stars_df['city'].str.lower() \n",
+    "print(stars_df['city'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "654fc3c3-545b-4115-8f8b-a2514b44e138",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                name  year       city          region  \\\n",
+      "306                          jin jin  2019      seoul     South Korea   \n",
+      "374                      da san yuan  2019     taipei          Taipei   \n",
+      "169                          yat lok  2019  hong kong       Hong Kong   \n",
+      "308  hill street tai hwa pork noodle  2018  singapore       Singapore   \n",
+      "309          putien (kitchener road)  2018  singapore       Singapore   \n",
+      "..                               ...   ...        ...             ...   \n",
+      "229                  gramercy tavern  2019   new york   New York City   \n",
+      "228                             noda  2019   new york   New York City   \n",
+      "225                            nomad  2019   new york   New York City   \n",
+      "242                            agern  2019   new york   New York City   \n",
+      "694                    gordon ramsay  2019    chelsea  United Kingdom   \n",
+      "\n",
+      "                   cuisine price  \\\n",
+      "306                chinese     $   \n",
+      "374                chinese     $   \n",
+      "169                chinese     $   \n",
+      "308  international cuisine     $   \n",
+      "309                chinese     $   \n",
+      "..                     ...   ...   \n",
+      "229           contemporary  $$$$   \n",
+      "228               japanese  $$$$   \n",
+      "225           contemporary  $$$$   \n",
+      "242           scandinavian  $$$$   \n",
+      "694                 french  $$$$   \n",
+      "\n",
+      "                                                   url    stars  \\\n",
+      "306  https://guide.michelin.com/kr/en/seoul-capital...   1 star   \n",
+      "374  https://guide.michelin.com/tw/en/taipei-region...   1 star   \n",
+      "169  https://guide.michelin.com/hk/en/hong-kong-reg...   1 star   \n",
+      "308  https://guide.michelin.com/sg/en/singapore-reg...   1 star   \n",
+      "309  https://guide.michelin.com/sg/en/singapore-reg...   1 star   \n",
+      "..                                                 ...      ...   \n",
+      "229  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "228  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "225  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "242  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+      "694  https://guide.michelin.com/gb/en/greater-londo...  3 stars   \n",
+      "\n",
+      "     price_ordinal  price_mean       cuisine_original  \n",
+      "306              1        20.0                chinese  \n",
+      "374              1        20.0              cantonese  \n",
+      "169              1        20.0  cantonese roast meats  \n",
+      "308              1        20.0            street food  \n",
+      "309              1        20.0                 fujian  \n",
+      "..             ...         ...                    ...  \n",
+      "229              4       100.0           contemporary  \n",
+      "228              4       100.0               japanese  \n",
+      "225              4       100.0           contemporary  \n",
+      "242              4       100.0           scandinavian  \n",
+      "694              4       100.0                 french  \n",
+      "\n",
+      "[571 rows x 11 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#check for duplicates\n",
+    "\n",
+    "duplicates = stars_df[stars_df.duplicated(['city'], keep=False)]\n",
+    "print(duplicates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "ee3f59d0-5adb-468b-86db-bd6ea87e1463",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# order A–Z\n",
+    "\n",
+    "#stars_df = stars_df.sort_values(by=\"city\")  \n",
+    "#stars_df['city'].unique()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "9a70b42b-accf-46fc-a32a-2377e25b0024",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove numbers and zip codes\n",
+    "\n",
+    "import re\n",
+    "\n",
+    "def clean_city_name(city_name):\n",
+    "    if isinstance(city_name, str):  # Check if the input is a string\n",
+    "        # Use regex to remove \" - numbers\" at the end of the string\n",
+    "        return re.sub(r'\\s-\\s\\d+$', '', city_name).strip()\n",
+    "    return city_name  # Return as is if it's not a string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "bb772e24-05ae-41ac-9d33-521afbb9068c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].apply(clean_city_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "565e3036-376f-42ae-ab1a-262ae8a046de",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['seoul' 'taipei' 'hong kong' 'singapore' 'bangkok' 'macau' 'chicago'\n",
+      " 'new york' 'københavn' 'los angeles' 'washington, d.c.' 'san francisco'\n",
+      " 'malmö' 'stockholm' 'blackrock' 'leith' 'aird mhór/ardmore' 'belfast'\n",
+      " 'pateley bridge' 'baile mhic andáin/thomastown' 'baltimore' 'edinburgh'\n",
+      " 'peat inn' 'westminster' 'anstruther' 'grasmere' 'bowness-on-windermere'\n",
+      " 'henne' 'cill chainnigh/kilkenny' 'cartmel' 'gaillimh/galway'\n",
+      " 'menai bridge/porthaethwy' 'lios dúin bhearna/lisdoonvarna' 'ballydehob'\n",
+      " 'corcaigh/cork' 'city centre' 'langho' 'newcastle upon tyne' 'dalry'\n",
+      " 'city of london' 'bermondsey' 'clapham common' 'wandsworth' 'växjö'\n",
+      " 'dorking' 'horsham' 'gravetye' 'clerkenwell' 'victoria' 'london'\n",
+      " 'shoreditch' 'finsbury' 'spitalfields' 'seasalter' 'göteborg' 'chelsea'\n",
+      " \"saint james's\" 'são paulo' 'saint helier/saint-hélier' 'biddenden'\n",
+      " 'fordwich' 'waternish' 'birkenhead' 'bray' 'winchester' 'bagshot' 'ascot'\n",
+      " 'egham' 'kew' 'chiswick' 'little dunmow' 'hammersmith' 'kensington'\n",
+      " 'marylebone' 'shinfield' \"burchett's green\" 'colerne' 'bath' 'lympstone'\n",
+      " 'hunstanton' 'oxford' 'murcott' 'morston' 'costa mesa' 'east chisenbury'\n",
+      " 'newbury' 'marlow' 'torquay' \"regent's park\" 'fulham' 'soho' 'mayfair'\n",
+      " 'south san francisco' 'belgravia' 'ripley' 'bloomsbury' 'castle combe'\n",
+      " 'malmesbury' 'birmingham' 'budapest' 'port isaac' 'hampton in arden'\n",
+      " 'whitebrook' 'penarth' 'portscatho' 'mountsorrel' nan 'padstow'\n",
+      " 'llandrillo' 'machynlleth' 'oldstead' 'chester' 'llanddewi skirrid'\n",
+      " 'harome' 'leeds' 'montgomery/trefaldwyn' 'south dalton' 'baslow' 'oslo'\n",
+      " 'winteringham' 'fence' 'kenilworth' 'chagford' 'fredericia' 'vejle'\n",
+      " 'upper hambleton' 'aarhus' 'bristol' 'chew magna' 'cheltenham' 'præstø'\n",
+      " 'knowstone' 'stratford-upon-avon' 'helsingfors / helsinki' 'ilfracombe'\n",
+      " 'kleinwalsertal' 'salzburg' 'hyde park' 'north kensington' 'cambridge'\n",
+      " 'wien' 'great milton' 'nottingham' 'aughton' 'summerhouse' 'auchterarder'\n",
+      " 'skåne-tranås' 'athína' 'leynar' 'järpen' 'rio de janeiro' 'rovinj'\n",
+      " 'san diego' 'lovran' 'zagreb' 'šibenik' 'dubrovnik' 'pedersker' 'hørve'\n",
+      " 'praha' 'hallwang' 'sacramento' 'monterey' 'phuket' 'warszawa'\n",
+      " 'stavanger' 'trondheim']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verify results\n",
+    "\n",
+    "print(stars_df['city'].unique())  # Display unique city names to verify the cleaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "7ca0d239-d0d6-4bf6-a80b-52abfe117f87",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remove special characters \n",
+    "stars_df['city'] = stars_df['city'].str.replace('/', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "ebe148d4-da9d-49bf-95cb-7dc791e9dbe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.replace('-', ' ', regex=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "bb93350d-af11-4757-b2ea-fe2ac99ca466",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df['city'] = stars_df['city'].str.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "efa0705f-be86-4f5d-b89a-9831265b29e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'unidecode'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[72]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m#the city column are stripped of accents and are presented in ASCII format.\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01munidecode\u001b[39;00m\n\u001b[32m      4\u001b[39m stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m] = stars_df[\u001b[33m'\u001b[39m\u001b[33mcity\u001b[39m\u001b[33m'\u001b[39m].astype(\u001b[38;5;28mstr\u001b[39m).apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: unidecode.unidecode(x))\n",
+      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'unidecode'"
+     ]
+    }
+   ],
+   "source": [
+    "#the city column are stripped of accents and are presented in ASCII format.\n",
+    "\n",
+    "import unidecode\n",
+    "stars_df['city'] = stars_df['city'].astype(str).apply(lambda x: unidecode.unidecode(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "94bb2635-47d6-47df-aa22-d0d1d1d86463",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Seoul' 'Taipei' 'Hong Kong' 'Singapore' 'Bangkok' 'Macau' 'Chicago'\n",
+      " 'New York' 'København' 'Los Angeles' 'Washington, D.C.' 'San Francisco'\n",
+      " 'Malmö' 'Stockholm' 'Blackrock' 'Leith' 'Aird Mhór Ardmore' 'Belfast'\n",
+      " 'Pateley Bridge' 'Baile Mhic Andáin Thomastown' 'Baltimore' 'Edinburgh'\n",
+      " 'Peat Inn' 'Westminster' 'Anstruther' 'Grasmere' 'Bowness On Windermere'\n",
+      " 'Henne' 'Cill Chainnigh Kilkenny' 'Cartmel' 'Gaillimh Galway'\n",
+      " 'Menai Bridge Porthaethwy' 'Lios Dúin Bhearna Lisdoonvarna' 'Ballydehob'\n",
+      " 'Corcaigh Cork' 'City Centre' 'Langho' 'Newcastle Upon Tyne' 'Dalry'\n",
+      " 'City Of London' 'Bermondsey' 'Clapham Common' 'Wandsworth' 'Växjö'\n",
+      " 'Dorking' 'Horsham' 'Gravetye' 'Clerkenwell' 'Victoria' 'London'\n",
+      " 'Shoreditch' 'Finsbury' 'Spitalfields' 'Seasalter' 'Göteborg' 'Chelsea'\n",
+      " \"Saint James'S\" 'São Paulo' 'Saint Helier Saint Hélier' 'Biddenden'\n",
+      " 'Fordwich' 'Waternish' 'Birkenhead' 'Bray' 'Winchester' 'Bagshot' 'Ascot'\n",
+      " 'Egham' 'Kew' 'Chiswick' 'Little Dunmow' 'Hammersmith' 'Kensington'\n",
+      " 'Marylebone' 'Shinfield' \"Burchett'S Green\" 'Colerne' 'Bath' 'Lympstone'\n",
+      " 'Hunstanton' 'Oxford' 'Murcott' 'Morston' 'Costa Mesa' 'East Chisenbury'\n",
+      " 'Newbury' 'Marlow' 'Torquay' \"Regent'S Park\" 'Fulham' 'Soho' 'Mayfair'\n",
+      " 'South San Francisco' 'Belgravia' 'Ripley' 'Bloomsbury' 'Castle Combe'\n",
+      " 'Malmesbury' 'Birmingham' 'Budapest' 'Port Isaac' 'Hampton In Arden'\n",
+      " 'Whitebrook' 'Penarth' 'Portscatho' 'Mountsorrel' nan 'Padstow'\n",
+      " 'Llandrillo' 'Machynlleth' 'Oldstead' 'Chester' 'Llanddewi Skirrid'\n",
+      " 'Harome' 'Leeds' 'Montgomery Trefaldwyn' 'South Dalton' 'Baslow' 'Oslo'\n",
+      " 'Winteringham' 'Fence' 'Kenilworth' 'Chagford' 'Fredericia' 'Vejle'\n",
+      " 'Upper Hambleton' 'Aarhus' 'Bristol' 'Chew Magna' 'Cheltenham' 'Præstø'\n",
+      " 'Knowstone' 'Stratford Upon Avon' 'Helsingfors   Helsinki' 'Ilfracombe'\n",
+      " 'Kleinwalsertal' 'Salzburg' 'Hyde Park' 'North Kensington' 'Cambridge'\n",
+      " 'Wien' 'Great Milton' 'Nottingham' 'Aughton' 'Summerhouse' 'Auchterarder'\n",
+      " 'Skåne Tranås' 'Athína' 'Leynar' 'Järpen' 'Rio De Janeiro' 'Rovinj'\n",
+      " 'San Diego' 'Lovran' 'Zagreb' 'Šibenik' 'Dubrovnik' 'Pedersker' 'Hørve'\n",
+      " 'Praha' 'Hallwang' 'Sacramento' 'Monterey' 'Phuket' 'Warszawa'\n",
+      " 'Stavanger' 'Trondheim']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verify the results\n",
+    "print(stars_df['city'].unique()) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "33b41649-7526-40c2-9a6a-9cd76700334a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#grouping suburbs into major city and add info in a new column \n",
+    "\n",
+    "#create a dictionary \n",
+    "\n",
+    "location_map = {\n",
+    "    # London + neighborhoods\n",
+    "    'north kensington': 'London',\n",
+    "    'kensington': 'London',\n",
+    "    'westminster': 'London',\n",
+    "    'soho': 'London',\n",
+    "    'mayfair': 'London',\n",
+    "    'marylebone': 'London',\n",
+    "    'chelsea': 'London',\n",
+    "    'clapham common': 'London',\n",
+    "    \"regent's park\": 'London',\n",
+    "    'shoreditch': 'London',\n",
+    "    'spitalfields': 'London',\n",
+    "    'belgravia': 'London',\n",
+    "    'bloomsbury': 'London',\n",
+    "    'finsbury': 'London',\n",
+    "    'fulham': 'London',\n",
+    "    'chiswick': 'London',\n",
+    "    'city centre': 'London',\n",
+    "    'city of london': 'London',\n",
+    "    'hyde park': 'London',\n",
+    "    # San Francisco\n",
+    "    'south san francisco': 'San Francisco',\n",
+    "    # Ireland\n",
+    "    'baile mhic andáin/thomastown': 'Thomastown',\n",
+    "    'gaillimh/galway': 'Galway',\n",
+    "    'cill chainnigh/kilkenny': 'Kilkenny',\n",
+    "    'lios dúin bhearna/lisdoonvarna': 'Lisdoonvarna',\n",
+    "    'athína': 'Athens',\n",
+    "    'ballydehob': 'Ballydehob',\n",
+    "    # Finland\n",
+    "    'helsingfors / helsinki': 'Helsinki',\n",
+    "    # Czech Republic\n",
+    "    'praha': 'Prague',\n",
+    "    # Austria\n",
+    "    'wien': 'Vienna',\n",
+    "    'salzburg': 'Salzburg',\n",
+    "    # Menai Bridge\n",
+    "    'menai bridge/porthaethwy': 'Menai Bridge',\n",
+    "    # USA cities\n",
+    "    'los angeles': 'Los Angeles',\n",
+    "    'san diego': 'San Diego',\n",
+    "    'sacramento': 'Sacramento',\n",
+    "    'new york': 'New York',\n",
+    "    'chicago': 'Chicago',\n",
+    "    'costa mesa': 'Costa Mesa',\n",
+    "    'monterey': 'Monterey',\n",
+    "    'washington, d.c.': 'Washington D.C.',\n",
+    "    'south dalton': 'Dalton',\n",
+    "    # Asia\n",
+    "    'bangkok': 'Bangkok',\n",
+    "    'phuket': 'Phuket',\n",
+    "    'hong kong': 'Hong Kong',\n",
+    "    'taipei': 'Taipei',\n",
+    "    'seoul': 'Seoul',\n",
+    "    'singapore': 'Singapore',\n",
+    "    'macau': 'Macau',\n",
+    "    # Croatia\n",
+    "    'lovran': 'Lovran',\n",
+    "    'rovinj': 'Rovinj',\n",
+    "    'zagreb': 'Zagreb',\n",
+    "    'šibenik': 'Sibenik',\n",
+    "    # Norway / Scandinavia\n",
+    "    'stavanger': 'Stavanger',\n",
+    "    'trondheim': 'Trondheim',\n",
+    "    'oslo': 'Oslo',\n",
+    "    'göteborg': 'Gothenburg',\n",
+    "    'växjö': 'Vaxjo',\n",
+    "    'skåne-tranås': 'Skane-Tranas',\n",
+    "    'vejle': 'Vejle',\n",
+    "    # Denmark\n",
+    "    'fredericia': 'Fredericia',\n",
+    "    'pedersker': 'Pedersker',\n",
+    "    'præstø': 'Praesto',\n",
+    "    # Sweden\n",
+    "    'malmö': 'Malmo',\n",
+    "    'stockholm': 'Stockholm',\n",
+    "    # Portugal / Ireland / UK misc\n",
+    "    'bath': 'Bath',\n",
+    "    'bristol': 'Bristol',\n",
+    "    'cambridge': 'Cambridge',\n",
+    "    'cheltenham': 'Cheltenham',\n",
+    "    'chester': 'Chester',\n",
+    "    'birmingham': 'Birmingham',\n",
+    "    'edinburgh': 'Edinburgh',\n",
+    "    'leeds': 'Leeds',\n",
+    "    'oxford': 'Oxford',\n",
+    "    'stratford-upon-avon': 'Stratford-Upon-Avon',\n",
+    "    'padstow': 'Padstow',\n",
+    "    'torquay': 'Torquay',\n",
+    "    'newcastle upon tyne': 'Newcastle upon Tyne',\n",
+    "    'nottingham': 'Nottingham',\n",
+    "    'bray': 'Bray',\n",
+    "    'bowness-on-windermere': 'Bowness-on-Windermere',\n",
+    "    'cartmel': 'Cartmel',\n",
+    "    'castle combe': 'Castle Combe',\n",
+    "    'chagford': 'Chagford',\n",
+    "    'chew magna': 'Chew Magna',\n",
+    "    'dalry': 'Dalry',\n",
+    "    'dorking': 'Dorking',\n",
+    "    'egham': 'Egham',\n",
+    "    'fence': 'Fence',\n",
+    "    'fordwich': 'Fordwich',\n",
+    "    'grasmere': 'Grasmere',\n",
+    "    'gravetye': 'Gravetye',\n",
+    "    'great milton': 'Great Milton',\n",
+    "    'hallwang': 'Hallwang',\n",
+    "    'hampton in arden': 'Hampton in Arden',\n",
+    "    'harome': 'Harome',\n",
+    "    'henne': 'Henne',\n",
+    "    'horsham': 'Horsham',\n",
+    "    'hunstanton': 'Hunstanton',\n",
+    "    'ilfracombe': 'Ilfracombe',\n",
+    "    'järpen': 'Jarpen',\n",
+    "    'kenilworth': 'Kenilworth',\n",
+    "    'kew': 'Kew',\n",
+    "    'kleinwalsertal': 'Kleinwalsertal',\n",
+    "    'knowstone': 'Knowstone',\n",
+    "    'langho': 'Langho',\n",
+    "    'leith': 'Leith',\n",
+    "    'leynar': 'Leynar',\n",
+    "    'little dunmow': 'Little Dunmow',\n",
+    "    'llanddewi skirrid': 'Llanddewi Skirrid',\n",
+    "    'llandrillo': 'Llandrillo',\n",
+    "    'lovran': 'Lovran',\n",
+    "    'lympstone': 'Lympstone',\n",
+    "    'machynlleth': 'Machynlleth',\n",
+    "    'malmesbury': 'Malmesbury',\n",
+    "    'marlow': 'Marlow',\n",
+    "    'morston': 'Morston',\n",
+    "    'mountsorrel': 'Mountsorrel',\n",
+    "    'murcott': 'Murcott',\n",
+    "    'newbury': 'Newbury',\n",
+    "    'oldstead': 'Oldstead',\n",
+    "    'peat inn': 'Peat Inn',\n",
+    "    'penarth': 'Penarth',\n",
+    "    'port isaac': 'Port Isaac',\n",
+    "    'portscatho': 'Portscatho',\n",
+    "    'ripley': 'Ripley',\n",
+    "    'saint helier/saint-hélier': 'Saint Helier',\n",
+    "    \"saint james's\": 'Saint James',\n",
+    "    'seasalter': 'Seasalter',\n",
+    "    'shinfield': 'Shinfield',\n",
+    "    'summerhouse': 'Summerhouse',\n",
+    "    'upper hambleton': 'Hambleton',\n",
+    "    'victoria': 'Victoria',\n",
+    "    'wandsworth': 'London',\n",
+    "    'whitebrook': 'Whitebrook',\n",
+    "    'winchester': 'Winchester',\n",
+    "    'winteringham': 'Winteringham'\n",
+    "}\n",
+    "\n",
+    "stars_df['major_city'] = stars_df['city'].replace(location_map)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "d936e600-5eaf-4813-a493-029cd4447b44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              city     major_city\n",
+      "15   San Francisco  San Francisco\n",
+      "568    Los Angeles    Los Angeles\n",
+      "672      Hong Kong      Hong Kong\n",
+      "558  San Francisco  San Francisco\n",
+      "19   San Francisco  San Francisco\n",
+      "46   San Francisco  San Francisco\n",
+      "597       New York       New York\n",
+      "30   San Francisco  San Francisco\n",
+      "546       Fordwich       Fordwich\n",
+      "544      Seasalter      Seasalter\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(stars_df[['city', 'major_city']].sample(10))  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "781e121d-126d-491e-871d-526563dc4d7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method Series.unique of 306                            jin jin\n",
+       "374                        da san yuan\n",
+       "169                            yat lok\n",
+       "308    hill street tai hwa pork noodle\n",
+       "309            putien (kitchener road)\n",
+       "                    ...               \n",
+       "229                    gramercy tavern\n",
+       "228                               noda\n",
+       "225                              nomad\n",
+       "242                              agern\n",
+       "694                      gordon ramsay\n",
+       "Name: name, Length: 695, dtype: object>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df['name'].unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "7256d411-b0f0-4aec-8e92-4888ba57806c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>price_ordinal</th>\n",
+       "      <th>price_mean</th>\n",
+       "      <th>cuisine_original</th>\n",
+       "      <th>major_city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>620</th>\n",
+       "      <td>odette</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>french</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/sg/en/singapore-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>french contemporary</td>\n",
+       "      <td>Singapore</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>619</th>\n",
+       "      <td>waku ghin</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>Singapore</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/sg/en/singapore-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>japanese contemporary</td>\n",
+       "      <td>Singapore</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>614</th>\n",
+       "      <td>kojima</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Seoul</td>\n",
+       "      <td>South Korea</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/kr/en/seoul-capital...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>sushi</td>\n",
+       "      <td>Seoul</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>613</th>\n",
+       "      <td>d.o.m.</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>Sao Paulo</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/br/en/sao-paulo-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>São Paulo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>612</th>\n",
+       "      <td>tuju</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>Sao Paulo</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/br/en/sao-paulo-reg...</td>\n",
+       "      <td>2 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>creative</td>\n",
+       "      <td>São Paulo</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>229</th>\n",
+       "      <td>gramercy tavern</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>noda</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>japanese</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>nomad</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>agern</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>scandinavian</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>1 star</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>scandinavian</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>694</th>\n",
+       "      <td>gordon ramsay</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Chelsea</td>\n",
+       "      <td>United Kingdom</td>\n",
+       "      <td>french</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/gb/en/greater-londo...</td>\n",
+       "      <td>3 stars</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>french</td>\n",
+       "      <td>Chelsea</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>200 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                name  year       city          region       cuisine price  \\\n",
+       "620           odette  2018  Singapore       Singapore        french  $$$$   \n",
+       "619        waku ghin  2018  Singapore       Singapore      japanese  $$$$   \n",
+       "614           kojima  2019      Seoul     South Korea      japanese  $$$$   \n",
+       "613           d.o.m.  2019  São Paulo       Sao Paulo      creative  $$$$   \n",
+       "612             tuju  2019  São Paulo       Sao Paulo      creative  $$$$   \n",
+       "..               ...   ...        ...             ...           ...   ...   \n",
+       "229  gramercy tavern  2019   New York   New York City  contemporary  $$$$   \n",
+       "228             noda  2019   New York   New York City      japanese  $$$$   \n",
+       "225            nomad  2019   New York   New York City  contemporary  $$$$   \n",
+       "242            agern  2019   New York   New York City  scandinavian  $$$$   \n",
+       "694    gordon ramsay  2019    Chelsea  United Kingdom        french  $$$$   \n",
+       "\n",
+       "                                                   url    stars  \\\n",
+       "620  https://guide.michelin.com/sg/en/singapore-reg...  2 stars   \n",
+       "619  https://guide.michelin.com/sg/en/singapore-reg...  2 stars   \n",
+       "614  https://guide.michelin.com/kr/en/seoul-capital...  2 stars   \n",
+       "613  https://guide.michelin.com/br/en/sao-paulo-reg...  2 stars   \n",
+       "612  https://guide.michelin.com/br/en/sao-paulo-reg...  2 stars   \n",
+       "..                                                 ...      ...   \n",
+       "229  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "228  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "225  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "242  https://guide.michelin.com/us/en/new-york-stat...   1 star   \n",
+       "694  https://guide.michelin.com/gb/en/greater-londo...  3 stars   \n",
+       "\n",
+       "     price_ordinal  price_mean       cuisine_original major_city  \n",
+       "620              4       100.0    french contemporary  Singapore  \n",
+       "619              4       100.0  japanese contemporary  Singapore  \n",
+       "614              4       100.0                  sushi      Seoul  \n",
+       "613              4       100.0               creative  São Paulo  \n",
+       "612              4       100.0               creative  São Paulo  \n",
+       "..             ...         ...                    ...        ...  \n",
+       "229              4       100.0           contemporary   New York  \n",
+       "228              4       100.0               japanese   New York  \n",
+       "225              4       100.0           contemporary   New York  \n",
+       "242              4       100.0           scandinavian   New York  \n",
+       "694              4       100.0                 french    Chelsea  \n",
+       "\n",
+       "[200 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.tail(200)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "b27be073-e390-4f35-874b-f82d3b6f75f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(695, 12)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "92c35efa-5040-43ab-870b-7e196664e5cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "4bf9e732-a173-44a0-9ad1-467b769dfd7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stars_df[\"cuisine\"].value_counts().head(10).plot(kind=\"bar\")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Cuisine\")\n",
+    "plt.ylabel(\"Nº of restaurants\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "9c163a3c-a403-4bb4-a9f1-0c6a6a56abdd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "top_cuisines = (\n",
+    "    stars_df[\"cuisine\"].value_counts()\n",
+    "    .head(10)\n",
+    "    .index\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,4))\n",
+    "sns.countplot(\n",
+    "    data=stars_df[stars_df[\"cuisine\"].isin(top_cuisines)],\n",
+    "    y=\"cuisine\",\n",
+    "    order=top_cuisines\n",
+    ")\n",
+    "plt.title(\"Top 10 types of cuisine\")\n",
+    "plt.xlabel(\"Nº of restaurants\")\n",
+    "plt.ylabel(\"Cuisine\")\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb894fca-55db-4cf2-b4df-f73dda35806a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df[\"stars_n\"] = stars_df[\"stars\"].str[0].astype(int)\n",
+    "\n",
+    "stars_df.groupby(\"region\")[\"stars_n\"].mean().sort_values().plot(kind=\"bar\")\n",
+    "plt.title(\"Mean of Stars per region\")\n",
+    "plt.xlabel(\"Region\")\n",
+    "plt.ylabel(\"Mean of stars\")\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "plt.tight_layout()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3acfdf4-815b-4471-8fe9-a1eca7c437d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#visuals: \n",
+    "#which are the cities with more restaurants? \n",
+    "#which is the city / region with highest 1 star / 2 stars / 3 stars/ 4 stars ?\n",
+    "# which are the cuisine dominating a city /region \n",
+    "# avg price point in a specific city based on restaurants?\n",
+    "# time series 2018 vs 2019 any trend? any star restautant grew over past year? \n",
+    "# cheapest vs most expensive cuisine? \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "3ec45bca-a08c-475c-824e-f2422a6a70ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SEARCH: chef's table at brooklyn fare, New York -> OK\n",
+      "DETAILS: ChIJiVVSAE1awokRzdvqVP_z3aA -> OK\n",
+      "SEARCH: eleven madison park, New York -> OK\n",
+      "DETAILS: ChIJEWbXz6ZZwokRLKmKrtPfVFY -> OK\n",
+      "SEARCH: per se, New York -> OK\n",
+      "DETAILS: ChIJp3PsL_ZYwokRZYqs_40RJF4 -> OK\n"
+     ]
+    }
+   ],
+   "source": [
+    "import requests\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "API_KEY = \"AIzaSyC8VCFpZ1WhNUegfd5ziwZPVRCe1pi35lo\"\n",
+    "\n",
+    "def get_place_rating(name, city, api_key=API_KEY, sleep_sec=0.2):\n",
+    "    query = f\"{name}, {city}\"\n",
+    "    url_search = \"https://maps.googleapis.com/maps/api/place/findplacefromtext/json\"\n",
+    "    params_search = {\n",
+    "        \"input\": query,\n",
+    "        \"inputtype\": \"textquery\",\n",
+    "        \"fields\": \"place_id\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    r = requests.get(url_search, params=params_search)\n",
+    "    data = r.json()\n",
+    "    status = data.get(\"status\")\n",
+    "    print(\"SEARCH:\", query, \"->\", status)\n",
+    "    if status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    place_id = data[\"candidates\"][0][\"place_id\"]\n",
+    "\n",
+    "    url_details = \"https://maps.googleapis.com/maps/api/place/details/json\"\n",
+    "    params_details = {\n",
+    "        \"place_id\": place_id,\n",
+    "        \"fields\": \"rating,user_ratings_total\",\n",
+    "        \"key\": api_key\n",
+    "    }\n",
+    "    d = requests.get(url_details, params=params_details)\n",
+    "    det = d.json()\n",
+    "    d_status = det.get(\"status\")\n",
+    "    print(\"DETAILS:\", place_id, \"->\", d_status)\n",
+    "    if d_status != \"OK\":\n",
+    "        return None, None\n",
+    "\n",
+    "    time.sleep(sleep_sec)\n",
+    "    result = det.get(\"result\", {})\n",
+    "    return result.get(\"rating\"), result.get(\"user_ratings_total\")\n",
+    "\n",
+    "\n",
+    "# inicializar colunas (se ainda não existirem)\n",
+    "if \"Review_rating\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_rating\"] = np.nan\n",
+    "if \"Review_count\" not in stars_df.columns:\n",
+    "    stars_df[\"Review_count\"] = np.nan\n",
+    "\n",
+    "# lista de restaurantes (ou partes do nome) que queres atualizar\n",
+    "target_names = [\"eleven madison park\", \"per se\", \"chef's table at brooklyn fare\"]  # podes editar/expandir\n",
+    "\n",
+    "for idx, row in stars_df.iterrows():\n",
+    "    name_lower = str(row[\"name\"]).lower()\n",
+    "\n",
+    "    # verifica se algum dos nomes alvo aparece no name da linha\n",
+    "    if any(tn.lower() in name_lower for tn in target_names):\n",
+    "        rating, count = get_place_rating(row[\"name\"], row[\"city\"])\n",
+    "        stars_df.at[idx, \"Review_rating\"] = rating\n",
+    "        stars_df.at[idx, \"Review_count\"] = count\n",
+    "    # os outros restaurantes ficam como estão (NaN ou valores antigos)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33a9bff2-d6da-40b1-b099-8a4b2e0689f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77fb715e-886d-4644-b619-ba96e9baa884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df = stars_df.sort_values(by=\"name\", ascending=True).reset_index(drop=True)\n",
+    "stars_df['name'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d04b692-dd56-4962-bc0b-e9805b8924ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rest_names = [\"per se\", \"eleven madison park\",\"chef's table at brooklyn fare\"]\n",
+    "\n",
+    "mask = stars_df[\"name\"].str.strip().str.lower().isin(\n",
+    "    [n.strip().lower() for n in rest_names]\n",
+    ")\n",
+    "linha_restaurante = stars_df.loc[mask, :]\n",
+    "\n",
+    "print(linha_restaurante)\n",
+    "print(stars_df.loc[mask, \"Review_rating\"])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e5e741-e192-4855-9334-4e1ba9e51180",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stars_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "8e1c6981-0553-4b40-b680-c63804b95c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'price_ordinal', 'price_mean', 'cuisine_original', 'major_city',\n",
+       "       'Review_rating', 'Review_count'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stars_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a970e70-c0af-4c09-a7d0-b405e1511c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "pd.set_option(\"display.max_columns\", None)  # mostra todas as colunas\n",
+    "stars_df.head()                            # ou stars_df.sample(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f3b61ea-927d-4a74-867a-9b237c78820c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/load_and_clean_data_username.ipynb b/notebooks/load_and_clean_data_username.ipynb
deleted file mode 100644
index 792d6005..00000000
--- a/notebooks/load_and_clean_data_username.ipynb
+++ /dev/null
@@ -1 +0,0 @@
-#
diff --git a/notebooks/stars_df.csv b/notebooks/stars_df.csv
new file mode 100644
index 00000000..3986490e
--- /dev/null
+++ b/notebooks/stars_df.csv
@@ -0,0 +1,696 @@
+name,year,city,region,cuisine,price,url,stars,price_ordinal,price_mean,cuisine_original,major_city,stars_n,Review_rating,Review_count
+108,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/108,1 star,3,62.5,modern cuisine,Kobenhavn,1,,
+21212,2019,Edinburgh,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/21212,1 star,3,62.5,creative,Edinburgh,1,,
+28+,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/28,1 star,3,62.5,modern cuisine,Goteborg,1,,
+360º,2019,Dubrovnik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/dubrovnik-neretva/dubrovnik/restaurant/360%C2%BA,1 star,4,100.0,modern cuisine,Dubrovnik,1,,
+8 1/2 otto e mezzo - bombana,2019,Macau,Macau,italian,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/8-1-2-otto-e-mezzo-bombana,1 star,3,62.5,italian,Macau,1,,
+8½ otto e mezzo - bombana,2019,Hong Kong,Hong Kong,italian,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/8%C2%BD-otto-e-mezzo-bombana,3 stars,4,100.0,italian,Hong Kong,3,,
+a. wong,2019,Victoria,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/a-wong,1 star,3,62.5,chinese,Victoria,1,,
+acadia,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/acadia,2 stars,4,100.0,contemporary,Chicago,2,,
+acquerello,2019,San Francisco,California,italian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/acquerello,2 stars,4,100.0,italian,San Francisco,2,,
+adam's,2019,Birmingham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/adam-s,1 star,3,62.5,modern cuisine,Birmingham,1,,
+addison,2019,San Diego,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-san-diego/restaurant/addison,1 star,4,100.0,contemporary,San Diego,1,,
+aend,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/aend,1 star,4,100.0,modern cuisine,Wien,1,,
+agern,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/agern,1 star,4,100.0,scandinavian,New York,1,,
+agrikultur,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/agrikultur,1 star,4,100.0,modern cuisine,Stockholm,1,,
+ah yat harbour view (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ah-yat-harbour-view-tsim-sha-tsui,1 star,2,37.5,cantonese,Hong Kong,1,,
+ai fiori,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ai-fiori,1 star,4,100.0,italian,New York,1,,
+al's place,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/al-s-place,1 star,2,37.5,californian,San Francisco,1,,
+alain ducasse at morpheus,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/alain-ducasse-at-morpheus,2 stars,4,100.0,french contemporary,Macau,2,,
+alain ducasse at the dorchester,2019,Mayfair,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alain-ducasse-at-the-dorchester,3 stars,4,100.0,french,Mayfair,3,,
+aldea,2019,New York,New York City,other european,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aldea,1 star,4,100.0,mediterranean,New York,1,,
+alinea,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/alinea,3 stars,4,100.0,contemporary,Chicago,3,,
+alla prima,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/alla-prima,2 stars,3,62.5,innovative,Seoul,2,,
+alma,2018,Singapore,Singapore,other european,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/alma,1 star,1,20.0,european contemporary,Singapore,1,,
+alouette,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/alouette,1 star,3,62.5,modern cuisine,Kobenhavn,1,,
+aloë,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/aloe,1 star,4,100.0,creative,Stockholm,1,,
+alyn williams at the westbury,2019,Mayfair,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/alyn-williams-at-the-westbury,1 star,3,62.5,modern cuisine,Mayfair,1,,
+amador,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/amador,3 stars,4,100.0,creative,Wien,3,,
+amaya,2019,Belgravia,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/amaya,1 star,3,62.5,indian,Belgravia,1,,
+amber,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/amber569032,2 stars,4,100.0,french contemporary,Hong Kong,2,,
+andrew fairlie at gleneagles,2019,Auchterarder,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/perth-and-kinross/auchterarder/restaurant/andrew-fairlie-at-gleneagles,2 stars,4,100.0,creative french,Auchterarder,2,,
+angler,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/angler,1 star,4,100.0,contemporary,San Francisco,1,,
+angler,2019,Finsbury,United Kingdom,seafood,$$$,https://guide.michelin.com/gb/en/greater-london/finsbury/restaurant/angler392235,1 star,3,62.5,seafood,Finsbury,1,,
+aniar,2019,Gaillimh Galway,Ireland,creative,$$$,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/aniar,1 star,3,62.5,creative,Gaillimh Galway,1,,
+aquavit,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aquavit,2 stars,4,100.0,scandinavian,New York,2,,
+aquavit,2019,Saint James'S,United Kingdom,scandinavian,$$$,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/aquavit501082,1 star,3,62.5,scandinavian,Saint James'S,1,,
+arbor,2019,nan,Hong Kong,international cuisine,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arbor,1 star,3,62.5,innovative,nan,1,,
+arcane,2019,Hong Kong,Hong Kong,other european,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/arcane,1 star,3,62.5,european contemporary,Hong Kong,1,,
+ask,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ask,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+aska,2019,New York,New York City,scandinavian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/aska,2 stars,4,100.0,scandinavian,New York,2,,
+aster,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/aster,1 star,3,62.5,californian,San Francisco,1,,
+atelier amaro,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/atelier-amaro,1 star,4,100.0,modern cuisine,Warszawa,1,,
+atelier crenn,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/atelier-crenn,3 stars,4,100.0,contemporary,San Francisco,3,,
+atera,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atera,2 stars,4,100.0,contemporary,New York,2,,
+atomix,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/atomix,1 star,4,100.0,korean,New York,1,,
+auberge du soleil,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/auberge-du-soleil,1 star,4,100.0,californian,San Francisco,1,,
+aubergine,2019,Monterey,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/monterey/restaurant/aubergine,1 star,4,100.0,contemporary,Monterey,1,,
+a‚o‚c,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/a%E2%80%9Ao%E2%80%9Ac,2 stars,4,100.0,modern cuisine,Kobenhavn,2,,
+babbo,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/babbo,1 star,4,100.0,italian,New York,1,,
+babel,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/babel,1 star,4,100.0,modern cuisine,Budapest,1,,
+bacchanalia,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/bacchanalia,1 star,2,37.5,innovative,Singapore,1,,
+balwoo gongyang,2019,Seoul,South Korea,korean,$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/balwoo-gongyang,1 star,2,37.5,temple cuisine,Seoul,1,,
+band of bohemia,2019,Chicago,Chicago,gastropub,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/band-of-bohemia,1 star,3,62.5,gastropub,Chicago,1,,
+bar crenn,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bar-crenn,1 star,3,62.5,french,San Francisco,1,,
+bar uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bar-uchu,1 star,4,100.0,japanese,New York,1,,
+barrafina,2019,Soho,United Kingdom,spanish,$$$,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/barrafina,1 star,3,62.5,spanish,Soho,1,,
+baumé,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/baume,2 stars,4,100.0,contemporary,South San Francisco,2,,
+beefbar,2019,Hong Kong,Hong Kong,american,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/beefbar,1 star,2,37.5,steakhouse,Hong Kong,1,,
+belmond le manoir aux quat' saisons,2019,Great Milton,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/oxfordshire/great-milton/restaurant/belmond-le-manoir-aux-quat-saisons,2 stars,4,100.0,french,Great Milton,2,,
+belon,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/belon,1 star,3,62.5,french,Hong Kong,1,,
+benares,2019,Mayfair,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/benares,1 star,3,62.5,indian,Mayfair,1,,
+benu,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/benu,3 stars,4,100.0,asian,San Francisco,3,,
+bhoga,2019,Goteborg,Sweden,creative,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/bhoga,1 star,3,62.5,creative,Goteborg,1,,
+bicena,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/bicena,1 star,3,62.5,korean,Seoul,1,,
+birdsong,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/birdsong,1 star,4,100.0,american,San Francisco,1,,
+bistro na's,2019,Los Angeles,California,chinese,$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/bistro-na-s,1 star,2,37.5,chinese,Los Angeles,1,,
+black rat,2019,Winchester,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/hampshire/winchester/restaurant/black-rat,1 star,3,62.5,modern cuisine,Winchester,1,,
+black swan,2019,Oldstead,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/north-yorkshire/oldstead/restaurant/black-swan,1 star,3,62.5,modern british,Oldstead,1,,
+blackbird,2019,Newbury,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/blackbird552018,1 star,3,62.5,classic cuisine,Newbury,1,,
+blackbird,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/blackbird,1 star,3,62.5,contemporary,Chicago,1,,
+blanca,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blanca,2 stars,4,100.0,contemporary,New York,2,,
+bloom in the park,2019,Malmo,Sweden,creative,$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/bloom-in-the-park,1 star,2,37.5,creative,Malmo,1,,
+blue duck tavern,2019,"Washington, D.C.",Washington DC,american,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/blue-duck-tavern,1 star,3,62.5,american,"Washington, D.C.",1,,
+blue hill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/blue-hill,1 star,4,100.0,american,New York,1,,
+bo innovation,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/bo-innovation,3 stars,4,100.0,innovative,Hong Kong,3,,
+bo.lan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/bo-lan,1 star,4,100.0,thai,Bangkok,1,,
+bohemia,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/bohemia,1 star,3,62.5,modern cuisine,Saint Helier Saint Helier,1,,
+boka,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/boka,1 star,3,62.5,contemporary,Chicago,1,,
+borkonyha winekitchen,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/borkonyha-winekitchen,1 star,4,100.0,modern cuisine,Budapest,1,,
+botrini's,2019,Athina,Greece,other european,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/botrini-s,1 star,4,100.0,mediterranean,Athina,1,,
+bouchon,2019,San Francisco,California,french,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/bouchon,1 star,3,62.5,french,San Francisco,1,,
+bouley at home,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/bouley-at-home,1 star,3,62.5,contemporary,New York,1,,
+braci,2018,Singapore,Singapore,italian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/braci,1 star,2,37.5,italian contemporary,Singapore,1,,
+braidwoods,2019,Dalry,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/north-ayrshire/dalry/restaurant/braidwoods,1 star,3,62.5,classic cuisine,Dalry,1,,
+brat,2019,Shoreditch,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/brat,1 star,3,62.5,traditional british,Shoreditch,1,,
+bresca,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/bresca,1 star,2,37.5,contemporary,"Washington, D.C.",1,,
+bulrush,2019,Bristol,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/bulrush,1 star,3,62.5,modern british,Bristol,1,,
+burnt ends,2018,Singapore,Singapore,american,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/burnt-ends,1 star,2,37.5,barbecue,Singapore,1,,
+bybrook,2019,Castle Combe,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/wiltshire/castle-combe/restaurant/bybrook,1 star,3,62.5,modern british,Castle Combe,1,,
+bâtard,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/batard,1 star,4,100.0,contemporary,New York,1,,
+béni,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/beni,1 star,2,37.5,french contemporary,Singapore,1,,
+café boulud,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-boulud,1 star,4,100.0,french,New York,1,,
+café china,2019,New York,New York City,chinese,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cafe-china,1 star,2,37.5,chinese,New York,1,,
+californios,2019,San Francisco,California,mexican,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/californios,2 stars,4,100.0,mexican,San Francisco,2,,
+campagne,2019,Cill Chainnigh Kilkenny,Ireland,british,$$$,https://guide.michelin.com/ie/en/kilkenny/cill-chainnigh-kilkenny/restaurant/campagne,1 star,3,62.5,modern british,Cill Chainnigh Kilkenny,1,,
+campton place,2019,San Francisco,California,indian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/campton-place,2 stars,4,100.0,indian,San Francisco,2,,
+candlenut,2018,Singapore,Singapore,other asian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/candlenut,1 star,1,20.0,peranakan,Singapore,1,,
+canvas,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/canvas566292,1 star,4,100.0,innovative,Bangkok,1,,
+caprice,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/caprice,3 stars,4,100.0,french,Hong Kong,3,,
+carbone,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/carbone,1 star,4,100.0,italian,New York,1,,
+carpe diem,2019,Salzburg,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/carpe-diem,1 star,4,100.0,market cuisine,Salzburg,1,,
+carters of moseley,2019,Birmingham,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/carters-of-moseley,1 star,3,62.5,modern british,Birmingham,1,,
+casa enríque,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-enrique,1 star,2,37.5,mexican,New York,1,,
+casa mono,2019,New York,New York City,spanish,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/casa-mono,1 star,3,62.5,spanish,New York,1,,
+casamia,2019,Bristol,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/casamia,1 star,3,62.5,creative,Bristol,1,,
+caviar russe,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/caviar-russe,1 star,4,100.0,contemporary,New York,1,,
+celebrity cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/celebrity-cuisine,1 star,2,37.5,cantonese,Hong Kong,1,,
+chapter one,2019,City Centre,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/chapter-one,1 star,3,62.5,modern cuisine,City Centre,1,,
+cheek by jowl,2018,Singapore,Singapore,australian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cheek-by-jowl,1 star,1,20.0,australian,Singapore,1,,
+chef kang's,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/chef-kang-s,1 star,3,62.5,cantonese,Singapore,1,,
+chef's table at brooklyn fare,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/chef-s-table-at-brooklyn-fare,3 stars,4,100.0,contemporary,New York,3,4.2,574.0
+chestnut,2019,Ballydehob,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/cork/ballydehob/restaurant/chestnut,1 star,3,62.5,modern cuisine,Ballydehob,1,,
+chez bruce,2019,Wandsworth,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/wandsworth/restaurant/chez-bruce,1 star,3,62.5,french,Wandsworth,1,,
+chez tj,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/chez-tj,1 star,4,100.0,contemporary,South San Francisco,1,,
+chim by siam wisdom,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/chim-by-siam-wisdom,1 star,3,62.5,thai,Bangkok,1,,
+cipriani,2019,Rio De Janeiro,Rio de Janeiro,italian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/cipriani,1 star,4,100.0,italian,Rio De Janeiro,1,,
+city social,2019,City Of London,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/city-social,1 star,3,62.5,modern cuisine,City Of London,1,,
+claro,2019,New York,New York City,mexican,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/claro,1 star,2,37.5,mexican,New York,1,,
+claude bosi at bibendum,2019,Chelsea,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/claude-bosi-at-bibendum,2 stars,4,100.0,french,Chelsea,2,,
+clock house,2019,Ripley,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/surrey/ripley/restaurant/clock-house,1 star,3,62.5,modern cuisine,Ripley,1,,
+clou,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/clou,1 star,4,100.0,modern cuisine,Kobenhavn,1,,
+club gascon,2019,London,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/london/restaurant/club-gascon,1 star,3,62.5,french,London,1,,
+coi,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/coi,2 stars,4,100.0,contemporary,San Francisco,2,,
+commis,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commis,2 stars,4,100.0,contemporary,San Francisco,2,,
+commonwealth,2019,San Francisco,California,contemporary,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/commonwealth,1 star,2,37.5,contemporary,San Francisco,1,,
+contra,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/contra,1 star,4,100.0,contemporary,New York,1,,
+core by clare smyth,2019,North Kensington,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/core-by-clare-smyth,2 stars,4,100.0,modern british,North Kensington,2,,
+corner house,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/corner-house,1 star,2,37.5,innovative,Singapore,1,,
+costes,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes,1 star,4,100.0,modern cuisine,Budapest,1,,
+costes downtown,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/costes-downtown,1 star,4,100.0,modern cuisine,Budapest,1,,
+cote,2019,New York,New York City,korean,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/cote,1 star,3,62.5,korean,New York,1,,
+coworth park,2019,Ascot,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/windsor-and-maidenhead/ascot/restaurant/coworth-park,1 star,3,62.5,modern cuisine,Ascot,1,,
+credo,2019,Trondheim,Norway,creative,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/credo,1 star,4,100.0,creative,Trondheim,1,,
+crown,2019,Burchett'S Green,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/windsor-and-maidenhead/burchett-s-green/restaurant/crown442549,1 star,3,62.5,regional cuisine,Burchett'S Green,1,,
+crystal jade golden palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/crystal-jade-golden-palace,1 star,1,20.0,chinese,Singapore,1,,
+cut,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/cut559418,1 star,4,100.0,steakhouse,Los Angeles,1,,
+cut,2018,Singapore,Singapore,american,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/cut,1 star,3,62.5,steakhouse,Singapore,1,,
+céleste,2019,Belgravia,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/celeste,1 star,3,62.5,creative french,Belgravia,1,,
+d.o.m.,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/d-o-m,2 stars,4,100.0,creative,Sao Paulo,2,,
+da san yuan,2019,Taipei,Taipei,chinese,$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-san-yuan,1 star,1,20.0,cantonese,Taipei,1,,
+da-wan,2019,Taipei,Taipei,american,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/da-wan,1 star,3,62.5,barbecue,Taipei,1,,
+daniel,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/daniel,2 stars,4,100.0,french,New York,2,,
+daniel berlin,2019,Skane Tranas,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/skane-tranas/restaurant/daniel-berlin,2 stars,4,100.0,creative,Skane Tranas,2,,
+danny's steakhouse,2019,Taipei,Taipei,american,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/danny-s-steakhouse,1 star,2,37.5,steakhouse,Taipei,1,,
+das loft,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/das-loft,1 star,4,100.0,modern cuisine,Wien,1,,
+del posto,2019,New York,New York City,italian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/del-posto,1 star,4,100.0,italian,New York,1,,
+demo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/demo,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+dialogue,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/dialogue,1 star,4,100.0,contemporary,Los Angeles,1,,
+dining in space,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dining-in-space,1 star,3,62.5,french contemporary,Seoul,1,,
+dining room at the goring,2019,Victoria,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/dining-room-at-the-goring,1 star,3,62.5,traditional british,Victoria,1,,
+dinner by heston blumenthal,2019,Hyde Park,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/greater-london/hyde-park/restaurant/dinner-by-heston-blumenthal,2 stars,4,100.0,traditional british,Hyde Park,2,,
+domestic,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/domestic,1 star,3,62.5,modern cuisine,Aarhus,1,,
+dosa,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/dosa511871,1 star,3,62.5,innovative,Seoul,1,,
+draga di lovrana,2019,Lovran,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/primorje-gorski-kotar/lovran/restaurant/draga-di-lovrana,1 star,4,100.0,modern cuisine,Lovran,1,,
+driftwood,2019,Portscatho,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cornwall/portscatho/restaurant/driftwood,1 star,3,62.5,modern cuisine,Portscatho,1,,
+duddell's,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/duddell-s,1 star,3,62.5,cantonese,Hong Kong,1,,
+dusek's (board & beer),2019,Chicago,Chicago,gastropub,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/dusek-s-board-beer,1 star,2,37.5,gastropub,Chicago,1,,
+edvard,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/edvard,1 star,4,100.0,modern cuisine,Wien,1,,
+eipic,2019,Belfast,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/eipic,1 star,3,62.5,modern cuisine,Belfast,1,,
+ekstedt,2019,Stockholm,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/ekstedt,1 star,3,62.5,meats and grills,Stockholm,1,,
+el ideas,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/el-ideas,1 star,4,100.0,contemporary,Chicago,1,,
+elements,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/elements,1 star,4,100.0,french contemporary,Bangkok,1,,
+elephant,2019,Torquay,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/torbay/torquay/restaurant/elephant,1 star,3,62.5,modern british,Torquay,1,,
+eleven madison park,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/eleven-madison-park,3 stars,4,100.0,contemporary,New York,3,4.4,3149.0
+elizabeth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elizabeth,1 star,4,100.0,contemporary,Chicago,1,,
+elske,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/elske,1 star,4,100.0,contemporary,Chicago,1,,
+elystan street,2019,Chelsea,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/elystan-street,1 star,3,62.5,modern british,Chelsea,1,,
+entente,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/entente,1 star,3,62.5,contemporary,Chicago,1,,
+era ora,2019,Kobenhavn,Denmark,italian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/era-ora,1 star,2,37.5,italian,Kobenhavn,1,,
+esszimmer,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/esszimmer,1 star,4,100.0,creative,Salzburg,1,,
+everest,2019,Chicago,Chicago,french,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/everest,1 star,4,100.0,french,Chicago,1,,
+evvai,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/evvai,1 star,4,100.0,modern,Sao Paulo,1,,
+exquisine,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/exquisine,1 star,3,62.5,innovative,Seoul,1,,
+fagn,2019,Trondheim,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/trondelag/trondheim/restaurant/fagn,1 star,4,100.0,modern cuisine,Trondheim,1,,
+farmhouse inn & restaurant,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/farmhouse-inn-restaurant,1 star,4,100.0,californian,San Francisco,1,,
+faro,2019,New York,New York City,american,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/faro,1 star,2,37.5,american,New York,1,,
+fat duck,2019,Bray,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/fat-duck,3 stars,4,100.0,creative,Bray,3,,
+feng wei ju,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/feng-wei-ju,2 stars,1,20.0,hunanese and sichuan,Macau,2,,
+field,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/field,1 star,4,100.0,modern cuisine,Praha,1,,
+fiola,2019,"Washington, D.C.",Washington DC,italian,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/fiola,1 star,4,100.0,italian,"Washington, D.C.",1,,
+fischer's at baslow hall,2019,Baslow,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/derbyshire/baslow/restaurant/fischer-s-at-baslow-hall,1 star,3,62.5,modern cuisine,Baslow,1,,
+five fields,2019,Chelsea,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/five-fields,1 star,3,62.5,modern cuisine,Chelsea,1,,
+fordwich arms,2019,Fordwich,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/kent/fordwich/restaurant/fordwich-arms,1 star,3,62.5,modern cuisine,Fordwich,1,,
+forest side,2019,Grasmere,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/cumbria/grasmere/restaurant/forest-side,1 star,3,62.5,modern british,Grasmere,1,,
+formel b,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/formel-b,1 star,2,37.5,modern cuisine,Kobenhavn,1,,
+forum,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/forum,2 stars,2,37.5,cantonese,Hong Kong,2,,
+fraiche,2019,Birkenhead,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/merseyside/birkenhead/restaurant/fraiche,1 star,3,62.5,creative,Birkenhead,1,,
+frantzén,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/frantzen,3 stars,4,100.0,modern cuisine,Stockholm,3,,
+frederikshøj,2019,Aarhus,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/frederikshoj,1 star,4,100.0,creative,Aarhus,1,,
+frederiksminde,2019,Praesto,Denmark,creative,$$$,https://guide.michelin.com/dk/en/zealand/praesto/restaurant/frederiksminde,1 star,3,62.5,creative,Praesto,1,,
+fu ho (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/fu-ho-tsim-sha-tsui,1 star,2,37.5,cantonese,Hong Kong,1,,
+fäviken magasinet,2019,Jarpen,Sweden,creative,$$$$,https://guide.michelin.com/se/en/jamtland/jarpen/restaurant/faviken-magasinet,2 stars,4,100.0,creative,Jarpen,2,,
+gaa,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaa,1 star,4,100.0,innovative,Bangkok,1,,
+gabriel kreuther,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gabriel-kreuther,2 stars,4,100.0,contemporary,New York,2,,
+gaggan,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/gaggan,2 stars,4,100.0,innovative,Bangkok,2,,
+galt,2019,Oslo,Norway,international cuisine,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/galt,1 star,3,62.5,modern cuisine,Oslo,1,,
+galvin at windows,2019,Mayfair,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/galvin-at-windows,1 star,3,62.5,modern cuisine,Mayfair,1,,
+galvin la chapelle,2019,Spitalfields,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/spitalfields/restaurant/galvin-la-chapelle,1 star,3,62.5,french,Spitalfields,1,,
+gaon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gaon,3 stars,4,100.0,korean,Seoul,3,,
+garibaldi,2018,Singapore,Singapore,italian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/garibaldi,1 star,1,20.0,italian,Singapore,1,,
+gary danko,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/gary-danko,1 star,4,100.0,contemporary,San Francisco,1,,
+gastrologik,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/gastrologik,2 stars,4,100.0,creative,Stockholm,2,,
+gastromé,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/gastrome,1 star,3,62.5,modern cuisine,Aarhus,1,,
+geranium,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/geranium,3 stars,4,100.0,creative,Kobenhavn,3,,
+gidleigh park,2019,Chagford,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/devon/chagford/restaurant/gidleigh-park,1 star,3,62.5,modern cuisine,Chagford,1,,
+ginza sushi ichi,2019,Bangkok,Thailand,japanese,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ginza-sushi-ichi,1 star,4,100.0,sushi,Bangkok,1,,
+golden flower,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/golden-flower,2 stars,2,37.5,chinese,Macau,2,,
+golden formosa,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/golden-formosa,1 star,2,37.5,taiwanese,Taipei,1,,
+goosefoot,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/goosefoot,1 star,4,100.0,contemporary,Chicago,1,,
+gordon ramsay,2019,Chelsea,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/chelsea/restaurant/gordon-ramsay,3 stars,4,100.0,french,Chelsea,3,,
+gotgan,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/gotgan,1 star,4,100.0,korean,Seoul,1,,
+gotham bar and grill,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gotham-bar-and-grill,1 star,4,100.0,american,New York,1,,
+gramercy tavern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/gramercy-tavern,1 star,4,100.0,contemporary,New York,1,,
+gravetye manor,2019,Gravetye,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-sussex/gravetye/restaurant/gravetye-manor,1 star,3,62.5,modern british,Gravetye,1,,
+greenhouse,2019,Mayfair,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/greenhouse69393,2 stars,4,100.0,creative,Mayfair,2,,
+greenhouse,2019,City Centre,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/greenhouse,1 star,3,62.5,modern cuisine,City Centre,1,,
+grön,2019,Helsingfors   Helsinki,Finland,finnish,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/gron,1 star,3,62.5,finnish,Helsingfors   Helsinki,1,,
+guo fu lou,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/guo-fu-lou567828,1 star,3,62.5,cantonese,Hong Kong,1,,
+gymkhana,2019,Mayfair,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/gymkhana,1 star,3,62.5,indian,Mayfair,1,,
+hakkasan hanway place,2019,Bloomsbury,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/hakkasan-hanway-place,1 star,3,62.5,chinese,Bloomsbury,1,,
+hakkasan mayfair,2019,Mayfair,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hakkasan-mayfair,1 star,3,62.5,chinese,Mayfair,1,,
+hambleton hall,2019,Upper Hambleton,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/rutland/upper-hambleton/restaurant/hambleton-hall,1 star,3,62.5,classic cuisine,Upper Hambleton,1,,
+hana re,2019,Costa Mesa,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/hana-re,1 star,4,100.0,japanese,Costa Mesa,1,,
+hand and flowers,2019,Marlow,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/hand-and-flowers,2 stars,4,100.0,modern british,Marlow,2,,
+hansikgonggan,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/hansikgonggan,1 star,3,62.5,korean,Seoul,1,,
+harbor house,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/harbor-house,1 star,4,100.0,californian,San Francisco,1,,
+harwood arms,2019,Fulham,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/fulham/restaurant/harwood-arms,1 star,3,62.5,modern british,Fulham,1,,
+hashiri,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/hashiri,1 star,4,100.0,japanese,San Francisco,1,,
+hayato,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/hayato,1 star,4,100.0,japanese,Los Angeles,1,,
+henne kirkeby kro,2019,Henne,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/henne/restaurant/henne-kirkeby-kro,2 stars,3,62.5,classic cuisine,Henne,2,,
+heron & grey,2019,Blackrock,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dun-laoghaire-rathdown/blackrock/restaurant/heron-grey,1 star,3,62.5,modern cuisine,Blackrock,1,,
+hide,2019,Mayfair,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/hide,1 star,3,62.5,modern british,Mayfair,1,,
+hill street tai hwa pork noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/hill-street-tai-hwa-pork-noodle,1 star,1,20.0,street food,Singapore,1,,
+hinds head,2019,Bray,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/hinds-head,1 star,3,62.5,traditional british,Bray,1,,
+hirohisa,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/hirohisa,1 star,3,62.5,japanese,New York,1,,
+ho hung kee,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ho-hung-kee,1 star,1,20.0,noodles and congee,Hong Kong,1,,
+house,2019,Aird Mhor Ardmore,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/waterford/aird-mhor-ardmore/restaurant/house,1 star,3,62.5,modern cuisine,Aird Mhor Ardmore,1,,
+house of tides,2019,Newcastle Upon Tyne,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/tyne-and-wear/newcastle-upon-tyne/restaurant/house-of-tides,1 star,3,62.5,modern cuisine,Newcastle Upon Tyne,1,,
+hrishi,2019,Bowness On Windermere,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cumbria/bowness-on-windermere/restaurant/hrishi,1 star,3,62.5,modern cuisine,Bowness On Windermere,1,,
+huto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/huto,1 star,4,100.0,japanese,Sao Paulo,1,,
+hytra,2019,Athina,Greece,international cuisine,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/hytra,1 star,4,100.0,modern cuisine,Athina,1,,
+hélène darroze at the connaught,2019,Mayfair,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/helene-darroze-at-the-connaught,2 stars,4,100.0,modern cuisine,Mayfair,2,,
+ichigo ichie,2019,Corcaigh Cork,Ireland,japanese,$$$,https://guide.michelin.com/ie/en/cork/corcaigh-cork/restaurant/ichigo-ichie,1 star,3,62.5,japanese,Corcaigh Cork,1,,
+ichimura at uchū,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ichimura-at-uchu,2 stars,4,100.0,japanese,New York,2,,
+iggy's,2018,Singapore,Singapore,other european,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/iggy-s,1 star,3,62.5,european contemporary,Singapore,1,,
+ikarus,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/ikarus,2 stars,4,100.0,creative,Salzburg,2,,
+ikoyi,2019,Saint James'S,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/ikoyi,1 star,3,62.5,creative,Saint James'S,1,,
+im teppanyaki & wine,2019,Hong Kong,Hong Kong,japanese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/im-teppanyaki-wine,1 star,2,37.5,teppanyaki,Hong Kong,1,,
+imperial treasure fine chinese cuisine,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/imperial-treasure-fine-chinese-cuisine,1 star,2,37.5,cantonese,Hong Kong,1,,
+imperial treasure fine teochew cuisine (orchard),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/imperial-treasure-fine-teochew-cuisine-orchard,1 star,1,20.0,chinese,Singapore,1,,
+impromptu by paul lee,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/impromptu-by-paul-lee,1 star,3,62.5,innovative,Taipei,1,,
+in situ,2019,San Francisco,California,international cuisine,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/in-situ,1 star,4,100.0,international,San Francisco,1,,
+j'aime by jean-michel lorain,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/j-aime-by-jean-michel-lorain,1 star,3,62.5,french contemporary,Bangkok,1,,
+jaan,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jaan,1 star,3,62.5,french contemporary,Singapore,1,,
+jade dragon,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/jade-dragon,3 stars,2,37.5,cantonese,Macau,3,,
+james sommerin,2019,Penarth,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/the-vale-of-glamorgan/penarth/restaurant/james-sommerin,1 star,3,62.5,modern cuisine,Penarth,1,,
+jardin de jade (wan chai),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/jardin-de-jade-wan-chai,1 star,2,37.5,shanghainese,Hong Kong,1,,
+jay fai,2019,Bangkok,Thailand,international cuisine,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/jay-fai,1 star,2,37.5,street food,Bangkok,1,,
+jean-georges,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jean-georges,2 stars,4,100.0,contemporary,New York,2,,
+jeju noodle bar,2019,New York,New York City,korean,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jeju-noodle-bar,1 star,2,37.5,korean,New York,1,,
+jewel bako,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jewel-bako,1 star,3,62.5,japanese,New York,1,,
+jiang-nan chun,2018,Singapore,Singapore,chinese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/jiang-nan-chun,1 star,3,62.5,cantonese,Singapore,1,,
+jin jin,2019,Seoul,South Korea,chinese,$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jin-jin,1 star,1,20.0,chinese,Seoul,1,,
+john's house,2019,Mountsorrel,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/leicestershire/mountsorrel/restaurant/john-s-house,1 star,3,62.5,modern cuisine,Mountsorrel,1,,
+joo ok,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/joo-ok,1 star,3,62.5,korean contemporary,Seoul,1,,
+jordnær,2019,Kobenhavn,Denmark,danish,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/jordnaer,1 star,3,62.5,danish,Kobenhavn,1,,
+jun sakamoto,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/jun-sakamoto,1 star,4,100.0,japanese,Sao Paulo,1,,
+jungsik,2019,New York,New York City,korean,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/jungsik,2 stars,4,100.0,korean,New York,2,,
+jungsik,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/jungsik511965,2 stars,3,62.5,korean contemporary,Seoul,2,,
+junoon,2019,New York,New York City,indian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/junoon,1 star,4,100.0,indian,New York,1,,
+jū-ni,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/ju-ni,1 star,4,100.0,japanese,San Francisco,1,,
+kadeau bornholm,2019,Pedersker,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/pedersker/restaurant/kadeau-bornholm,1 star,4,100.0,creative,Pedersker,1,,
+kadeau copenhagen,2019,Kobenhavn,Denmark,international cuisine,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kadeau-copenhagen,2 stars,4,100.0,modern cuisine,Kobenhavn,2,,
+kai,2019,Mayfair,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/kai123638,1 star,3,62.5,chinese,Mayfair,1,,
+kaiseki den by saotome,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kaiseki-den-by-saotome,1 star,4,100.0,japanese,Hong Kong,1,,
+kajitsu,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kajitsu,1 star,3,62.5,japanese,New York,1,,
+kali,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kali,1 star,3,62.5,californian,Los Angeles,1,,
+kam's roast goose,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kam-s-roast-goose,1 star,1,20.0,cantonese roast meats,Hong Kong,1,,
+kan suke,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kan-suke,1 star,4,100.0,japanese,Sao Paulo,1,,
+kanoyama,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kanoyama,1 star,3,62.5,japanese,New York,1,,
+kashiwaya,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/kashiwaya,2 stars,4,100.0,japanese,Hong Kong,2,,
+kato,2019,Los Angeles,California,other asian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/kato,1 star,3,62.5,asian,Los Angeles,1,,
+keiko à nob hill,2019,San Francisco,California,other asian,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/keiko-a-nob-hill,1 star,4,100.0,fusion,San Francisco,1,,
+ken an ho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ken-an-ho,1 star,4,100.0,japanese,Taipei,1,,
+kenzo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kenzo,1 star,4,100.0,japanese,San Francisco,1,,
+kiin kiin,2019,Kobenhavn,Denmark,other asian,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kiin-kiin,1 star,2,37.5,thai,Kobenhavn,1,,
+kilian stuba,2019,Kleinwalsertal,Austria,creative,$$$$,https://guide.michelin.com/at/en/vorarlberg/kleinwalsertal/restaurant/kilian-stuba,1 star,4,100.0,creative,Kleinwalsertal,1,,
+kin khao,2019,San Francisco,California,other asian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kin-khao,1 star,2,37.5,thai,San Francisco,1,,
+king,2019,Macau,Macau,chinese,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/king380226,1 star,1,20.0,cantonese,Macau,1,,
+kinjo,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/kinjo,1 star,4,100.0,japanese,San Francisco,1,,
+kinoshita,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kinoshita,1 star,4,100.0,japanese,Sao Paulo,1,,
+kinship,2019,"Washington, D.C.",Washington DC,contemporary,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/kinship,1 star,3,62.5,contemporary,"Washington, D.C.",1,,
+kitchen table at bubbledogs,2019,Bloomsbury,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/kitchen-table-at-bubbledogs,2 stars,4,100.0,modern cuisine,Bloomsbury,2,,
+kitchen w8,2019,Kensington,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/kensington/restaurant/kitchen-w8,1 star,3,62.5,modern cuisine,Kensington,1,,
+kitchin,2019,Leith,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/kitchin,1 star,3,62.5,modern cuisine,Leith,1,,
+kitcho,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/kitcho,1 star,4,100.0,sushi,Taipei,1,,
+ko,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/ko,2 stars,4,100.0,contemporary,New York,2,,
+kojima,2019,Seoul,South Korea,japanese,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kojima,2 stars,4,100.0,sushi,Seoul,2,,
+koka,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/koka,1 star,3,62.5,modern cuisine,Goteborg,1,,
+kokkeriet,2019,Kobenhavn,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kokkeriet,1 star,3,62.5,modern cuisine,Kobenhavn,1,,
+koks,2019,Leynar,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/faroe-islands/leynar/restaurant/koks558780,2 stars,4,100.0,creative,Leynar,2,,
+komi,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/komi,1 star,4,100.0,mediterranean,"Washington, D.C.",1,,
+kong hans kælder,2019,Kobenhavn,Denmark,french,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/kong-hans-kaelder,1 star,4,100.0,classic french,Kobenhavn,1,,
+konstantin filippou,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/konstantin-filippou,2 stars,4,100.0,modern cuisine,Wien,2,,
+kontrast,2019,Oslo,Norway,scandinavian,$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/kontrast,1 star,3,62.5,scandinavian,Oslo,1,,
+kosaka,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kosaka,1 star,4,100.0,japanese,New York,1,,
+kosushi,2019,Sao Paulo,Sao Paulo,japanese,$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/kosushi,1 star,3,62.5,japanese,Sao Paulo,1,,
+kwonsooksoo,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/kwonsooksoo,2 stars,3,62.5,korean,Seoul,2,,
+kyo ya,2019,New York,New York City,japanese,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/kyo-ya,1 star,3,62.5,japanese,New York,1,,
+l'amitié,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/l-amitie,1 star,3,62.5,french,Seoul,1,,
+l'appart,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-appart,1 star,4,100.0,french,New York,1,,
+l'atelier de joël robuchon,2019,Taipei,Taipei,french,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/l-atelier-de-joel-robuchon550759,1 star,3,62.5,french contemporary,Taipei,1,,
+l'atelier de joël robuchon,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/l-atelier-de-joel-robuchon,3 stars,4,100.0,french contemporary,Hong Kong,3,,
+l'atelier de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/l-atelier-de-joel-robuchon562505,2 stars,4,100.0,french,New York,2,,
+l'ecrivain,2019,City Centre,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/l-ecrivain,1 star,3,62.5,modern cuisine,City Centre,1,,
+l'enclume,2019,Cartmel,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/l-enclume,2 stars,4,100.0,creative,Cartmel,2,,
+l'ortolan,2019,Shinfield,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/wokingham/shinfield/restaurant/l-ortolan,1 star,3,62.5,french,Shinfield,1,,
+la dame de pic,2019,City Of London,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/city-of-london/restaurant/la-dame-de-pic,1 star,3,62.5,modern french,City Of London,1,,
+la degustation bohême bourgeoise,2019,Praha,Czech Republic,international cuisine,$$$$,https://guide.michelin.com/cz/en/prague/praha/restaurant/la-degustation-boheme-bourgeoise,1 star,4,100.0,modern cuisine,Praha,1,,
+la toque,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/la-toque,1 star,4,100.0,contemporary,San Francisco,1,,
+la trompette,2019,Chiswick,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/chiswick/restaurant/la-trompette,1 star,3,62.5,modern british,Chiswick,1,,
+la yeon,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/la-yeon,3 stars,4,100.0,korean,Seoul,3,,
+labyrinth,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/labyrinth,1 star,2,37.5,innovative,Singapore,1,,
+lady helen,2019,Baile Mhic Andain Thomastown,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/kilkenny/baile-mhic-andain-thomastown/restaurant/lady-helen,1 star,3,62.5,modern cuisine,Baile Mhic Andain Thomastown,1,,
+lai heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/lai-heen,1 star,2,37.5,cantonese,Macau,1,,
+lasai,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/lasai,1 star,4,100.0,modern,Rio De Janeiro,1,,
+lazy bear,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lazy-bear,2 stars,4,100.0,contemporary,San Francisco,2,,
+le bernardin,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-bernardin,3 stars,4,100.0,seafood,New York,3,,
+le champignon sauvage,2019,Cheltenham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/gloucestershire/cheltenham/restaurant/le-champignon-sauvage,1 star,3,62.5,modern cuisine,Cheltenham,1,,
+le ciel by toni mörwald,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/le-ciel-by-toni-morwald,1 star,4,100.0,classic cuisine,Wien,1,,
+le comptoir,2019,Los Angeles,California,american,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/le-comptoir,1 star,4,100.0,californian,Los Angeles,1,,
+le coucou,2019,New York,New York City,french,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-coucou,1 star,3,62.5,french,New York,1,,
+le du,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-du,1 star,4,100.0,thai,Bangkok,1,,
+le gavroche,2019,Mayfair,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/le-gavroche,2 stars,4,100.0,french,Mayfair,2,,
+le grill de joël robuchon,2019,New York,New York City,french,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/le-grill-de-joel-robuchon,1 star,4,100.0,french,New York,1,,
+le normandie,2019,Bangkok,Thailand,french,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/le-normandie,2 stars,4,100.0,french contemporary,Bangkok,2,,
+le palais,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/le-palais,3 stars,2,37.5,cantonese,Taipei,3,,
+ledbury,2019,North Kensington,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/greater-london/north-kensington/restaurant/ledbury,2 stars,4,100.0,modern cuisine,North Kensington,2,,
+lee jong kuk 104,2019,Seoul,South Korea,korean,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/lee-jong-kuk-104,1 star,4,100.0,korean,Seoul,1,,
+lei garden,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/lei-garden501509,1 star,1,20.0,cantonese,Singapore,1,,
+lei garden (kwun tong),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-kwun-tong,1 star,1,20.0,cantonese,Hong Kong,1,,
+lei garden (mong kok),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lei-garden-mong-kok,1 star,1,20.0,cantonese,Hong Kong,1,,
+leroy,2019,Shoreditch,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/leroy,1 star,3,62.5,modern british,Shoreditch,1,,
+les amis,2018,Singapore,Singapore,french,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/les-amis,2 stars,3,62.5,french,Singapore,2,,
+liao fan hong kong soya sauce chicken rice & noodle,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/liao-fan-hong-kong-soya-sauce-chicken-rice-noodle,1 star,1,20.0,street food,Singapore,1,,
+loaf on,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/loaf-on,1 star,2,37.5,cantonese,Hong Kong,1,,
+loam,2019,Gaillimh Galway,Ireland,creative,$$$,https://guide.michelin.com/ie/en/galway/gaillimh-galway/restaurant/loam,1 star,3,62.5,creative,Gaillimh Galway,1,,
+locanda locatelli,2019,Marylebone,United Kingdom,italian,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/locanda-locatelli,1 star,3,62.5,italian,Marylebone,1,,
+loch bay,2019,Waternish,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/highland/waternish/restaurant/loch-bay,1 star,3,62.5,modern cuisine,Waternish,1,,
+logy,2019,Taipei,Taipei,other asian,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/logy,1 star,4,100.0,asian contemporary,Taipei,1,,
+longtail,2019,Taipei,Taipei,international cuisine,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/longtail,1 star,2,37.5,innovative,Taipei,1,,
+lord stanley,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/lord-stanley,1 star,3,62.5,californian,San Francisco,1,,
+luce,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/luce,1 star,3,62.5,contemporary,San Francisco,1,,
+lung king heen,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/lung-king-heen,3 stars,4,100.0,cantonese,Hong Kong,3,,
+lyle's,2019,Shoreditch,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/lyle-s,1 star,3,62.5,modern british,Shoreditch,1,,
+lympstone manor,2019,Lympstone,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/devon/lympstone/restaurant/lympstone-manor,1 star,3,62.5,modern cuisine,Lympstone,1,,
+ma cuisine,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/ma-cuisine,1 star,1,20.0,french,Singapore,1,,
+maaemo,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/maaemo,3 stars,4,100.0,modern cuisine,Oslo,3,,
+madcap,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madcap,1 star,4,100.0,contemporary,San Francisco,1,,
+madera,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madera,1 star,3,62.5,contemporary,San Francisco,1,,
+madrona manor,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/madrona-manor,1 star,4,100.0,contemporary,San Francisco,1,,
+man wah,2019,Hong Kong,Hong Kong,chinese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/man-wah,1 star,4,100.0,cantonese,Hong Kong,1,,
+mandarin grill + bar,2019,Hong Kong,Hong Kong,other european,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/mandarin-grill-bar,1 star,4,100.0,european contemporary,Hong Kong,1,,
+manresa,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/manresa,3 stars,4,100.0,contemporary,South San Francisco,3,,
+maní,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/mani,1 star,4,100.0,creative,Sao Paulo,1,,
+marchal,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/marchal,1 star,2,37.5,modern cuisine,Kobenhavn,1,,
+marcus,2019,Belgravia,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/marcus,1 star,3,62.5,modern cuisine,Belgravia,1,,
+marea,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/marea,2 stars,4,100.0,seafood,New York,2,,
+martin wishart,2019,Leith,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/leith/restaurant/martin-wishart,1 star,3,62.5,modern cuisine,Leith,1,,
+masa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/masa,3 stars,4,100.0,japanese,New York,3,,
+masons arms,2019,Knowstone,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/devon/knowstone/restaurant/masons-arms,1 star,3,62.5,classic french,Knowstone,1,,
+masseria,2019,"Washington, D.C.",Washington DC,italian,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/masseria,1 star,3,62.5,italian,"Washington, D.C.",1,,
+mathias dahlgren-matbaren,2019,Stockholm,Sweden,international cuisine,$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/mathias-dahlgren-matbaren,1 star,2,37.5,modern cuisine,Stockholm,1,,
+matt worswick at the latymer,2019,Bagshot,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/surrey/bagshot/restaurant/matt-worswick-at-the-latymer,1 star,3,62.5,modern cuisine,Bagshot,1,,
+maude,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/maude,1 star,4,100.0,contemporary,Los Angeles,1,,
+maum,2019,South San Francisco,California,korean,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/maum,1 star,4,100.0,korean,South San Francisco,1,,
+meadowsweet,2019,New York,New York City,other european,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/meadowsweet,1 star,3,62.5,mediterranean,New York,1,,
+mee,2019,Rio De Janeiro,Rio de Janeiro,other asian,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/mee,1 star,4,100.0,asian influences,Rio De Janeiro,1,,
+meta,2018,Singapore,Singapore,international cuisine,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/meta,1 star,2,37.5,innovative,Singapore,1,,
+methavalai sorndaeng,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/methavalai-sorndaeng,1 star,2,37.5,thai,Bangkok,1,,
+mews,2019,Baltimore,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/cork/ie-baltimore/restaurant/mews,1 star,3,62.5,modern cuisine,Baltimore,1,,
+mezzaluna,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/mezzaluna,2 stars,4,100.0,innovative,Bangkok,2,,
+me‚mu,2019,Vejle,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/vejle/restaurant/me%E2%80%9Amu,1 star,3,62.5,modern cuisine,Vejle,1,,
+michael mina,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/michael-mina,1 star,4,100.0,contemporary,San Francisco,1,,
+midsummer house,2019,Cambridge,United Kingdom,international cuisine,$$$$,https://guide.michelin.com/gb/en/cambridgeshire/cambridge/restaurant/midsummer-house,2 stars,4,100.0,modern cuisine,Cambridge,2,,
+ming court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ming-court,1 star,3,62.5,cantonese,Hong Kong,1,,
+ming fu,2019,Taipei,Taipei,other asian,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ming-fu,1 star,3,62.5,taiwanese,Taipei,1,,
+mingles,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mingles,2 stars,3,62.5,korean contemporary,Seoul,2,,
+minibar,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/minibar,2 stars,4,100.0,contemporary,"Washington, D.C.",2,,
+mister jiu's,2019,San Francisco,California,chinese,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mister-jius,1 star,3,62.5,chinese,San Francisco,1,,
+mizumi (macau),2019,Macau,Macau,japanese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/mizumi-macau,2 stars,2,37.5,japanese,Macau,2,,
+monte,2019,Rovinj,Croatia,creative,$$$$,https://guide.michelin.com/hr/en/istria/rovinj/restaurant/monte,1 star,4,100.0,creative,Rovinj,1,,
+moor hall,2019,Aughton,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/lancashire/aughton/restaurant/moor-hall,2 stars,4,100.0,modern british,Aughton,2,,
+mori sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/mori-sushi,1 star,4,100.0,japanese,Los Angeles,1,,
+morston hall,2019,Morston,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/norfolk/morston/restaurant/morston-hall,1 star,3,62.5,modern british,Morston,1,,
+mosu,2019,Seoul,South Korea,international cuisine,$$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/mosu,1 star,4,100.0,innovative,Seoul,1,,
+mountain and sea house,2019,Taipei,Taipei,other asian,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mountain-and-sea-house,1 star,2,37.5,taiwanese,Taipei,1,,
+mourad,2019,San Francisco,California,moroccan,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/mourad,1 star,3,62.5,moroccan,San Francisco,1,,
+mraz & sohn,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/mraz-sohn,2 stars,4,100.0,creative,Wien,2,,
+mume,2019,Taipei,Taipei,other european,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/mume,1 star,3,62.5,european contemporary,Taipei,1,,
+muoki,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/muoki,1 star,3,62.5,innovative,Seoul,1,,
+murano,2019,Mayfair,United Kingdom,italian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/murano,1 star,3,62.5,italian,Mayfair,1,,
+métier,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/metier,1 star,4,100.0,contemporary,"Washington, D.C.",1,,
+n/naka,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/n-naka,2 stars,4,100.0,contemporary,Los Angeles,2,,
+nahm,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/nahm,1 star,3,62.5,thai,Bangkok,1,,
+new punjab club,2019,Hong Kong,Hong Kong,indian,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/new-punjab-club,1 star,2,37.5,indian,Hong Kong,1,,
+nico,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/nico,1 star,3,62.5,contemporary,San Francisco,1,,
+nix,2019,New York,New York City,vegetarian,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nix,1 star,3,62.5,vegetarian,New York,1,,
+noda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/noda,1 star,4,100.0,japanese,New York,1,,
+noel,2019,Zagreb,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/zagreb-region/zagreb/restaurant/noel,1 star,4,100.0,modern cuisine,Zagreb,1,,
+noma,2019,Kobenhavn,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/noma,2 stars,4,100.0,creative,Kobenhavn,2,,
+nomad,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/nomad,1 star,4,100.0,contemporary,New York,1,,
+north pond,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/north-pond,1 star,3,62.5,contemporary,Chicago,1,,
+northcote,2019,Langho,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/lancashire/langho/restaurant/northcote,1 star,3,62.5,modern british,Langho,1,,
+nouri,2018,Singapore,Singapore,international cuisine,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/nouri,1 star,1,20.0,innovative,Singapore,1,,
+nozawa bar,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/nozawa-bar,1 star,4,100.0,japanese,Los Angeles,1,,
+number one,2019,Edinburgh,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/city-of-edinburgh/edinburgh/restaurant/number-one,1 star,3,62.5,modern cuisine,Edinburgh,1,,
+nut tree,2019,Murcott,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/oxfordshire/murcott/restaurant/nut-tree,1 star,3,62.5,traditional british,Murcott,1,,
+oaxen krog,2019,Stockholm,Sweden,creative,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/oaxen-krog,2 stars,4,100.0,creative,Stockholm,2,,
+octavia,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/octavia,1 star,3,62.5,californian,San Francisco,1,,
+octavium,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/octavium,1 star,3,62.5,italian,Hong Kong,1,,
+odette,2018,Singapore,Singapore,french,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/odette,2 stars,4,100.0,french contemporary,Singapore,2,,
+okuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/okuda,1 star,4,100.0,japanese,New York,1,,
+olive tree,2019,Bath,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/bath/restaurant/olive-tree,1 star,3,62.5,modern cuisine,Bath,1,,
+olo,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/olo,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+olympe,2019,Rio De Janeiro,Rio de Janeiro,french,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/olympe,1 star,4,100.0,french,Rio De Janeiro,1,,
+omakase,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/omakase,1 star,4,100.0,japanese,San Francisco,1,,
+onyx,2019,Budapest,Hungary,international cuisine,$$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/onyx,2 stars,4,100.0,modern cuisine,Budapest,2,,
+operakällaren,2019,Stockholm,Sweden,international cuisine,$$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/operakallaren,1 star,4,100.0,classic cuisine,Stockholm,1,,
+ora,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/ora,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+oriole,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/oriole,2 stars,4,100.0,contemporary,Chicago,2,,
+oro,2019,Rio De Janeiro,Rio de Janeiro,creative,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oro,2 stars,4,100.0,creative,Rio De Janeiro,2,,
+orsa & winston,2019,Los Angeles,California,other asian,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/orsa-winston,1 star,4,100.0,fusion,Los Angeles,1,,
+osteria mozza,2019,Los Angeles,California,italian,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/osteria-mozza,1 star,3,62.5,italian,Los Angeles,1,,
+oteque,2019,Rio De Janeiro,Rio de Janeiro,international cuisine,$$$$,https://guide.michelin.com/br/en/rio-de-janeiro-region/rio-de-janeiro/restaurant/oteque,1 star,4,100.0,modern,Rio De Janeiro,1,,
+outlaw's fish kitchen,2019,Port Isaac,United Kingdom,seafood,$$$,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/outlaw-s-fish-kitchen,1 star,3,62.5,seafood,Port Isaac,1,,
+ox,2019,Belfast,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/belfast/belfast/restaurant/ox399109,1 star,3,62.5,modern british,Belfast,1,,
+oxford kitchen,2019,Oxford,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/oxfordshire/oxford/restaurant/oxford-kitchen,1 star,3,62.5,modern british,Oxford,1,,
+oxomoco,2019,New York,New York City,mexican,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/oxomoco,1 star,3,62.5,mexican,New York,1,,
+paco tapas,2019,Bristol,United Kingdom,spanish,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/paco-tapas,1 star,3,62.5,spanish,Bristol,1,,
+palace,2019,Helsingfors   Helsinki,Finland,international cuisine,$$$,https://guide.michelin.com/fi/en/uusimaa/helsingfors-helsinki/restaurant/palace,1 star,3,62.5,modern cuisine,Helsingfors   Helsinki,1,,
+pang's kitchen,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pang-s-kitchen,1 star,1,20.0,cantonese,Hong Kong,1,,
+parachute,2019,Chicago,Chicago,other asian,$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/parachute,1 star,2,37.5,fusion,Chicago,1,,
+paste,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/paste,1 star,3,62.5,thai,Bangkok,1,,
+patrick guilbaud,2019,City Centre,Ireland,french,$$$$,https://guide.michelin.com/ie/en/dublin/city-centre/restaurant/patrick-guilbaud,2 stars,4,100.0,modern french,City Centre,2,,
+paul ainsworth at no.6,2019,Padstow,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cornwall/padstow/restaurant/paul-ainsworth-at-no-6,1 star,3,62.5,modern cuisine,Padstow,1,,
+pearl dragon,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/pearl-dragon,1 star,3,62.5,cantonese,Macau,1,,
+peel's,2019,Hampton In Arden,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-midlands/hampton-in-arden/restaurant/peel-s,1 star,3,62.5,creative british,Hampton In Arden,1,,
+pelegrini,2019,Sibenik,Croatia,international cuisine,$$$$,https://guide.michelin.com/hr/en/sibenik-knin/sibenik/restaurant/pelegrini,1 star,4,100.0,modern cuisine,Sibenik,1,,
+per se,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/per-se,3 stars,4,100.0,contemporary,New York,3,4.5,2009.0
+peter luger,2019,New York,New York City,american,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/peter-luger,1 star,4,100.0,steakhouse,New York,1,,
+pfefferschiff,2019,Hallwang,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/salzburg-region/hallwang/restaurant/pfefferschiff,1 star,4,100.0,classic cuisine,Hallwang,1,,
+picchi,2019,Sao Paulo,Sao Paulo,italian,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/picchi,1 star,4,100.0,italian,Sao Paulo,1,,
+pied à terre,2019,Bloomsbury,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/pied-a-terre,1 star,3,62.5,creative,Bloomsbury,1,,
+pierre,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/pierre,2 stars,4,100.0,french contemporary,Hong Kong,2,,
+pineapple and pearls,2019,"Washington, D.C.",Washington DC,contemporary,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/pineapple-and-pearls,2 stars,4,100.0,contemporary,"Washington, D.C.",2,,
+pipe and glass,2019,South Dalton,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/east-riding-of-yorkshire/south-dalton/restaurant/pipe-and-glass,1 star,3,62.5,modern british,South Dalton,1,,
+plume,2019,"Washington, D.C.",Washington DC,other european,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/plume,1 star,4,100.0,european,"Washington, D.C.",1,,
+plumed horse,2019,South San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/plumed-horse,1 star,4,100.0,contemporary,South San Francisco,1,,
+pm & vänner,2019,Vaxjo,Sweden,creative,$$$,https://guide.michelin.com/se/en/kronoberg/vaxjo/restaurant/pm-vanner,1 star,3,62.5,creative,Vaxjo,1,,
+pollen street social,2019,Mayfair,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/pollen-street-social,1 star,3,62.5,creative,Mayfair,1,,
+pony & trap,2019,Chew Magna,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/bath-and-north-east-somerset/chew-magna/restaurant/pony-trap,1 star,3,62.5,modern british,Chew Magna,1,,
+poom,2019,Seoul,South Korea,korean,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/poom,1 star,3,62.5,korean,Seoul,1,,
+portland,2019,Regent'S Park,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/regent-s-park/restaurant/portland,1 star,3,62.5,modern cuisine,Regent'S Park,1,,
+pramerl & the wolf,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/pramerl-the-wolf,1 star,4,100.0,creative,Wien,1,,
+protégé,2019,South San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/south-san-francisco/restaurant/protege,1 star,3,62.5,contemporary,South San Francisco,1,,
+providence,2019,Los Angeles,California,seafood,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/providence,2 stars,4,100.0,seafood,Los Angeles,2,,
+pru,2019,Phuket,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/phuket-region/phuket/restaurant/pru,1 star,4,100.0,innovative,Phuket,1,,
+purnell's,2019,Birmingham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/purnell-s,1 star,3,62.5,modern cuisine,Birmingham,1,,
+putien (kitchener road),2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/putien-kitchener-road,1 star,1,20.0,fujian,Singapore,1,,
+pétrus,2019,Belgravia,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/belgravia/restaurant/petrus284688,1 star,3,62.5,french,Belgravia,1,,
+q sushi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/q-sushi,1 star,4,100.0,japanese,Los Angeles,1,,
+qi (wan chai),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/qi-wan-chai,1 star,1,20.0,sichuan,Hong Kong,1,,
+quilon,2019,Victoria,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/victoria/restaurant/quilon,1 star,3,62.5,indian,Victoria,1,,
+quince,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/quince,3 stars,4,100.0,contemporary,San Francisco,3,,
+r-haan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/r-haan,1 star,4,100.0,thai,Bangkok,1,,
+raby hunt,2019,Summerhouse,United Kingdom,british,$$$$,https://guide.michelin.com/gb/en/darlington/summerhouse/restaurant/raby-hunt,2 stars,4,100.0,modern british,Summerhouse,2,,
+rasa,2019,San Francisco,California,indian,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rasa,1 star,2,37.5,indian,San Francisco,1,,
+raw,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/raw,2 stars,3,62.5,innovative,Taipei,2,,
+re-naa,2019,Stavanger,Norway,creative,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/re-naa,1 star,4,100.0,creative,Stavanger,1,,
+rech,2019,Hong Kong,Hong Kong,seafood,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/rech,1 star,4,100.0,seafood,Hong Kong,1,,
+red lion freehouse,2019,East Chisenbury,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/wiltshire/east-chisenbury/restaurant/red-lion-freehouse,1 star,3,62.5,classic cuisine,East Chisenbury,1,,
+relæ,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/relae,1 star,2,37.5,modern cuisine,Kobenhavn,1,,
+restaurant hywel jones by lucknam park,2019,Colerne,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/wiltshire/colerne/restaurant/restaurant-hywel-jones-by-lucknam-park,1 star,3,62.5,modern british,Colerne,1,,
+restaurant nathan outlaw,2019,Port Isaac,United Kingdom,seafood,$$$$,https://guide.michelin.com/gb/en/cornwall/port-isaac/restaurant/restaurant-nathan-outlaw,2 stars,4,100.0,seafood,Port Isaac,2,,
+restaurant sat bains,2019,Nottingham,United Kingdom,creative,$$$$,https://guide.michelin.com/gb/en/nottingham/nottingham/restaurant/restaurant-sat-bains,2 stars,4,100.0,creative,Nottingham,2,,
+restaurant tristan,2019,Horsham,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/west-sussex/horsham/restaurant/restaurant-tristan,1 star,3,62.5,modern british,Horsham,1,,
+rhubarb,2018,Singapore,Singapore,french,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/rhubarb,1 star,1,20.0,french contemporary,Singapore,1,,
+rich table,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/rich-table,1 star,3,62.5,contemporary,San Francisco,1,,
+ritz restaurant,2019,Westminster,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/westminster/restaurant/ritz-restaurant,1 star,3,62.5,modern british,Westminster,1,,
+river café,2019,Hammersmith,United Kingdom,italian,$$$,https://guide.michelin.com/gb/en/greater-london/hammersmith/restaurant/river-cafe,1 star,3,62.5,italian,Hammersmith,1,,
+robuchon au dôme,2019,Macau,Macau,french,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/robuchon-au-dome,3 stars,4,100.0,french contemporary,Macau,3,,
+rogan & co,2019,Cartmel,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/cumbria/cartmel/restaurant/rogan-co,1 star,3,62.5,creative british,Cartmel,1,,
+roganic,2019,Marylebone,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/roganic,1 star,3,62.5,creative british,Marylebone,1,,
+roister,2019,Chicago,Chicago,contemporary,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/roister,1 star,3,62.5,contemporary,Chicago,1,,
+rose's luxury,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/rose-s-luxury,1 star,2,37.5,contemporary,"Washington, D.C.",1,,
+ruean panya,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/ruean-panya,1 star,2,37.5,thai,Bangkok,1,,
+rustic canyon,2019,Los Angeles,California,american,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/rustic-canyon,1 star,3,62.5,californian,Los Angeles,1,,
+ryo gastronomia,2019,Sao Paulo,Sao Paulo,japanese,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/ryo-gastronomia,1 star,4,100.0,japanese,Sao Paulo,1,,
+saawaan,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saawaan,1 star,4,100.0,thai contemporary,Bangkok,1,,
+sabi omakase,2019,Stavanger,Norway,japanese,$$$$,https://guide.michelin.com/no/en/rogaland/stavanger/restaurant/sabi-omakase,1 star,4,100.0,sushi,Stavanger,1,,
+sabor,2019,Mayfair,United Kingdom,spanish,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sabor,1 star,3,62.5,spanish,Mayfair,1,,
+saint pierre,2018,Singapore,Singapore,french,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/saint-pierre,1 star,2,37.5,french contemporary,Singapore,1,,
+saison,2019,San Francisco,California,american,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/saison,2 stars,4,100.0,californian,San Francisco,2,,
+salt,2019,Stratford Upon Avon,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/warwickshire/stratford-upon-avon/restaurant/salt522869,1 star,3,62.5,modern british,Stratford Upon Avon,1,,
+samphire,2019,Saint Helier Saint Helier,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/saint-helier/saint-helier-saint-helier/restaurant/samphire392987,1 star,3,62.5,modern cuisine,Saint Helier Saint Helier,1,,
+saneh jaan,2019,Bangkok,Thailand,other asian,$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/saneh-jaan,1 star,2,37.5,thai,Bangkok,1,,
+satsuki,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/satsuki,1 star,4,100.0,japanese,New York,1,,
+sav,2019,Malmo,Sweden,creative,$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/sav,1 star,3,62.5,creative,Malmo,1,,
+savelberg,2019,Bangkok,Thailand,french,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/savelberg,1 star,3,62.5,french contemporary,Bangkok,1,,
+schwa,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/schwa,1 star,4,100.0,contemporary,Chicago,1,,
+senns.restaurant,2019,Salzburg,Austria,creative,$$$$,https://guide.michelin.com/at/en/salzburg-region/salzburg/restaurant/senns-restaurant,2 stars,4,100.0,creative,Salzburg,2,,
+senses,2019,Warszawa,Poland,international cuisine,$$$$,https://guide.michelin.com/pl/en/masovia/warszawa/restaurant/senses,1 star,4,100.0,modern cuisine,Warszawa,1,,
+sepia,2019,Chicago,Chicago,american,$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/sepia,1 star,3,62.5,american,Chicago,1,,
+seven park place,2019,Saint James'S,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/saint-james-s/restaurant/seven-park-place,1 star,3,62.5,modern cuisine,Saint James'S,1,,
+shang palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/shang-palace,1 star,3,62.5,cantonese,Hong Kong,1,,
+shibumi,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shibumi559644,1 star,4,100.0,japanese,Los Angeles,1,,
+shiki,2019,Wien,Austria,japanese,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/shiki,1 star,4,100.0,japanese,Wien,1,,
+shin sushi,2019,Los Angeles,California,japanese,$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shin-sushi,1 star,3,62.5,japanese,Los Angeles,1,,
+shinji (bras basah road),2018,Singapore,Singapore,japanese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-bras-basah-road,1 star,2,37.5,sushi,Singapore,1,,
+shinji (tanglin road),2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shinji-tanglin-road,1 star,4,100.0,sushi,Singapore,1,,
+shinji by kanesaka,2019,Macau,Macau,japanese,$$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/shinji-by-kanesaka,1 star,4,100.0,sushi,Macau,1,,
+shisen hanten,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shisen-hanten,2 stars,1,20.0,chinese,Singapore,2,,
+shoukouwa,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/shoukouwa,2 stars,4,100.0,sushi,Singapore,2,,
+shoun ryugin,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/shoun-ryugin,2 stars,4,100.0,japanese contemporary,Taipei,2,,
+shunji,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/shunji,1 star,4,100.0,japanese,Los Angeles,1,,
+silvio nickol gourmet restaurant,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/silvio-nickol-gourmet-restaurant,2 stars,4,100.0,modern cuisine,Wien,2,,
+simon radley at chester grosvenor,2019,Chester,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/cheshire-west-and-chester/chester/restaurant/simon-radley-at-chester-grosvenor,1 star,3,62.5,modern cuisine,Chester,1,,
+simpsons,2019,Birmingham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-midlands/birmingham/restaurant/simpsons,1 star,3,62.5,modern cuisine,Birmingham,1,,
+singlethread,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/singlethread,3 stars,4,100.0,contemporary,San Francisco,3,,
+siren by rw,2019,"Washington, D.C.",Washington DC,seafood,$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/siren-by-rw,1 star,3,62.5,seafood,"Washington, D.C.",1,,
+sk mat & människor,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/sk-mat-manniskor,1 star,3,62.5,modern cuisine,Goteborg,1,,
+sketch (the lecture room & library),2019,Mayfair,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/sketch-the-lecture-room-library,2 stars,4,100.0,modern french,Mayfair,2,,
+slotskøkkenet,2019,Horve,Denmark,creative,$$$$,https://guide.michelin.com/dk/en/zealand/horve/restaurant/slotskokkenet,1 star,4,100.0,creative,Horve,1,,
+smyth,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/smyth,2 stars,4,100.0,contemporary,Chicago,2,,
+social eating house,2019,Soho,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/social-eating-house,1 star,3,62.5,modern cuisine,Soho,1,,
+soigné,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/soigne,1 star,3,62.5,innovative,Seoul,1,,
+somni,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/somni,2 stars,4,100.0,contemporary,Los Angeles,2,,
+sons & daughters,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sons-daughters,1 star,4,100.0,contemporary,San Francisco,1,,
+sorn,2019,Bangkok,Thailand,other asian,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sorn,1 star,4,100.0,southern thai,Bangkok,1,,
+sorrel,2019,Dorking,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/surrey/dorking/restaurant/sorrel545459,1 star,3,62.5,modern british,Dorking,1,,
+sorrel,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sorrel,1 star,3,62.5,californian,San Francisco,1,,
+sosban & the old butchers,2019,Menai Bridge Porthaethwy,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/isle-of-anglesey/menai-bridge-porthaethwy/restaurant/sosban-the-old-butchers,1 star,3,62.5,modern cuisine,Menai Bridge Porthaethwy,1,,
+spiaggia,2019,Chicago,Chicago,italian,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/spiaggia,1 star,4,100.0,italian,Chicago,1,,
+spondi,2019,Athina,Greece,french,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/spondi,2 stars,4,100.0,french,Athina,2,,
+spqr,2019,San Francisco,California,italian,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spqr,1 star,3,62.5,italian,San Francisco,1,,
+spring moon,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/spring-moon,1 star,3,62.5,cantonese,Hong Kong,1,,
+spruce,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/spruce,1 star,3,62.5,californian,San Francisco,1,,
+sra bua by kiin kiin,2019,Bangkok,Thailand,other asian,$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/sra-bua-by-kiin-kiin,1 star,3,62.5,thai contemporary,Bangkok,1,,
+st john,2019,Clerkenwell,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/greater-london/clerkenwell/restaurant/st-john,1 star,3,62.5,traditional british,Clerkenwell,1,,
+stand,2019,Budapest,Hungary,international cuisine,$$$,https://guide.michelin.com/hu/en/central-hungary/budapest/restaurant/stand,1 star,3,62.5,modern cuisine,Budapest,1,,
+star inn at harome,2019,Harome,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/north-yorkshire/harome/restaurant/star-inn-at-harome,1 star,3,62.5,modern british,Harome,1,,
+state bird provisions,2019,San Francisco,California,american,$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/state-bird-provisions,1 star,2,37.5,american,San Francisco,1,,
+statholdergaarden,2019,Oslo,Norway,international cuisine,$$$$,https://guide.michelin.com/no/en/oslo-region/oslo/restaurant/statholdergaarden,1 star,4,100.0,classic cuisine,Oslo,1,,
+stay,2019,Seoul,South Korea,french,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/stay,1 star,3,62.5,french contemporary,Seoul,1,,
+steirereck im stadtpark,2019,Wien,Austria,creative,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/steirereck-im-stadtpark,2 stars,4,100.0,creative,Wien,2,,
+story,2019,Bermondsey,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/bermondsey/restaurant/story,1 star,3,62.5,modern cuisine,Bermondsey,1,,
+stud!o at the standard,2019,Kobenhavn,Denmark,creative,$$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/stud-o-at-the-standard,1 star,3,62.5,creative,Kobenhavn,1,,
+suan thip,2019,Bangkok,Thailand,other asian,$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suan-thip,1 star,1,20.0,thai,Bangkok,1,,
+substans,2019,Aarhus,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/central-denmark/aarhus/restaurant/substans,1 star,3,62.5,modern cuisine,Aarhus,1,,
+summer palace,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/summer-palace,1 star,3,62.5,cantonese,Hong Kong,1,,
+summer palace,2018,Singapore,Singapore,chinese,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-palace501658,1 star,1,20.0,cantonese,Singapore,1,,
+summer pavilion,2018,Singapore,Singapore,chinese,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/summer-pavilion,1 star,2,37.5,cantonese,Singapore,1,,
+sun tung lok,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sun-tung-lok,2 stars,2,37.5,cantonese,Hong Kong,2,,
+sushi amamoto,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-amamoto,2 stars,4,100.0,sushi,Taipei,2,,
+sushi amane,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-amane,1 star,4,100.0,japanese,New York,1,,
+sushi ginza onodera,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-ginza-onodera,2 stars,4,100.0,japanese,New York,2,,
+sushi ginza onodera,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/sushi-ginza-onodera559850,2 stars,4,100.0,japanese,Los Angeles,2,,
+sushi ichi,2018,Singapore,Singapore,japanese,$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-ichi,1 star,3,62.5,sushi,Singapore,1,,
+sushi inoue,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-inoue,1 star,4,100.0,japanese,New York,1,,
+sushi kimura,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/sushi-kimura,1 star,4,100.0,sushi,Singapore,1,,
+sushi nakazawa,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-nakazawa,1 star,4,100.0,japanese,New York,1,,
+sushi nomura,2019,Taipei,Taipei,japanese,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-nomura,1 star,3,62.5,sushi,Taipei,1,,
+sushi noz,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-noz,1 star,4,100.0,japanese,New York,1,,
+sushi ryu,2019,Taipei,Taipei,japanese,$$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/sushi-ryu,1 star,4,100.0,sushi,Taipei,1,,
+sushi saito,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-saito,2 stars,4,100.0,sushi,Hong Kong,2,,
+sushi shikon,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-shikon,3 stars,4,100.0,sushi,Hong Kong,3,,
+sushi sho,2019,Stockholm,Sweden,japanese,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/sushi-sho,1 star,3,62.5,japanese,Stockholm,1,,
+sushi taro,2019,"Washington, D.C.",Washington DC,japanese,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/sushi-taro,1 star,4,100.0,japanese,"Washington, D.C.",1,,
+sushi tokami,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-tokami,1 star,4,100.0,sushi,Hong Kong,1,,
+sushi wadatsumi,2019,Hong Kong,Hong Kong,japanese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/sushi-wadatsumi,1 star,3,62.5,sushi,Hong Kong,1,,
+sushi yasuda,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/sushi-yasuda,1 star,4,100.0,japanese,New York,1,,
+sushi yoshizumi,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/sushi-yoshizumi,1 star,4,100.0,japanese,San Francisco,1,,
+søllerød kro,2019,Kobenhavn,Denmark,international cuisine,$$,https://guide.michelin.com/dk/en/capital-region/kobenhavn/restaurant/sollerod-kro,1 star,2,37.5,modern cuisine,Kobenhavn,1,,
+sühring,2019,Bangkok,Thailand,other european,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/suhring,2 stars,4,100.0,european contemporary,Bangkok,2,,
+t'ang court,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/t-ang-court,3 stars,3,62.5,cantonese,Hong Kong,3,,
+ta vie,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ta-vie,2 stars,4,100.0,innovative,Hong Kong,2,,
+table for four,2019,Seoul,South Korea,other european,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/table-for-four,1 star,3,62.5,european contemporary,Seoul,1,,
+taco maría,2019,Costa Mesa,California,mexican,$$$,https://guide.michelin.com/us/en/california/us-costa-mesa/restaurant/taco-maria,1 star,3,62.5,mexican,Costa Mesa,1,,
+tail up goat,2019,"Washington, D.C.",Washington DC,contemporary,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/tail-up-goat,1 star,2,37.5,contemporary,"Washington, D.C.",1,,
+tainan tan tsu mien seafood,2019,Taipei,Taipei,seafood,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tainan-tan-tsu-mien-seafood,1 star,2,37.5,seafood,Taipei,1,,
+takumi by daisuke mori,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/takumi-by-daisuke-mori,1 star,4,100.0,innovative,Hong Kong,1,,
+tangará jean-georges,2019,Sao Paulo,Sao Paulo,international cuisine,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tangara-jean-georges,1 star,4,100.0,modern,Sao Paulo,1,,
+tate,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tate,1 star,4,100.0,innovative,Hong Kong,1,,
+taïrroir,2019,Taipei,Taipei,international cuisine,$$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tairroir,2 stars,3,62.5,innovative,Taipei,2,,
+temporis,2019,Chicago,Chicago,contemporary,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/temporis,1 star,4,100.0,contemporary,Chicago,1,,
+tempura matsui,2019,New York,New York City,japanese,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tempura-matsui,1 star,4,100.0,japanese,New York,1,,
+tenku ryugin,2019,Hong Kong,Hong Kong,japanese,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tenku-ryugin,2 stars,4,100.0,japanese,Hong Kong,2,,
+texture,2019,Marylebone,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/texture,1 star,3,62.5,creative,Marylebone,1,,
+the araki,2019,Mayfair,United Kingdom,japanese,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-araki,3 stars,4,100.0,japanese,Mayfair,3,,
+the cellar,2019,Anstruther,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/fife/anstruther/restaurant/the-cellar,1 star,3,62.5,modern cuisine,Anstruther,1,,
+the checkers,2019,Montgomery Trefaldwyn,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/powys/montgomery-trefaldwyn/restaurant/the-checkers,1 star,3,62.5,french,Montgomery Trefaldwyn,1,,
+the clocktower,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-clocktower,1 star,3,62.5,contemporary,New York,1,,
+the clove club,2019,Shoreditch,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/shoreditch/restaurant/the-clove-club,1 star,3,62.5,modern cuisine,Shoreditch,1,,
+the coach,2019,Marlow,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/buckinghamshire/marlow/restaurant/the-coach,1 star,3,62.5,modern british,Marlow,1,,
+the cross at kenilworth,2019,Kenilworth,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/warwickshire/kenilworth/restaurant/the-cross-at-kenilworth,1 star,3,62.5,classic cuisine,Kenilworth,1,,
+the dabney,2019,"Washington, D.C.",Washington DC,american,$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-dabney,1 star,2,37.5,american,"Washington, D.C.",1,,
+the dining room,2019,Malmesbury,United Kingdom,other asian,$$$,https://guide.michelin.com/gb/en/wiltshire/malmesbury/restaurant/the-dining-room193454,1 star,3,62.5,asian influences,Malmesbury,1,,
+the eight,2019,Macau,Macau,chinese,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-eight,3 stars,3,62.5,chinese,Macau,3,,
+the finch,2019,New York,New York City,american,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-finch,1 star,3,62.5,american,New York,1,,
+the french laundry,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-french-laundry,3 stars,4,100.0,contemporary,San Francisco,3,,
+the glasshouse,2019,Kew,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/kew/restaurant/the-glasshouse,1 star,3,62.5,modern cuisine,Kew,1,,
+the golden peacock,2019,Macau,Macau,indian,$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-golden-peacock,1 star,1,20.0,indian,Macau,1,,
+the guest house,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/the-guest-house,2 stars,2,37.5,sichuan-huai yang,Taipei,2,,
+the inn at little washington,2019,"Washington, D.C.",Washington DC,american,$$$$,https://guide.michelin.com/us/en/washington/washington-dc/restaurant/the-inn-at-little-washington,3 stars,4,100.0,american,"Washington, D.C.",3,,
+the kitchen,2019,Sacramento,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-sacramento/restaurant/the-kitchen559371,1 star,4,100.0,contemporary,Sacramento,1,,
+the kitchen,2019,Macau,Macau,american,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-kitchen417810,1 star,3,62.5,steakhouse,Macau,1,,
+the man behind the curtain,2019,Leeds,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/kent/leeds/restaurant/the-man-behind-the-curtain,1 star,3,62.5,creative,Leeds,1,,
+the modern,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-modern,2 stars,4,100.0,contemporary,New York,2,,
+the musket room,2019,New York,New York City,contemporary,$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-musket-room,1 star,3,62.5,contemporary,New York,1,,
+the neptune,2019,Hunstanton,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/norfolk/hunstanton/restaurant/the-neptune,1 star,3,62.5,modern cuisine,Hunstanton,1,,
+the ninth,2019,Bloomsbury,United Kingdom,other european,$$$,https://guide.michelin.com/gb/en/greater-london/bloomsbury/restaurant/the-ninth,1 star,3,62.5,mediterranean cuisine,Bloomsbury,1,,
+the ocean,2019,Hong Kong,Hong Kong,french,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/the-ocean,1 star,4,100.0,french,Hong Kong,1,,
+the peat inn,2019,Peat Inn,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/fife/peat-inn/restaurant/the-peat-inn,1 star,3,62.5,classic cuisine,Peat Inn,1,,
+the progress,2019,San Francisco,California,american,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-progress,1 star,3,62.5,californian,San Francisco,1,,
+the restaurant at meadowood,2019,San Francisco,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-restaurant-at-meadowood,3 stars,4,100.0,contemporary,San Francisco,3,,
+the river café,2019,New York,New York City,contemporary,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/the-river-cafe,1 star,4,100.0,contemporary,New York,1,,
+the song of india,2018,Singapore,Singapore,indian,$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/the-song-of-india,1 star,1,20.0,indian,Singapore,1,,
+the sportsman,2019,Seasalter,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/kent/seasalter/restaurant/the-sportsman,1 star,3,62.5,modern british,Seasalter,1,,
+the square,2019,Mayfair,United Kingdom,french,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/the-square69391,1 star,3,62.5,creative french,Mayfair,1,,
+the tasting room,2019,Macau,Macau,french,$$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/the-tasting-room,2 stars,3,62.5,french contemporary,Macau,2,,
+the village pub,2019,San Francisco,California,contemporary,$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/the-village-pub,1 star,3,62.5,contemporary,San Francisco,1,,
+the whitebrook,2019,Whitebrook,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/monmouthshire/whitebrook/restaurant/the-whitebrook,1 star,3,62.5,modern british,Whitebrook,1,,
+thomas carr @ the olive room,2019,Ilfracombe,United Kingdom,seafood,$$$,https://guide.michelin.com/gb/en/devon/ilfracombe/restaurant/thomas-carr-the-olive-room,1 star,3,62.5,seafood,Ilfracombe,1,,
+thörnströms kök,2019,Goteborg,Sweden,international cuisine,$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/thornstroms-kok,1 star,3,62.5,classic cuisine,Goteborg,1,,
+ti trin ned,2019,Fredericia,Denmark,international cuisine,$$$,https://guide.michelin.com/dk/en/southern-denmark/fredericia/restaurant/ti-trin-ned,1 star,3,62.5,modern cuisine,Fredericia,1,,
+tian,2019,Wien,Austria,vegetarian,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/tian,1 star,4,100.0,vegetarian,Wien,1,,
+tien hsiang lo,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/tien-hsiang-lo,1 star,2,37.5,hang zhou,Taipei,1,,
+tim allen's flitch of bacon,2019,Little Dunmow,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/essex/little-dunmow/restaurant/tim-allen-s-flitch-of-bacon,1 star,3,62.5,modern british,Little Dunmow,1,,
+tim ho wan (sham shui po),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tim-ho-wan-sham-shui-po,1 star,1,20.0,dim sum,Hong Kong,1,,
+tim's kitchen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/tim-s-kitchen,1 star,2,37.5,cantonese,Macau,1,,
+tin lung heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tin-lung-heen,2 stars,3,62.5,cantonese,Hong Kong,2,,
+topolobampo,2019,Chicago,Chicago,mexican,$$$$,https://guide.michelin.com/us/en/illinois/chicago/restaurant/topolobampo,1 star,4,100.0,mexican,Chicago,1,,
+tosca,2019,Hong Kong,Hong Kong,italian,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/tosca352519,1 star,3,62.5,italian,Hong Kong,1,,
+trinity,2019,Clapham Common,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/greater-london/clapham-common/restaurant/trinity,1 star,3,62.5,modern cuisine,Clapham Common,1,,
+trishna,2019,Marylebone,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/marylebone/restaurant/trishna,1 star,3,62.5,indian,Marylebone,1,,
+trois mec,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/trois-mec,1 star,4,100.0,contemporary,Los Angeles,1,,
+tudor room,2019,Egham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/surrey/egham/restaurant/tudor-room,1 star,3,62.5,modern cuisine,Egham,1,,
+tuju,2019,Sao Paulo,Sao Paulo,creative,$$$$,https://guide.michelin.com/br/en/sao-paulo-region/sao-paulo/restaurant/tuju,2 stars,4,100.0,creative,Sao Paulo,2,,
+tuome,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/tuome,1 star,2,37.5,fusion,New York,1,,
+tyddyn llan,2019,Llandrillo,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/denbighshire/llandrillo/restaurant/tyddyn-llan,1 star,3,62.5,classic cuisine,Llandrillo,1,,
+umu,2019,Mayfair,United Kingdom,japanese,$$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/umu,2 stars,4,100.0,japanese,Mayfair,2,,
+uncle boons,2019,New York,New York City,other asian,$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/uncle-boons,1 star,2,37.5,thai,New York,1,,
+upper house,2019,Goteborg,Sweden,creative,$$$$,https://guide.michelin.com/se/en/vastra-gotaland/goteborg/restaurant/upper-house,1 star,4,100.0,creative,Goteborg,1,,
+upstairs at mikkeller,2019,Bangkok,Thailand,international cuisine,$$$$,https://guide.michelin.com/th/en/bangkok-region/bangkok/restaurant/upstairs-at-mikkeller,1 star,4,100.0,innovative,Bangkok,1,,
+urasawa,2019,Los Angeles,California,japanese,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/urasawa,2 stars,4,100.0,japanese,Los Angeles,2,,
+varoulko seaside,2019,Athina,Greece,seafood,$$$$,https://guide.michelin.com/gr/en/attica/athina/restaurant/varoulko-seaside,1 star,4,100.0,seafood,Athina,1,,
+vea,2019,Hong Kong,Hong Kong,international cuisine,$$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/vea,1 star,4,100.0,innovative,Hong Kong,1,,
+veeraswamy,2019,Mayfair,United Kingdom,indian,$$$,https://guide.michelin.com/gb/en/greater-london/mayfair/restaurant/veeraswamy,1 star,3,62.5,indian,Mayfair,1,,
+vespertine,2019,Los Angeles,California,contemporary,$$$$,https://guide.michelin.com/us/en/california/us-los-angeles/restaurant/vespertine,2 stars,4,100.0,contemporary,Los Angeles,2,,
+vollmers,2019,Malmo,Sweden,creative,$$$$,https://guide.michelin.com/se/en/skane/malmo/restaurant/vollmers,2 stars,4,100.0,creative,Malmo,2,,
+volt,2019,Stockholm,Sweden,creative,$$$,https://guide.michelin.com/se/en/stockholm-region/stockholm/restaurant/volt,1 star,3,62.5,creative,Stockholm,1,,
+wako,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wako,1 star,4,100.0,japanese,San Francisco,1,,
+waku ghin,2018,Singapore,Singapore,japanese,$$$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/waku-ghin,2 stars,4,100.0,japanese contemporary,Singapore,2,,
+wakuriya,2019,San Francisco,California,japanese,$$$$,https://guide.michelin.com/us/en/california/san-francisco/restaurant/wakuriya,1 star,4,100.0,japanese,San Francisco,1,,
+wallsé,2019,New York,New York City,austrian,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/wallse,1 star,4,100.0,austrian,New York,1,,
+walnut tree,2019,Llanddewi Skirrid,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/monmouthshire/llanddewi-skirrid/restaurant/walnut-tree,1 star,3,62.5,modern british,Llanddewi Skirrid,1,,
+walter bauer,2019,Wien,Austria,international cuisine,$$$$,https://guide.michelin.com/at/en/vienna/wien/restaurant/walter-bauer,1 star,4,100.0,classic cuisine,Wien,1,,
+waterside inn,2019,Bray,United Kingdom,french,$$$$,https://guide.michelin.com/gb/en/buckinghamshire/bray/restaurant/waterside-inn,3 stars,4,100.0,classic french,Bray,3,,
+west house,2019,Biddenden,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/kent/biddenden/restaurant/west-house,1 star,3,62.5,modern british,Biddenden,1,,
+white swan,2019,Fence,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/lancashire/fence/restaurant/white-swan,1 star,3,62.5,modern british,Fence,1,,
+whitegrass,2018,Singapore,Singapore,australian,$$,https://guide.michelin.com/sg/en/singapore-region/singapore/restaurant/whitegrass,1 star,2,37.5,australian,Singapore,1,,
+wild honey inn,2019,Lios Duin Bhearna Lisdoonvarna,Ireland,international cuisine,$$$,https://guide.michelin.com/ie/en/clare/lios-duin-bhearna-lisdoonvarna/restaurant/wild-honey-inn,1 star,3,62.5,classic cuisine,Lios Duin Bhearna Lisdoonvarna,1,,
+wilks,2019,Bristol,United Kingdom,british,$$$,https://guide.michelin.com/gb/en/south-gloucestershire/bristol/restaurant/wilks,1 star,3,62.5,modern british,Bristol,1,,
+wing lei,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/wing-lei,1 star,2,37.5,cantonese,Macau,1,,
+winteringham fields,2019,Winteringham,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/north-lincolnshire/winteringham/restaurant/winteringham-fields,1 star,3,62.5,modern cuisine,Winteringham,1,,
+woodspeen,2019,Newbury,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/west-berkshire/newbury/restaurant/woodspeen,1 star,3,62.5,modern cuisine,Newbury,1,,
+xin rong ji,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/xin-rong-ji,1 star,3,62.5,taizhou,Hong Kong,1,,
+ya ge,2019,Taipei,Taipei,chinese,$$,https://guide.michelin.com/tw/en/taipei-region/taipei/restaurant/ya-ge,1 star,2,37.5,cantonese,Taipei,1,,
+yan toh heen,2019,Hong Kong,Hong Kong,chinese,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yan-toh-heen,2 stars,3,62.5,cantonese,Hong Kong,2,,
+yat lok,2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-lok,1 star,1,20.0,cantonese roast meats,Hong Kong,1,,
+yat tung heen (jordan),2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yat-tung-heen-jordan,1 star,2,37.5,cantonese,Hong Kong,1,,
+yauatcha soho,2019,Soho,United Kingdom,chinese,$$$,https://guide.michelin.com/gb/en/greater-london/soho/restaurant/yauatcha-soho,1 star,3,62.5,chinese,Soho,1,,
+yee tung heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/yee-tung-heen,1 star,2,37.5,cantonese,Hong Kong,1,,
+ying,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/ying,1 star,2,37.5,cantonese,Macau,1,,
+ying jee club,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ying-jee-club,2 stars,2,37.5,cantonese,Hong Kong,2,,
+ynyshir,2019,Machynlleth,United Kingdom,creative,$$$,https://guide.michelin.com/gb/en/ceredigion/machynlleth/restaurant/ynyshir,1 star,3,62.5,creative,Machynlleth,1,,
+yorke arms,2019,Pateley Bridge,United Kingdom,international cuisine,$$$,https://guide.michelin.com/gb/en/north-yorkshire/pateley-bridge/restaurant/yorke-arms,1 star,3,62.5,modern cuisine,Pateley Bridge,1,,
+yu yuan,2019,Seoul,South Korea,chinese,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/yu-yuan,1 star,3,62.5,chinese,Seoul,1,,
+yè shanghai (tsim sha tsui),2019,Hong Kong,Hong Kong,chinese,$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ye-shanghai-tsim-sha-tsui,1 star,1,20.0,shanghainese,Hong Kong,1,,
+zero complex,2019,Seoul,South Korea,international cuisine,$$$,https://guide.michelin.com/kr/en/seoul-capital-area/kr-seoul/restaurant/zero-complex,1 star,3,62.5,innovative,Seoul,1,,
+zhejiang heen,2019,Hong Kong,Hong Kong,chinese,$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/zhejiang-heen,1 star,2,37.5,shanghainese,Hong Kong,1,,
+zi yat heen,2019,Macau,Macau,chinese,$$,https://guide.michelin.com/mo/en/macau-region/macau/restaurant/zi-yat-heen,1 star,2,37.5,cantonese,Macau,1,,
+zz's clam bar,2019,New York,New York City,seafood,$$$$,https://guide.michelin.com/us/en/new-york-state/new-york/restaurant/zz-s-clam-bar,1 star,4,100.0,seafood,New York,1,,
+écriture,2019,Hong Kong,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/ecriture,2 stars,3,62.5,french contemporary,Hong Kong,2,,
+épure,2019,nan,Hong Kong,french,$$$,https://guide.michelin.com/hk/en/hong-kong-region/hong-kong/restaurant/epure,1 star,3,62.5,french,nan,1,,
diff --git a/notebooks/visuals_graph_Chiara.ipynb b/notebooks/visuals_graph_Chiara.ipynb
new file mode 100644
index 00000000..6b0f48d9
--- /dev/null
+++ b/notebooks/visuals_graph_Chiara.ipynb
@@ -0,0 +1,535 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d9d5116c-bb5d-4a2b-92fc-309a8c9fa1d6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                               name  year       city  \\\n",
+      "0                                       ho hung kee  2019  Hong Kong   \n",
+      "1                                       feng wei ju  2019      Macau   \n",
+      "2  imperial treasure fine teochew cuisine (orchard)  2018  Singapore   \n",
+      "3                                     shisen hanten  2018  Singapore   \n",
+      "4                                        ma cuisine  2018  Singapore   \n",
+      "\n",
+      "      region  cuisine price  \\\n",
+      "0  Hong Kong  chinese     $   \n",
+      "1      Macau  chinese     $   \n",
+      "2  Singapore  chinese     $   \n",
+      "3  Singapore  chinese     $   \n",
+      "4  Singapore   french     $   \n",
+      "\n",
+      "                                                 url    stars major_city  \n",
+      "0  https://guide.michelin.com/hk/en/hong-kong-reg...   1 star  Hong Kong  \n",
+      "1  https://guide.michelin.com/mo/en/macau-region/...  2 stars      Macau  \n",
+      "2  https://guide.michelin.com/sg/en/singapore-reg...   1 star  Singapore  \n",
+      "3  https://guide.michelin.com/sg/en/singapore-reg...  2 stars  Singapore  \n",
+      "4  https://guide.michelin.com/sg/en/singapore-reg...   1 star  Singapore  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "\n",
+    "df = pd.read_csv('/Users/chiara/Ironhack/week4/first_project/data/clean/CleanStars.csv')\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "ae8328ab-0f00-455b-8a41-dadaab14b5ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['name', 'year', 'city', 'region', 'cuisine', 'price', 'url', 'stars',\n",
+       "       'major_city'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "07f15f06-9954-4463-b4b8-d602076040a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/st/1pfcl_tj40x4zfj7bthg6pdw0000gn/T/ipykernel_99774/369054855.py:10: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data= top_10_cities, x='city', y='restaurant_count', palette='viridis')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Count of restaurants per city Graph\n",
+    "\n",
+    "city_counts = df['city'].value_counts().reset_index()\n",
+    "city_counts.columns = ['city', 'restaurant_count']\n",
+    "\n",
+    "top_10_cities = city_counts.head(10)\n",
+    "\n",
+    "# Using Seaborn for a bar plot\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.barplot(data= top_10_cities, x='city', y='restaurant_count', palette='viridis')\n",
+    "plt.xticks(rotation=90)  # Rotate city names for readability\n",
+    "plt.title('Number of Restaurants per City')\n",
+    "plt.xlabel('City')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "28a276a9-83b7-4b6e-a568-4442038ab130",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/st/1pfcl_tj40x4zfj7bthg6pdw0000gn/T/ipykernel_99774/3331831065.py:15: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  filtered_grouped_data['total_restaurants'] = filtered_grouped_data.groupby('city')['restaurant_count'].transform('sum')\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "# Step 1: Group by region, city, and stars, and aggregate\n",
+    "\n",
+    "grouped_data = df.groupby(['city', 'stars']).size().reset_index(name='restaurant_count')\n",
+    "\n",
+    "# Step 2: Aggregate to get total restaurant counts per city\n",
+    "total_city_counts = grouped_data.groupby(['city'])['restaurant_count'].sum().reset_index()\n",
+    "\n",
+    "# Step 3: Sort by restaurant counts and take the top 10\n",
+    "top_10_cities = total_city_counts.nlargest(10, 'restaurant_count')['city']\n",
+    "\n",
+    "# Step 4: Filter the original grouped data to include only these top 10 cities\n",
+    "filtered_grouped_data = grouped_data[grouped_data['city'].isin(top_10_cities)]\n",
+    "\n",
+    "# Step 5: Sort `filtered_grouped_data` by total restaurant count for each city\n",
+    "filtered_grouped_data['total_restaurants'] = filtered_grouped_data.groupby('city')['restaurant_count'].transform('sum')\n",
+    "filtered_grouped_data = filtered_grouped_data.sort_values(by='total_restaurants', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6ef3fcc8-77f2-41b2-b4e7-562de7d11949",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/st/1pfcl_tj40x4zfj7bthg6pdw0000gn/T/ipykernel_99774/2414122452.py:2: FutureWarning: \n",
+      "\n",
+      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
+      "\n",
+      "  sns.barplot(data=filtered_grouped_data, x='city', y='restaurant_count', hue='stars', ci=None, palette='bright')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "sns.barplot(data=filtered_grouped_data, x='city', y='restaurant_count', hue='stars', ci=None, palette='bright')\n",
+    "\n",
+    "plt.xticks(rotation=45, ha='right')  # Rotate labels for better readability\n",
+    "plt.title('Breakdown of Restaurants by Top 10 Cities and Stars')\n",
+    "plt.xlabel('City')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.legend(title='Stars')  # Legend with the title 'Stars'\n",
+    "plt.tight_layout()  # Adjust layout to fit elements properly\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d5948771-7b07-4804-9dc8-5cc1fa63323e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/st/1pfcl_tj40x4zfj7bthg6pdw0000gn/T/ipykernel_99774/835109087.py:18: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  filtered_region_data['total_restaurants'] = filtered_region_data.groupby('region')['restaurant_count'].transform('sum')\n"
+     ]
+    }
+   ],
+   "source": [
+    "#most popular cuisine per Region\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Group by region and cuisine, then count unique restaurant names\n",
+    "region_cuisine_counts = df.groupby(['region', 'cuisine'])['name'].nunique().reset_index(name='restaurant_count')\n",
+    "\n",
+    "# Aggregate to get total restaurant counts per region\n",
+    "total_region_counts = region_cuisine_counts.groupby('region')['restaurant_count'].sum().reset_index()\n",
+    "\n",
+    "# Sort by restaurant counts and get the top 5 regions\n",
+    "top_5_regions = total_region_counts.nlargest(5, 'restaurant_count')['region']\n",
+    "\n",
+    "# Filter the original data to include only the top 5 regions\n",
+    "filtered_region_data = region_cuisine_counts[region_cuisine_counts['region'].isin(top_5_regions)]\n",
+    "\n",
+    "# Ensure the filtered data is sorted by total restaurant counts in descending order\n",
+    "filtered_region_data['total_restaurants'] = filtered_region_data.groupby('region')['restaurant_count'].transform('sum')\n",
+    "filtered_region_data = filtered_region_data.sort_values(by='total_restaurants', ascending=False)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "318b9b50-93f5-4076-819f-315f03d81d20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/st/1pfcl_tj40x4zfj7bthg6pdw0000gn/T/ipykernel_99774/2405595645.py:3: FutureWarning: \n",
+      "\n",
+      "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
+      "\n",
+      "  bar_plot = sns.barplot(data=filtered_region_data, x='region', y='restaurant_count', hue='cuisine', ci=None, palette='muted', order=filtered_region_data['region'].unique())\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "bar_plot = sns.barplot(data=filtered_region_data, x='region', y='restaurant_count', hue='cuisine', ci=None, palette='muted', order=filtered_region_data['region'].unique())\n",
+    "\n",
+    "plt.xticks(rotation=45, ha='right')  # Rotate labels for better readability\n",
+    "plt.title('Top 5 Regions by Number of Restaurants and Cuisine')\n",
+    "plt.xlabel('Region')\n",
+    "plt.ylabel('Number of Restaurants')\n",
+    "plt.legend(title='Cuisine')  # Legend with the title 'Cuisine'\n",
+    "plt.tight_layout()  # Adjust layout to ensure elements fit well\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "33ed7dc2-30c4-40db-89a2-b4d87fd82981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#which cuisine have the highest recognition of 3 stars?\n",
+    "\n",
+    "three_star_df = df[df['stars'] == '3 stars']\n",
+    "\n",
+    "# Count the number of 3-star restaurants for each cuisine\n",
+    "cuisine_counts = three_star_df['cuisine'].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "ffc227e4-7390-47cc-8bbc-419f7c256d8c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the cuisines count\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "cuisine_counts.plot(kind='bar', color='skyblue')\n",
+    "\n",
+    "plt.xlabel('Cuisine')\n",
+    "plt.ylabel('Number of 3-Star Restaurants')\n",
+    "plt.title('Number of 3-Star Restaurants by Cuisine')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "3fe06cbe-d580-40ea-9f6b-b358bf17f52e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>major_city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>453</th>\n",
+       "      <td>eleven madison park</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>3 stars</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>455</th>\n",
+       "      <td>per se</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>3 stars</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>467</th>\n",
+       "      <td>chef's table at brooklyn fare</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>New York City</td>\n",
+       "      <td>contemporary</td>\n",
+       "      <td>$$$$</td>\n",
+       "      <td>https://guide.michelin.com/us/en/new-york-stat...</td>\n",
+       "      <td>3 stars</td>\n",
+       "      <td>New York</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              name  year      city         region  \\\n",
+       "453            eleven madison park  2019  New York  New York City   \n",
+       "455                         per se  2019  New York  New York City   \n",
+       "467  chef's table at brooklyn fare  2019  New York  New York City   \n",
+       "\n",
+       "          cuisine price                                                url  \\\n",
+       "453  contemporary  $$$$  https://guide.michelin.com/us/en/new-york-stat...   \n",
+       "455  contemporary  $$$$  https://guide.michelin.com/us/en/new-york-stat...   \n",
+       "467  contemporary  $$$$  https://guide.michelin.com/us/en/new-york-stat...   \n",
+       "\n",
+       "       stars major_city  \n",
+       "453  3 stars   New York  \n",
+       "455  3 stars   New York  \n",
+       "467  3 stars   New York  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# most expensive restaurant in contemporary cuisine in new york and hong k\n",
+    "\n",
+    "\n",
+    "# Filter restaurants with 3 stars, contemporary cuisine, and located in New York\n",
+    "filtered_df = df[(df['stars'] == '3 stars') & \n",
+    "                 (df['cuisine'] == 'contemporary') & \n",
+    "                 (df['city'] == 'New York')]\n",
+    "\n",
+    "# Display the result\n",
+    "filtered_df\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "aa60601e-9797-4adf-b45d-3adc72079c6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>city</th>\n",
+       "      <th>region</th>\n",
+       "      <th>cuisine</th>\n",
+       "      <th>price</th>\n",
+       "      <th>url</th>\n",
+       "      <th>stars</th>\n",
+       "      <th>major_city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [name, year, city, region, cuisine, price, url, stars, major_city]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter restaurants with 3 stars, contemporary cuisine, and located in Hong Kong\n",
+    "filtered_df2 = df[(df['stars'] == '3 stars') & \n",
+    "                  (df['cuisine'].str.contains('contemporary', case=False, na=False)) & \n",
+    "                  (df['city'] == 'Hong Kong')]\n",
+    "\n",
+    "# Display the result\n",
+    "filtered_df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b944658-6261-4a49-a48e-2821c7398f2a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/slides/Final Slide_Chiara.pdf b/slides/Final Slide_Chiara.pdf
new file mode 100644
index 00000000..eeb6f731
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diff --git a/slides/slide_zina.pdf b/slides/slide_zina.pdf
new file mode 100644
index 00000000..a60f3c9a
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diff --git a/slides/slides_pedro.pdf b/slides/slides_pedro.pdf
new file mode 100644
index 00000000..61ab80d5
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diff --git a/sql_scripts/allstars1.sql b/sql_scripts/allstars1.sql
new file mode 100644
index 00000000..70667d49
--- /dev/null
+++ b/sql_scripts/allstars1.sql
@@ -0,0 +1,44 @@
+CREATE TABLE IF NOT EXISTS `Restaurant` (
+	`restaurant_id` BIGINT NOT NULL AUTO_INCREMENT UNIQUE,
+	`restaurant_name` VARCHAR(255) NOT NULL,
+	`city` VARCHAR(255) NOT NULL,
+	`region` VARCHAR(255) NOT NULL,
+	`url` VARCHAR(255) NOT NULL,
+	`cuisine_id` BIGINT NOT NULL,
+	PRIMARY KEY(`restaurant_id`)
+);
+
+
+CREATE TABLE IF NOT EXISTS `Stars rating` (
+	`star_id` BIGINT NOT NULL AUTO_INCREMENT UNIQUE,
+	`price_mean` FLOAT NOT NULL,
+	`year` YEAR NOT NULL,
+	`stars` INTEGER NOT NULL,
+	`price_tag` VARCHAR(255) NOT NULL,
+	`restaurant_id` BIGINT NOT NULL,
+	PRIMARY KEY(`star_id`)
+);
+
+
+CREATE TABLE IF NOT EXISTS `Cuisine` (
+	`cuisine_id` BIGINT NOT NULL,
+	`price_mean` FLOAT NOT NULL,
+	`cuisine_type` VARCHAR(255) NOT NULL,
+	PRIMARY KEY(`cuisine_id`)
+);
+
+
+ALTER TABLE `Stars rating`
+ADD FOREIGN KEY(`restaurant_id`) REFERENCES `Restaurant`(`restaurant_id`)
+ON UPDATE NO ACTION ON DELETE NO ACTION;
+ALTER TABLE `Restaurant`
+ADD FOREIGN KEY(`cuisine_id`) REFERENCES `Cuisine`(`cuisine_id`)
+ON UPDATE NO ACTION ON DELETE NO ACTION;
+
+<<<<<<< HEAD
+SELECT name, review_rating, review_count
+FROM stars_df
+WHERE name IN ('eleven madison park','per se','chef''s table at brooklyn fare');
+=======
+SELECT * FROM stars_df
+>>>>>>> Pedro
diff --git a/sql_scripts/create_database.sql b/sql_scripts/create_database.sql
deleted file mode 100644
index e69de29b..00000000
diff --git a/sql_scripts/query_database.sql b/sql_scripts/query_database.sql
deleted file mode 100644
index e69de29b..00000000
diff --git a/src/sources.txt b/src/sources.txt
new file mode 100644
index 00000000..adc0843d
--- /dev/null
+++ b/src/sources.txt
@@ -0,0 +1,2 @@
+https://guide.michelin.com
+WITH APIKEY - https://customerreviews.google.com/
\ No newline at end of file