diff --git a/README.md b/README.md index f637438f..e21f70b8 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,19 @@ # Project overview -... +Objective: +Turning a “blind” travel business policy into a data-driven one. + +Our problem statement (draft - subject to change): +Many travel agencies / tourism service providers struggle to use their historical booking/trip data effectively. Without structured insight into when, where, and for how long travelers go, companies lack evidence to: +optimize marketing and promotions by season / region, +forecast demand to negotiate with accommodation or transport providers, +tailor travel packages to traveler behavior (e.g. trip duration, popular destinations), or +detect under-served / emerging travel destinations that might deserve new offerings. + +Business Use-Cases: +Demand forecasting & capacity planning: by anticipating high-season destinations and months. +Marketing & promotions optimization: align campaigns with seasons, demographics, destinations. +New product/offer development: design destination-specific travel packages (city-break, long trip, destination-bundle) based on observed traveler behavior. +Cost & supplier negotiation leverage: if you know high-traffic destinations ahead of time, you can negotiate better deals with hotels, transport providers, or local vendors. This is similar to what firms in “business travel analytics” aim for. # Installation @@ -55,23 +69,30 @@ If you're a Windows user type: uv pip install -r requirements.txt ``` -# Questions -... +# Hypothesis +1. Seasonality patterns vary between continents Europe and Asia +2. Different traveler demographics (age, nationality) show different destination and trip duration preferences. +3. Travelers choose different accommodation types for different trip durations, or based on their age. +4. Transportation type preferences differ per travelers’ age +5. Accommodation and transportation prices in high demand continents follow seasonality patterns. # Dataset -... +https://www.kaggle.com/datasets/rkiattisak/traveler-trip-data -## Main dataset issues - -- ... -- ... -- ... - -## Solutions for the dataset issues -... +## Main dataset issues & Solutions provided +- Dataset had no major issues +- Needed some basic cleaning across some columns, especially the numerical ones, where we could find currency symbols, text, and commas. +- We needed to prepare and format the date columns in a ways that we can extrapolate new columns specifying the month, so that we can use it in seasonality research. +- The column "destination" was appending city and country together, we actually decided to split that into two new columns with separate information for each. However, many country information was missing (almost the half), therefore we needed to import a library that gets those values. Before that, we needed to format and replace a lot of the cities values in a way that they could be understood by the library in order for it to work. +- We also created a "Boolean" type column called "is_intercontinental_trip", essentially assessing whether the nationality of the traveler and their destination were within the same continent. +- The data set is not big enouhg to be able to draw confident conclusions about different cities, countries, or nationalities of travelers. Therefore, for each of these three columns we created a new column that clustered them into continents. # Conclussions -... +- The dataset is limited, therefore, we need to be careful with the conclusions we make. +- Businesses in Europe and need to adjust their pricing for the month of May till September, in order to enjoy a more balanced demand. For Asian businesses, those month would be March, April, and September. +- For months of low demand, do marketing campaigns and promotions, highlighting the low prices that a traveler could benefits for. This way, businesses can create demand and revenue, even in months with historically low demand. +- To better address the needs of Student-Early Professionals, create discount packages for short term trips in Asia, using Hostels as a primary accommodation. For the Young Professionals, create long trip offerings focusing on Asia and Oceania. For Young Parents, focus on weekend getaways in Europe using the train. +- Follow the demand curves, and make sure to buy less of equipment needed during low demand periods, and buy, early in advance, using economies of scale discounts when you expect a high demand. # Next steps -... +- We would like to find more observations on trvel trip data, especially for older generations, in order to be able to have a more statistically signigicant reserarch outcome, while further curating new products for a larger subset of traveler demographics. diff --git a/data/clean/travel_trip_complete.csv b/data/clean/travel_trip_complete.csv new file mode 100644 index 00000000..036579d4 --- /dev/null +++ b/data/clean/travel_trip_complete.csv @@ -0,0 +1,138 @@ +destination,trip_id,destination_country,destination_city,start_date,end_date,duration_days,traveler_name,traveler_age,traveler_gender,traveler_nationality,accommodation_type,accommodation_cost,transportation_type,transportation_cost,end_year,end_month,start_year,start_month,start_month_name,end_month_name,traveler_nationality_clean +"London, UK",1,UK,London,2023-05-01,2023-05-01,7.0,john smith,35.0,Male,American,Hotel,1200,Flight,600.0,2023,5,2023,5,May,May,United States +"Phuket, Thailand",2,Thailand,Phuket,2023-06-01,2023-06-01,5.0,jane doe,28.0,Female,Canadian,Resort,800,Flight,500.0,2023,6,2023,6,June,June,Canada +"Bali, Indonesia",3,Indonesia,Bali,2023-07-01,2023-07-01,7.0,david lee,45.0,Male,Korean,Villa,1000,Flight,700.0,2023,7,2023,7,July,July,South Korea +"New York, USA",4,USA,New York,2023-08-01,2023-08-01,14.0,sarah johnson,29.0,Female,British,Hotel,2000,Flight,1000.0,2023,8,2023,8,August,August,United Kingdom +"Tokyo, Japan",5,Japan,Tokyo,2023-09-01,2023-09-01,7.0,kim nguyen,26.0,Female,Vietnamese,Airbnb,700,Train,200.0,2023,9,2023,9,September,September,Vietnamese +"Paris, France",6,France,Paris,2023-10-01,2023-10-01,5.0,michael brown,42.0,Male,American,Hotel,1500,Flight,800.0,2023,10,2023,10,October,October,United States +"Sydney, Australia",7,Australia,Sydney,2023-11-01,2023-11-01,10.0,emily davis,33.0,Female,Australian,Hostel,500,Flight,1200.0,2023,11,2023,11,November,November,Australian +"Rio de Janeiro, Brazil",8,Brazil,Rio de Janeiro,2024-01-01,2024-01-01,7.0,lucas santos,25.0,Male,Brazilian,Airbnb,900,Flight,600.0,2024,1,2024,1,January,January,Brazil +"Amsterdam, Netherlands",9,Netherlands,Amsterdam,2024-02-01,2024-02-01,7.0,laura janssen,31.0,Female,Dutch,Hotel,1200,Train,200.0,2024,2,2024,2,February,February,Dutch +"Dubai, United Arab Emirates",10,United Arab Emirates,Dubai,2024-03-01,2024-03-01,7.0,mohammed ali,39.0,Male,Emirati,Resort,2500,Flight,800.0,2024,3,2024,3,March,March,United Arab Emirates +"Cancun, Mexico",11,Mexico,Cancun,2024-04-01,2024-04-01,7.0,ana hernandez,27.0,Female,Mexican,Hotel,1000,Flight,500.0,2024,4,2024,4,April,April,Mexican +"Barcelona, Spain",12,Spain,Barcelona,2024-05-01,2024-05-01,7.0,carlos garcia,36.0,Male,Spanish,Airbnb,800,Train,100.0,2024,5,2024,5,May,May,Spain +"Honolulu, Hawaii",13,Hawaii,Honolulu,2024-06-01,2024-06-01,8.0,lily wong,29.0,Female,Chinese,Resort,3000,Flight,1200.0,2024,6,2024,6,June,June,China +"Berlin, Germany",14,Germany,Berlin,2024-07-01,2024-07-01,9.0,hans mueller,48.0,Male,German,Hotel,1400,Flight,700.0,2024,7,2024,7,July,July,Germany +"Marrakech, Morocco",15,Morocco,Marrakech,2024-08-01,2024-08-01,7.0,fatima khouri,26.0,Female,Moroccan,Villa,600,Flight,400.0,2024,8,2024,8,August,August,Moroccan +"Edinburgh, Scotland",16,Scotland,Edinburgh,2024-09-01,2024-09-01,7.0,james mackenzie,32.0,Male,Scottish,Hotel,900,Train,150.0,2024,9,2024,9,September,September,United Kingdom +Paris,17,,Paris,2023-09-01,2023-09-01,9.0,sarah johnson,30.0,Female,American,Hotel,900,Airplane,400.0,2023,9,2023,9,September,September,United States +Bali,18,,Bali,2023-08-01,2023-08-01,10.0,michael chang,28.0,Male,Chinese,Resort,1500,Airplane,700.0,2023,8,2023,8,August,August,China +London,19,,London,2023-07-01,2023-07-01,6.0,olivia rodriguez,35.0,Female,British,Hotel,1200,Train,150.0,2023,7,2023,7,July,July,United Kingdom +Tokyo,20,,Tokyo,2023-10-01,2023-10-01,10.0,kenji nakamura,45.0,Male,Japanese,Hotel,1200,Airplane,800.0,2023,10,2023,10,October,October,Japan +New York,21,,New York,2023-11-01,2023-11-01,5.0,emily lee,27.0,Female,American,Airbnb,600,Bus,100.0,2023,11,2023,11,November,November,United States +Sydney,22,,Sydney,2023-12-01,2023-12-01,7.0,james wilson,32.0,Male,Australian,Hotel,1000,Airplane,600.0,2023,12,2023,12,December,December,Australian +Rome,23,,Rome,2023-11-01,2023-11-01,7.0,sofia russo,29.0,Female,Italian,Airbnb,700,Train,80.0,2023,11,2023,11,November,November,Italy +Bangkok,24,,Bangkok,2023-09-01,2023-09-01,8.0,raj patel,40.0,Male,Indian,Hostel,400,Airplane,500.0,2023,9,2023,9,September,September,Indian +Paris,25,,Paris,2023-12-01,2023-12-01,6.0,lily nguyen,24.0,Female,Vietnamese,Hotel,1400,Train,100.0,2023,12,2023,12,December,December,Vietnamese +Hawaii,26,,Hawaii,2023-08-01,2023-08-01,9.0,david kim,34.0,Male,Korean,Resort,2000,Airplane,800.0,2023,8,2023,8,August,August,South Korea +Barcelona,27,,Barcelona,2023-10-01,2023-10-01,8.0,maria garcia,31.0,Female,Spanish,Hotel,1100,Train,150.0,2023,10,2023,10,October,October,Spain +Japan,28,,Japan,2022-05-01,2022-05-01,8.0,alice smith,30.0,Female,American,Hotel,800,Airplane,500.0,2022,5,2022,5,May,May,United States +Thailand,29,,Thailand,2022-06-01,2022-06-01,7.0,bob johnson,45.0,Male,Canadian,Hostel,200,Train,150.0,2022,6,2022,6,June,June,Canada +France,30,,France,2022-07-01,2022-07-01,9.0,charlie lee,25.0,Male,Korean,Airbnb,600,Rental Car,300.0,2022,7,2022,7,July,July,South Korea +Australia,31,,Australia,2022-08-01,2022-09-01,13.0,emma davis,28.0,Female,British,Hotel,1000,Rental Car,500.0,2022,9,2022,8,August,September,United Kingdom +Brazil,32,,Brazil,2022-09-01,2022-09-01,9.0,olivia martin,33.0,Female,Australian,Hostel,150,Bus,50.0,2022,9,2022,9,September,September,Australian +Greece,33,,Greece,2022-10-01,2022-10-01,8.0,harry wilson,20.0,Male,American,Airbnb,400,Airplane,600.0,2022,10,2022,10,October,October,United States +Egypt,34,,Egypt,2022-11-01,2022-11-01,7.0,sophia lee,37.0,Female,Canadian,Hotel,700,Train,100.0,2022,11,2022,11,November,November,Canada +Mexico,35,,Mexico,2023-01-01,2023-01-01,10.0,james brown,42.0,Male,British,Airbnb,500,Airplane,800.0,2023,1,2023,1,January,January,United Kingdom +Italy,36,,Italy,2023-02-01,2023-02-01,6.0,mia johnson,31.0,Female,American,Hostel,180,Train,120.0,2023,2,2023,2,February,February,United States +Spain,37,,Spain,2023-03-01,2023-03-01,8.0,william davis,27.0,Male,Korean,Hotel,900,Rental Car,400.0,2023,3,2023,3,March,March,South Korea +Canada,38,,Canada,2023-04-01,2023-04-01,7.0,amelia brown,38.0,Female,Australian,Airbnb,350,Bus,75.0,2023,4,2023,4,April,April,Australian +"Paris, France",39,France,Paris,2022-06-01,2022-06-01,7.0,mia johnson,25.0,Female,American,Hotel,1400,Airplane,600.0,2022,6,2022,6,June,June,United States +"Sydney, Australia",40,Australia,Sydney,2023-01-01,2023-01-01,7.0,adam lee,33.0,Male,Canadian,Airbnb,800,Train,150.0,2023,1,2023,1,January,January,Canada +"Tokyo, Japan",41,Japan,Tokyo,2022-12-01,2022-12-01,8.0,sarah wong,28.0,Female,Chinese,Hostel,500,Airplane,900.0,2022,12,2022,12,December,December,China +"Cancun, Mexico",42,Mexico,Cancun,2023-07-01,2023-07-01,7.0,john smith,45.0,Male,American,Resort,2200,Airplane,800.0,2023,7,2023,7,July,July,United States +"Rio de Janeiro, Brazil",43,Brazil,Rio de Janeiro,2022-11-01,2022-11-01,7.0,maria silva,30.0,Female,Brazilian,Hotel,1200,Airplane,700.0,2022,11,2022,11,November,November,Brazil +"London, UK",44,UK,London,2023-03-01,2023-03-01,7.0,peter brown,55.0,Male,British,Airbnb,900,Train,100.0,2023,3,2023,3,March,March,United Kingdom +"Barcelona, Spain",45,Spain,Barcelona,2023-08-01,2023-08-01,7.0,emma garcia,27.0,Female,Spanish,Hostel,600,Airplane,600.0,2023,8,2023,8,August,August,Spain +"New York City, USA",46,USA,New York City,2022-09-01,2022-09-01,7.0,michael davis,41.0,Male,American,Hotel,1500,Airplane,500.0,2022,9,2022,9,September,September,United States +"Bangkok, Thailand",47,Thailand,Bangkok,2023-05-01,2023-05-01,6.0,nina patel,29.0,Female,Indian,Airbnb,500,Bus,50.0,2023,5,2023,5,May,May,Indian +"Vancouver, Canada",48,Canada,Vancouver,2022-07-01,2022-07-01,7.0,kevin kim,24.0,Male,Korean,Hostel,400,Train,150.0,2022,7,2022,7,July,July,South Korea +"Amsterdam, Netherlands",49,Netherlands,Amsterdam,2023-06-01,2023-06-01,8.0,laura van den berg,31.0,Female,Dutch,Hotel,1100,Airplane,700.0,2023,6,2023,6,June,June,Dutch +"Paris, France",50,France,Paris,2023-08-01,2023-08-01,7.0,jennifer nguyen,31.0,Female,Canadian,Hotel,1200,Train,300.0,2023,8,2023,8,August,August,Canada +"Tokyo, Japan",51,Japan,Tokyo,2023-10-01,2023-10-01,10.0,david kim,25.0,Male,American,Hostel,500,Bus,100.0,2023,10,2023,10,October,October,United States +"Sydney, AUS",52,AUS,Sydney,2023-11-01,2023-11-01,7.0,rachel lee,27.0,Female,South Korean,Airbnb,900,Rental Car,200.0,2023,11,2023,11,November,November,South Korea +"New York, USA",53,USA,New York,2023-12-01,2023-12-01,7.0,jessica wong,28.0,Female,Canadian,Hotel,1400,Flight,800.0,2023,12,2023,12,December,December,Canada +"Rio de Janeiro, Brazil",54,Brazil,Rio de Janeiro,2024-01-01,2024-01-01,9.0,felipe almeida,30.0,Male,Brazilian,Airbnb,800,Train,150.0,2024,1,2024,1,January,January,Brazil +"Bangkok, Thailand",55,Thailand,Bangkok,2024-02-01,2024-02-01,8.0,nisa patel,23.0,Female,Indian,Hostel,400,Bus,50.0,2024,2,2024,2,February,February,Indian +"London, UK",56,UK,London,2024-03-01,2024-03-01,8.0,ben smith,35.0,Male,British,Hotel,1000,Train,200.0,2024,3,2024,3,March,March,United Kingdom +"Barcelona, Spain",57,Spain,Barcelona,2024-04-01,2024-04-01,8.0,laura gomez,29.0,Female,Spanish,Airbnb,700,Rental Car,250.0,2024,4,2024,4,April,April,Spain +"Seoul, South Korea",58,South Korea,Seoul,2024-05-01,2024-05-01,8.0,park min woo,27.0,Male,South Korean,Hostel,500,Subway,20.0,2024,5,2024,5,May,May,South Korea +"Los Angeles, USA",59,USA,Los Angeles,2024-06-01,2024-06-01,7.0,michael chen,26.0,Male,Chinese,Hotel,1200,Rental Car,300.0,2024,6,2024,6,June,June,China +"Rome, Italy",60,Italy,Rome,2024-07-01,2024-07-01,8.0,sofia rossi,33.0,Female,Italian,Airbnb,800,Train,100.0,2024,7,2024,7,July,July,Italy +Paris,61,,Paris,2022-07-01,2022-07-01,6.0,rachel sanders,35.0,Female,American,Hotel,1200,Airplane,800.0,2022,7,2022,7,July,July,United States +Tokyo,62,,Tokyo,2022-09-01,2022-09-01,7.0,kenji nakamura,28.0,Male,Japanese,Hostel,400,Train,300.0,2022,9,2022,9,September,September,Japan +Cape Town,63,,Cape Town,2023-01-01,2023-01-01,9.0,emily watson,29.0,Female,British,Vacation rental,800,Rental Car,200.0,2023,1,2023,1,January,January,United Kingdom +Sydney,64,,Sydney,2023-06-01,2023-06-01,6.0,david lee,43.0,Male,Australian,Hotel,1500,Airplane,1200.0,2023,6,2023,6,June,June,Australian +Barcelona,65,,Barcelona,2023-08-01,2023-08-01,7.0,ana rodriguez,31.0,Female,Spanish,Vacation rental,900,Airplane,700.0,2023,8,2023,8,August,August,Spain +Bali,66,,Bali,2024-02-01,2024-02-01,7.0,tom wilson,27.0,Male,American,Resort,2200,Airplane,1000.0,2024,2,2024,2,February,February,United States +Paris,67,,Paris,2024-05-01,2024-05-01,6.0,olivia green,39.0,Female,French,Hotel,1100,Train,200.0,2024,5,2024,5,May,May,French +New York,68,,New York,2024-07-01,2024-07-01,6.0,james chen,25.0,Male,American,Vacation rental,1000,Airplane,800.0,2024,7,2024,7,July,July,United States +Bangkok,69,,Bangkok,2024-09-01,2024-09-01,8.0,lila patel,33.0,Female,Indian,Hostel,300,Airplane,700.0,2024,9,2024,9,September,September,Indian +Rome,70,,Rome,2025-02-01,2025-02-01,6.0,marco rossi,41.0,Male,Italian,Hotel,1300,Train,100.0,2025,2,2025,2,February,February,Italy +Bali,71,,Bali,2025-05-01,2025-05-01,8.0,sarah brown,37.0,Female,British,Resort,1800,Airplane,1000.0,2025,5,2025,5,May,May,United Kingdom +"Bali, Indonesia",73,Indonesia,Bali,2022-08-01,2022-08-01,7.0,sarah lee,35.0,Female,South Korean,Resort,500,Airplane,800.0,2022,8,2022,8,August,August,South Korea +"Tokyo, Japan",74,Japan,Tokyo,2023-01-01,2023-01-01,8.0,alex kim,29.0,Male,American,Hotel,1000,Train,200.0,2023,1,2023,1,January,January,United States +"Cancun, Mexico",75,Mexico,Cancun,2023-04-01,2023-04-01,7.0,maria hernandez,42.0,Female,Mexican,Resort,800,Airplane,500.0,2023,4,2023,4,April,April,Mexican +"Paris, France",76,France,Paris,2023-06-01,2023-06-01,7.0,john smith,46.0,Male,British,Hotel,1200,Airplane,700.0,2023,6,2023,6,June,June,United Kingdom +"Cape Town, SA",77,SA,Cape Town,2023-09-01,2023-09-01,9.0,mark johnson,31.0,Male,South African,Vacation rental,400,Private Car,300.0,2023,9,2023,9,September,September,South African +"Bali, Indonesia",78,Indonesia,Bali,2023-11-01,2023-11-01,7.0,amanda chen,25.0,Female,Taiwanese,Resort,600,Airplane,700.0,2023,11,2023,11,November,November,Taiwan +"Sydney, Aus",79,Aus,Sydney,2024-02-01,2024-02-01,7.0,david lee,38.0,Male,Australian,Hotel,900,Airplane,600.0,2024,2,2024,2,February,February,Australian +"Bangkok, Thai",80,Thai,Bangkok,2024-05-01,2024-05-01,7.0,nana kwon,27.0,Female,Korean,Hotel,400,Airplane,400.0,2024,5,2024,5,May,May,South Korea +"New York, USA",81,USA,New York,2024-08-01,2024-08-01,7.0,tom hanks,60.0,Male,American,Hotel,1500,Airplane,1000.0,2024,8,2024,8,August,August,United States +"Phuket, Thai",82,Thai,Phuket,2025-01-01,2025-01-01,7.0,emma watson,32.0,Female,British,Resort,700,Airplane,800.0,2025,1,2025,1,January,January,United Kingdom +"Rome, Italy",83,Italy,Rome,2025-04-01,2025-04-01,7.0,james kim,41.0,Male,American,Hotel,100,Train,405.0,2025,4,2025,4,April,April,United States +Paris,84,,Paris,2021-06-01,2021-06-01,6.0,john smith,35.0,Male,American,Hotel,800,Airplane,500.0,2021,6,2021,6,June,June,United States +Tokyo,85,,Tokyo,2021-07-01,2021-07-01,10.0,sarah lee,28.0,Female,Korean,Airbnb,500,Train,300.0,2021,7,2021,7,July,July,South Korea +Bali,86,,Bali,2021-08-01,2021-08-01,11.0,maria garcia,42.0,Female,Spanish,Resort,1200,Airplane,700.0,2021,8,2021,8,August,August,Spain +Sydney,87,,Sydney,2021-09-01,2021-09-01,9.0,david lee,45.0,Male,Australian,Hotel,900,Airplane,600.0,2021,9,2021,9,September,September,Australian +New York,88,,New York,2021-10-01,2021-10-01,6.0,emily davis,31.0,Female,American,Airbnb,700,Rental Car,200.0,2021,10,2021,10,October,October,United States +London,89,,London,2021-11-01,2021-11-01,11.0,james wilson,29.0,Male,British,Hostel,300,Airplane,400.0,2021,11,2021,11,November,November,United Kingdom +Dubai,90,,Dubai,2022-01-01,2022-01-01,8.0,fatima ahmed,24.0,Female,Emirati,Hotel,1000,Airplane,800.0,2022,1,2022,1,January,January,United Arab Emirates +Bangkok,91,,Bangkok,2022-02-01,2022-02-01,7.0,liam nguyen,26.0,Male,Vietnamese,Airbnb,400,Train,100.0,2022,2,2022,2,February,February,Vietnamese +Rome,92,,Rome,2022-03-01,2022-03-01,11.0,giulia rossi,30.0,Female,Italian,Hostel,200,Airplane,350.0,2022,3,2022,3,March,March,Italy +Bali,93,,Bali,2022-04-01,2022-04-01,11.0,putra wijaya,33.0,Male,Indonesian,Villa,1500,Rental Car,300.0,2022,4,2022,4,April,April,Indonesian +Seoul,94,,Seoul,2022-05-01,2022-05-01,10.0,kim min ji,27.0,Female,Korean,Hotel,800,Train,150.0,2022,5,2022,5,May,May,South Korea +Paris,95,,Paris,2022-06-01,2022-06-01,5.0,john smith,35.0,Male,USA,Hotel,500,Airplane,800.0,2022,6,2022,6,June,June,United States +Tokyo,96,,Tokyo,2022-09-01,2022-09-01,9.0,emily johnson,28.0,Female,Canada,Airbnb,400,Train,200.0,2022,9,2022,9,September,September,Canada +Sydney,97,,Sydney,2022-11-01,2022-12-01,9.0,david lee,45.0,Male,South Korea,Hostel,200,Airplane,1200.0,2022,12,2022,11,November,December,South Korea +London,98,,London,2023-02-01,2023-02-01,5.0,sarah brown,37.0,Female,UK,Hotel,600,Airplane,700.0,2023,2,2023,2,February,February,United Kingdom +New York,99,,New York,2023-05-01,2023-05-01,6.0,michael wong,50.0,Male,China,Airbnb,800,Rental Car,300.0,2023,5,2023,5,May,May,China +Rome,100,,Rome,2023-08-01,2023-08-01,7.0,jessica chen,31.0,Female,Taiwan,Hotel,700,Airplane,900.0,2023,8,2023,8,August,August,Taiwan +Bangkok,101,,Bangkok,2023-11-01,2023-11-01,8.0,ken tanaka,42.0,Male,Japan,Hostel,300,Train,100.0,2023,11,2023,11,November,November,Japan +Cape Town,102,,Cape Town,2024-01-01,2024-01-01,8.0,maria garcia,27.0,Female,Spain,Airbnb,500,Airplane,1500.0,2024,1,2024,1,January,January,Spain +Rio de Janeiro,103,,Rio de Janeiro,2024-04-01,2024-04-01,7.0,rodrigo oliveira,33.0,Male,Brazil,Hotel,900,Rental Car,400.0,2024,4,2024,4,April,April,Brazil +Bali,104,,Bali,2024-07-01,2024-07-01,6.0,olivia kim,29.0,Female,South Korea,Villa,1200,Airplane,1000.0,2024,7,2024,7,July,July,South Korea +Amsterdam,105,,Amsterdam,2024-10-01,2024-10-01,7.0,robert mueller,41.0,Male,Germany,Hotel,600,Train,150.0,2024,10,2024,10,October,October,Germany +Paris,106,,Paris,2022-05-01,2022-05-01,5.0,john smith,35.0,Male,USA,Hotel,1000,Airplane,800.0,2022,5,2022,5,May,May,United States +Tokyo,107,,Tokyo,2022-09-01,2022-09-01,9.0,sarah lee,28.0,Female,South Korea,Airbnb,800,Train,500.0,2022,9,2022,9,September,September,South Korea +New York,108,,New York,2022-06-01,2022-06-01,5.0,michael wong,42.0,Male,Hong Kong,Hotel,1200,Rental Car,200.0,2022,6,2022,6,June,June,Hong Kong +Bali,109,,Bali,2022-08-01,2022-08-01,8.0,lisa chen,30.0,Female,Taiwan,Resort,1500,Airplane,1200.0,2022,8,2022,8,August,August,Taiwan +Sydney,110,,Sydney,2022-07-01,2022-07-01,9.0,david kim,26.0,Male,Canada,Hostel,300,Airplane,900.0,2022,7,2022,7,July,July,Canada +London,111,,London,2022-06-01,2022-06-01,5.0,emily wong,38.0,Female,United Kingdom,Hotel,900,Train,150.0,2022,6,2022,6,June,June,United Kingdom +Phuket,112,,Phuket,2022-09-01,2022-09-01,7.0,mark tan,45.0,Male,Singapore,Villa,2000,Airplane,700.0,2022,9,2022,9,September,September,Singapore +Rome,113,,Rome,2022-05-01,2022-05-01,7.0,emma lee,31.0,Female,Italy,Hotel,1100,Train,250.0,2022,5,2022,5,May,May,Italy +Santorini,114,,Santorini,2022-07-01,2022-07-01,7.0,george chen,27.0,Male,Greece,Airbnb,1000,Ferry,150.0,2022,7,2022,7,July,July,Greece +Dubai,115,,Dubai,2022-08-01,2022-08-01,5.0,sophia kim,29.0,Female,United Arab Emirates,Hotel,1500,Rental Car,300.0,2022,8,2022,8,August,August,United Arab Emirates +Phnom Penh,116,,Phnom Penh,2022-09-01,2022-09-01,5.0,alex ng,33.0,Male,Cambodia,Hostel,200,Airplane,500.0,2022,9,2022,9,September,September,Cambodia +"Tokyo, Japan",117,Japan,Tokyo,2022-02-01,2022-02-01,9.0,alice smith,32.0,Female,American,Hotel,1000,Airplane,700.0,2022,2,2022,2,February,February,United States +"Paris, France",118,France,Paris,2022-03-01,2022-03-01,7.0,bob johnson,47.0,Male,Canadian,Hotel,1200,Train,500.0,2022,3,2022,3,March,March,Canada +"Sydney, Aus",119,Aus,Sydney,2022-05-01,2022-05-01,11.0,cindy chen,26.0,Female,Chinese,Airbnb,800,Airplane,1000.0,2022,5,2022,5,May,May,China +"Rome, Italy",120,Italy,Rome,2022-06-01,2022-06-01,7.0,david lee,38.0,Male,Korean,Hotel,900,Train,400.0,2022,6,2022,6,June,June,South Korea +"Bali, Indonesia",121,Indonesia,Bali,2022-07-01,2022-07-01,10.0,emily kim,29.0,Female,Korean,Hostel,500,Airplane,800.0,2022,7,2022,7,July,July,South Korea +"Cancun, Mexico",122,Mexico,Cancun,2022-08-01,2022-08-01,8.0,frank li,41.0,Male,American,Hotel,1300,Airplane,600.0,2022,8,2022,8,August,August,United States +"Athens, Greece",123,Greece,Athens,2022-09-01,2022-09-01,10.0,gina lee,35.0,Female,Korean,Airbnb,700,Airplane,900.0,2022,9,2022,9,September,September,South Korea +"Tokyo, Japan",124,Japan,Tokyo,2022-10-01,2022-10-01,8.0,henry kim,24.0,Male,Korean,Hotel,1200,Airplane,700.0,2022,10,2022,10,October,October,South Korea +"Sydney, Aus",125,Aus,Sydney,2022-11-01,2022-11-01,10.0,isabella chen,30.0,Female,Chinese,Airbnb,900,Airplane,1000.0,2022,11,2022,11,November,November,China +"Paris, France",126,France,Paris,2022-12-01,2023-01-01,8.0,jack smith,28.0,Male,American,Hostel,400,Airplane,700.0,2023,1,2022,12,December,January,United States +"Bali, Indonesia",127,Indonesia,Bali,2023-02-01,2023-02-01,8.0,katie johnson,33.0,Female,Canadian,Hotel,800,Airplane,800.0,2023,2,2023,2,February,February,Canada +"Paris, France",129,France,Paris,2023-05-01,2023-05-01,6.0,john doe,35.0,Male,American,Hotel,5000,Airplane,2500.0,2023,5,2023,5,May,May,United States +"Tokyo, Japan",130,Japan,Tokyo,2023-05-01,2023-05-01,7.0,jane smith,28.0,Female,British,Airbnb,7000,Train,1500.0,2023,5,2023,5,May,May,United Kingdom +"Cape Town, South Africa",131,South Africa,Cape Town,2023-06-01,2023-06-01,9.0,michael johnson,45.0,Male,South African,Hostel,3000,Private Car,2000.0,2023,6,2023,6,June,June,South African +"Sydney, Australia",132,Australia,Sydney,2023-06-01,2023-06-01,6.0,sarah lee,31.0,Female,Australian,Hotel,6000,Airplane,3000.0,2023,6,2023,6,June,June,Australian +"Rome, Italy",133,Italy,Rome,2023-07-01,2023-07-01,7.0,david kim,42.0,Male,Korean,Airbnb,4000,Train,1500.0,2023,7,2023,7,July,July,South Korea +"New York City, USA",134,USA,New York City,2023-07-01,2023-07-01,7.0,emily davis,27.0,Female,American,Hotel,8000,Airplane,2500.0,2023,7,2023,7,July,July,United States +"Rio de Janeiro, Brazil",135,Brazil,Rio de Janeiro,2023-08-01,2023-08-01,9.0,jose perez,37.0,Male,Brazilian,Hostel,2500,Private Car,2000.0,2023,8,2023,8,August,August,Brazil +"Vancouver, Canada",136,Canada,Vancouver,2023-08-01,2023-08-01,6.0,emma wilson,29.0,Female,Canadian,Hotel,5000,Airplane,3000.0,2023,8,2023,8,August,August,Canada +"Bangkok, Thailand",137,Thailand,Bangkok,2023-09-01,2023-09-01,7.0,ryan chen,34.0,Male,Chinese,Hostel,2000,Train,1000.0,2023,9,2023,9,September,September,China +"Barcelona, Spain",138,Spain,Barcelona,2023-09-01,2023-09-01,7.0,sofia rodriguez,25.0,Female,Spanish,Airbnb,6000,Airplane,2500.0,2023,9,2023,9,September,September,Spain +"Auckland, New Zealand",139,New Zealand,Auckland,2023-10-01,2023-10-01,7.0,william brown,39.0,Male,New Zealander,Hotel,7000,Train,2500.0,2023,10,2023,10,October,October,New Zealander diff --git a/data/clean/travel_trip_complete.parquet b/data/clean/travel_trip_complete.parquet new file mode 100644 index 00000000..ba92e2e7 Binary files /dev/null and b/data/clean/travel_trip_complete.parquet differ diff --git a/data/raw/Travel details dataset.csv b/data/raw/Travel details dataset.csv new file mode 100644 index 00000000..53c2e9dd --- /dev/null +++ b/data/raw/Travel details dataset.csv @@ -0,0 +1,140 @@ +Trip ID,Destination,Start date,End date,Duration (days),Traveler name,Traveler age,Traveler gender,Traveler nationality,Accommodation type,Accommodation cost,Transportation type,Transportation cost +1,"London, UK",5/1/2023,5/8/2023,7,John Smith,35,Male,American,Hotel,1200,Flight,600 +2,"Phuket, Thailand",6/15/2023,6/20/2023,5,Jane Doe,28,Female,Canadian,Resort,800,Flight,500 +3,"Bali, Indonesia",7/1/2023,7/8/2023,7,David Lee,45,Male,Korean,Villa,1000,Flight,700 +4,"New York, USA",8/15/2023,8/29/2023,14,Sarah Johnson,29,Female,British,Hotel,2000,Flight,1000 +5,"Tokyo, Japan",9/10/2023,9/17/2023,7,Kim Nguyen,26,Female,Vietnamese,Airbnb,700,Train,200 +6,"Paris, France",10/5/2023,10/10/2023,5,Michael Brown,42,Male,American,Hotel,1500,Flight,800 +7,"Sydney, Australia",11/20/2023,11/30/2023,10,Emily Davis,33,Female,Australian,Hostel,500,Flight,1200 +8,"Rio de Janeiro, Brazil",1/5/2024,1/12/2024,7,Lucas Santos,25,Male,Brazilian,Airbnb,900,Flight,600 +9,"Amsterdam, Netherlands",2/14/2024,2/21/2024,7,Laura Janssen,31,Female,Dutch,Hotel,1200,Train,200 +10,"Dubai, United Arab Emirates",3/10/2024,3/17/2024,7,Mohammed Ali,39,Male,Emirati,Resort,2500,Flight,800 +11,"Cancun, Mexico",4/1/2024,4/8/2024,7,Ana Hernandez,27,Female,Mexican,Hotel,1000,Flight,500 +12,"Barcelona, Spain",5/15/2024,5/22/2024,7,Carlos Garcia,36,Male,Spanish,Airbnb,800,Train,100 +13,"Honolulu, Hawaii",6/10/2024,6/18/2024,8,Lily Wong,29,Female,Chinese,Resort,3000,Flight,1200 +14,"Berlin, Germany",7/1/2024,7/10/2024,9,Hans Mueller,48,Male,German,Hotel,1400,Flight,700 +15,"Marrakech, Morocco",8/20/2024,8/27/2024,7,Fatima Khouri,26,Female,Moroccan,Riad,600,Flight,400 +16,"Edinburgh, Scotland",9/5/2024,9/12/2024,7,James MacKenzie,32,Male,Scottish,Hotel,900,Train,150 +17,Paris,9/1/2023,9/10/2023,9,Sarah Johnson,30,Female,American,Hotel,$900 ,Plane,$400 +18,Bali,8/15/2023,8/25/2023,10,Michael Chang,28,Male,Chinese,Resort,"$1,500 ",Plane,$700 +19,London,7/22/2023,7/28/2023,6,Olivia Rodriguez,35,Female,British,Hotel,"$1,200 ",Train,$150 +20,Tokyo,10/5/2023,10/15/2023,10,Kenji Nakamura,45,Male,Japanese,Hotel,"$1,200 ",Plane,$800 +21,New York,11/20/2023,11/25/2023,5,Emily Lee,27,Female,American,Airbnb,$600 ,Bus,$100 +22,Sydney,12/5/2023,12/12/2023,7,James Wilson,32,Male,Australian,Hotel,"$1,000 ",Plane,$600 +23,Rome,11/1/2023,11/8/2023,7,Sofia Russo,29,Female,Italian,Airbnb,$700 ,Train,$80 +24,Bangkok,9/15/2023,9/23/2023,8,Raj Patel,40,Male,Indian,Hostel,$400 ,Plane,$500 +25,Paris,12/22/2023,12/28/2023,6,Lily Nguyen,24,Female,Vietnamese,Hotel,"$1,400 ",Train,$100 +26,Hawaii,8/1/2023,8/10/2023,9,David Kim,34,Male,Korean,Resort,"$2,000 ",Plane,$800 +27,Barcelona,10/20/2023,10/28/2023,8,Maria Garcia,31,Female,Spanish,Hotel,"$1,100 ",Train,$150 +28,Japan,5/10/2022,5/18/2022,8,Alice Smith,30,Female,American,Hotel,$800 ,Plane,$500 +29,Thailand,6/15/2022,6/22/2022,7,Bob Johnson,45,Male,Canadian,Hostel,$200 ,Train,$150 +30,France,7/2/2022,7/11/2022,9,Charlie Lee,25,Male,Korean,Airbnb,$600 ,Car rental,$300 +31,Australia,8/20/2022,9/2/2022,13,Emma Davis,28,Female,British,Hotel,"$1,000 ",Car rental,$500 +32,Brazil,9/5/2022,9/14/2022,9,Olivia Martin,33,Female,Australian,Hostel,$150 ,Bus,$50 +33,Greece,10/12/2022,10/20/2022,8,Harry Wilson,20,Male,American,Airbnb,$400 ,Plane,$600 +34,Egypt,11/8/2022,11/15/2022,7,Sophia Lee,37,Female,Canadian,Hotel,$700 ,Train,$100 +35,Mexico,1/5/2023,1/15/2023,10,James Brown,42,Male,British,Airbnb,$500 ,Plane,$800 +36,Italy,2/14/2023,2/20/2023,6,Mia Johnson,31,Female,American,Hostel,$180 ,Train,$120 +37,Spain,3/23/2023,3/31/2023,8,William Davis,27,Male,Korean,Hotel,$900 ,Car rental,$400 +38,Canada,4/19/2023,4/26/2023,7,Amelia Brown,38,Female,Australian,Airbnb,$350 ,Bus,$75 +39,"Paris, France",6/12/2022,6/19/2022,7,Mia Johnson,25,Female,American,Hotel,1400,Plane,600 +40,"Sydney, Australia",1/2/2023,1/9/2023,7,Adam Lee,33,Male,Canadian,Airbnb,800,Train,150 +41,"Tokyo, Japan",12/10/2022,12/18/2022,8,Sarah Wong,28,Female,Chinese,Hostel,500,Plane,900 +42,"Cancun, Mexico",7/1/2023,7/8/2023,7,John Smith,45,Male,American,Resort,2200,Plane,800 +43,"Rio de Janeiro, Brazil",11/20/2022,11/27/2022,7,Maria Silva,30,Female,Brazilian,Hotel,1200,Plane,700 +44,"London, UK",3/5/2023,3/12/2023,7,Peter Brown,55,Male,British,Airbnb,900,Train,100 +45,"Barcelona, Spain",8/18/2023,8/25/2023,7,Emma Garcia,27,Female,Spanish,Hostel,600,Plane,600 +46,"New York City, USA",9/15/2022,9/22/2022,7,Michael Davis,41,Male,American,Hotel,1500,Plane,500 +47,"Bangkok, Thailand",5/1/2023,5/7/2023,6,Nina Patel,29,Female,Indian,Airbnb,500,Bus,50 +48,"Vancouver, Canada",7/10/2022,7/17/2022,7,Kevin Kim,24,Male,Korean,Hostel,400,Train,150 +49,"Amsterdam, Netherlands",6/20/2023,6/28/2023,8,Laura van den Berg,31,Female,Dutch,Hotel,1100,Plane,700 +50,"Paris, France",8/15/2023,8/22/2023,7,Jennifer Nguyen,31,Female,Canadian,Hotel,"$1,200 ",Train,$300 +51,"Tokyo, Japan",10/10/2023,10/20/2023,10,David Kim,25,Male,American,Hostel,$500 ,Bus,$100 +52,"Sydney, AUS",11/5/2023,11/12/2023,7,Rachel Lee,27,Female,South Korean,Airbnb,$900 ,Car rental,$200 +53,"New York, USA",12/24/2023,12/31/2023,7,Jessica Wong,28,Female,Canadian,Hotel,"$1,400 ",Flight,$800 +54,"Rio de Janeiro, Brazil",1/15/2024,1/24/2024,9,Felipe Almeida,30,Male,Brazilian,Airbnb,$800 ,Train,$150 +55,"Bangkok, Thailand",2/1/2024,2/9/2024,8,Nisa Patel,23,Female,Indian,Hostel,$400 ,Bus,$50 +56,"London, UK",3/15/2024,3/23/2024,8,Ben Smith,35,Male,British,Hotel,"$1,000 ",Train,$200 +57,"Barcelona, Spain",4/5/2024,4/13/2024,8,Laura Gomez,29,Female,Spanish,Airbnb,$700 ,Car rental,$250 +58,"Seoul, South Korea",5/10/2024,5/18/2024,8,Park Min Woo,27,Male,South Korean,Hostel,$500 ,Subway,$20 +59,"Los Angeles, USA",6/20/2024,6/27/2024,7,Michael Chen,26,Male,Chinese,Hotel,"$1,200 ",Car rental,$300 +60,"Rome, Italy",7/15/2024,7/23/2024,8,Sofia Rossi,33,Female,Italian,Airbnb,$800 ,Train,$100 +61,Paris,7/12/2022,7/18/2022,6,Rachel Sanders,35,Female,American,Hotel,1200,Plane,800 +62,Tokyo,9/3/2022,9/10/2022,7,Kenji Nakamura,28,Male,Japanese,Hostel,400,Train,300 +63,Cape Town,1/7/2023,1/16/2023,9,Emily Watson,29,Female,British,Vacation rental,800,Car rental,200 +64,Sydney,6/23/2023,6/29/2023,6,David Lee,43,Male,Australian,Hotel,1500,Plane,1200 +65,Barcelona,8/18/2023,8/25/2023,7,Ana Rodriguez,31,Female,Spanish,Vacation rental,900,Plane,700 +66,Bali,2/1/2024,2/8/2024,7,Tom Wilson,27,Male,American,Resort,2200,Plane,1000 +67,Paris,5/6/2024,5/12/2024,6,Olivia Green,39,Female,French,Hotel,1100,Train,200 +68,New York,7/20/2024,7/26/2024,6,James Chen,25,Male,American,Vacation rental,1000,Plane,800 +69,Bangkok,9/8/2024,9/16/2024,8,Lila Patel,33,Female,Indian,Hostel,300,Plane,700 +70,Rome,2/14/2025,2/20/2025,6,Marco Rossi,41,Male,Italian,Hotel,1300,Train,100 +71,Bali,5/21/2025,5/29/2025,8,Sarah Brown,37,Female,British,Resort,1800,Plane,1000 +72,,,,,,,,,,,, +73,"Bali, Indonesia",8/5/2022,8/12/2022,7,Sarah Lee,35,Female,South Korean,Resort,500 USD,Plane,800 USD +74,"Tokyo, Japan",1/1/2023,1/9/2023,8,Alex Kim,29,Male,American,Hotel,1000 USD,Train,200 USD +75,"Cancun, Mexico",4/15/2023,4/22/2023,7,Maria Hernandez,42,Female,Mexican,Resort,800 USD,Plane,500 USD +76,"Paris, France",6/7/2023,6/14/2023,7,John Smith,46,Male,British,Hotel,1200 USD,Plane,700 USD +77,"Cape Town, SA",9/1/2023,9/10/2023,9,Mark Johnson,31,Male,South African,Guesthouse,400 USD,Car,300 USD +78,"Bali, Indonesia",11/12/2023,11/19/2023,7,Amanda Chen,25,Female,Taiwanese,Resort,600 USD,Plane,700 USD +79,"Sydney, Aus",2/5/2024,2/12/2024,7,David Lee,38,Male,Australian,Hotel,900 USD,Plane,600 USD +80,"Bangkok, Thai",5/15/2024,5/22/2024,7,Nana Kwon,27,Female,Korean,Hotel,400 USD,Plane,400 USD +81,"New York, USA",8/20/2024,8/27/2024,7,Tom Hanks,60,Male,American,Hotel,1500 USD,Plane,1000 USD +82,"Phuket, Thai",1/1/2025,1/8/2025,7,Emma Watson,32,Female,British,Resort,700 USD,Plane,800 USD +83,"Rome, Italy",4/15/2025,4/22/2025,7,James Kim,41,Male,American,Hotel,100,, +84,Paris,6/15/2021,6/20/2021,6,John Smith,35,Male,American,Hotel,800 USD,Plane,500 USD +85,Tokyo,7/1/2021,7/10/2021,10,Sarah Lee,28,Female,Korean,Airbnb,500 USD,Train,300 USD +86,Bali,8/10/2021,8/20/2021,11,Maria Garcia,42,Female,Spanish,Resort,1200 USD,Plane,700 USD +87,Sydney,9/1/2021,9/10/2021,9,David Lee,45,Male,Australian,Hotel,900 USD,Plane,600 USD +88,New York,10/15/2021,10/20/2021,6,Emily Davis,31,Female,American,Airbnb,700 USD,Car rental,200 USD +89,London,11/20/2021,11/30/2021,11,James Wilson,29,Male,British,Hostel,300 USD,Plane,400 USD +90,Dubai,1/1/2022,1/8/2022,8,Fatima Ahmed,24,Female,Emirati,Hotel,1000 USD,Plane,800 USD +91,Bangkok,2/14/2022,2/20/2022,7,Liam Nguyen,26,Male,Vietnamese,Airbnb,400 USD,Train,100 USD +92,Rome,3/10/2022,3/20/2022,11,Giulia Rossi,30,Female,Italian,Hostel,200 USD,Plane,350 USD +93,Bali,4/15/2022,4/25/2022,11,Putra Wijaya,33,Male,Indonesian,Villa,1500 USD,Car rental,300 USD +94,Seoul,5/1/2022,5/10/2022,10,Kim Min-ji,27,Female,Korean,Hotel,800 USD,Train,150 USD +95,Paris,6/15/2022,6/20/2022,5,John Smith,35,Male,USA,Hotel,$500 ,Plane,$800 +96,Tokyo,9/1/2022,9/10/2022,9,Emily Johnson,28,Female,Canada,Airbnb,$400 ,Train,$200 +97,Sydney,11/23/2022,12/2/2022,9,David Lee,45,Male,South Korea,Hostel,$200 ,Plane,"$1,200 " +98,London,2/14/2023,2/19/2023,5,Sarah Brown,37,Female,UK,Hotel,$600 ,Plane,$700 +99,New York,5/8/2023,5/14/2023,6,Michael Wong,50,Male,China,Airbnb,$800 ,Car rental,$300 +100,Rome,8/20/2023,8/27/2023,7,Jessica Chen,31,Female,Taiwan,Hotel,$700 ,Plane,$900 +101,Bangkok,11/12/2023,11/20/2023,8,Ken Tanaka,42,Male,Japan,Hostel,$300 ,Train,$100 +102,Cape Town,1/6/2024,1/14/2024,8,Maria Garcia,27,Female,Spain,Airbnb,$500 ,Plane,"$1,500 " +103,Rio de Janeiro,4/3/2024,4/10/2024,7,Rodrigo Oliveira,33,Male,Brazil,Hotel,$900 ,Car rental,$400 +104,Bali,7/22/2024,7/28/2024,6,Olivia Kim,29,Female,South Korea,Villa,"$1,200 ",Plane,"$1,000 " +105,Amsterdam,10/10/2024,10/17/2024,7,Robert Mueller,41,Male,Germany,Hotel,$600 ,Train,$150 +106,Paris,5/15/2022,5/20/2022,5,John Smith,35,Male,USA,Hotel,1000,Plane,800 +107,Tokyo,9/1/2022,9/10/2022,9,Sarah Lee,28,Female,South Korea,Airbnb,800,Train,500 +108,New York,6/20/2022,6/25/2022,5,Michael Wong,42,Male,Hong Kong,Hotel,1200,Car rental,200 +109,Bali,8/12/2022,8/20/2022,8,Lisa Chen,30,Female,Taiwan,Resort,1500,Plane,1200 +110,Sydney,7/1/2022,7/10/2022,9,David Kim,26,Male,Canada,Hostel,300,Plane,900 +111,London,6/10/2022,6/15/2022,5,Emily Wong,38,Female,United Kingdom,Hotel,900,Train,150 +112,Phuket,9/5/2022,9/12/2022,7,Mark Tan,45,Male,Singapore,Villa,2000,Plane,700 +113,Rome,5/1/2022,5/8/2022,7,Emma Lee,31,Female,Italy,Hotel,1100,Train,250 +114,Santorini,7/15/2022,7/22/2022,7,George Chen,27,Male,Greece,Airbnb,1000,Ferry,150 +115,Dubai,8/25/2022,8/30/2022,5,Sophia Kim,29,Female,United Arab Emirates,Hotel,1500,Car rental,300 +116,Phnom Penh,9/10/2022,9/15/2022,5,Alex Ng,33,Male,Cambodia,Hostel,200,Plane,500 +117,"Tokyo, Japan",2/5/2022,2/14/2022,9,Alice Smith,32,Female,American,Hotel,1000,Plane,700 +118,"Paris, France",3/15/2022,3/22/2022,7,Bob Johnson,47,Male,Canadian,Hotel,1200,Train,500 +119,"Sydney, Aus",5/1/2022,5/12/2022,11,Cindy Chen,26,Female,Chinese,Airbnb,800,Plane,1000 +120,"Rome, Italy",6/10/2022,6/17/2022,7,David Lee,38,Male,Korean,Hotel,900,Train,400 +121,"Bali, Indonesia",7/20/2022,7/30/2022,10,Emily Kim,29,Female,Korean,Hostel,500,Plane,800 +122,"Cancun, Mexico",8/8/2022,8/16/2022,8,Frank Li,41,Male,American,Hotel,1300,Plane,600 +123,"Athens, Greece",9/20/2022,9/30/2022,10,Gina Lee,35,Female,Korean,Airbnb,700,Plane,900 +124,"Tokyo, Japan",10/5/2022,10/13/2022,8,Henry Kim,24,Male,Korean,Hotel,1200,Plane,700 +125,"Sydney, Aus",11/11/2022,11/21/2022,10,Isabella Chen,30,Female,Chinese,Airbnb,900,Plane,1000 +126,"Paris, France",12/24/2022,1/1/2023,8,Jack Smith,28,Male,American,Hostel,400,Plane,700 +127,"Bali, Indonesia",2/10/2023,2/18/2023,8,Katie Johnson,33,Female,Canadian,Hotel,800,Plane,800 +128,,,,,,,,,,,, +129,"Paris, France",5/1/2023,5/7/2023,6,John Doe,35,Male,American,Hotel,5000,Airplane,2500 +130,"Tokyo, Japan",5/15/2023,5/22/2023,7,Jane Smith,28,Female,British,Airbnb,7000,Train,1500 +131,"Cape Town, South Africa",6/1/2023,6/10/2023,9,Michael Johnson,45,Male,South African,Hostel,3000,Car,2000 +132,"Sydney, Australia",6/15/2023,6/21/2023,6,Sarah Lee,31,Female,Australian,Hotel,6000,Airplane,3000 +133,"Rome, Italy",7/1/2023,7/8/2023,7,David Kim,42,Male,Korean,Airbnb,4000,Train,1500 +134,"New York City, USA",7/15/2023,7/22/2023,7,Emily Davis,27,Female,American,Hotel,8000,Airplane,2500 +135,"Rio de Janeiro, Brazil",8/1/2023,8/10/2023,9,Jose Perez,37,Male,Brazilian,Hostel,2500,Car,2000 +136,"Vancouver, Canada",8/15/2023,8/21/2023,6,Emma Wilson,29,Female,Canadian,Hotel,5000,Airplane,3000 +137,"Bangkok, Thailand",9/1/2023,9/8/2023,7,Ryan Chen,34,Male,Chinese,Hostel,2000,Train,1000 +138,"Barcelona, Spain",9/15/2023,9/22/2023,7,Sofia Rodriguez,25,Female,Spanish,Airbnb,6000,Airplane,2500 +139,"Auckland, New Zealand",10/1/2023,10/8/2023,7,William Brown,39,Male,New Zealander,Hotel,7000,Train,2500 diff --git a/notebooks/anaconda_projects/db/project_filebrowser.db b/notebooks/anaconda_projects/db/project_filebrowser.db new file mode 100644 index 00000000..3fa3a4a0 Binary files /dev/null and b/notebooks/anaconda_projects/db/project_filebrowser.db differ diff --git a/notebooks/analysis_travel_trip.ipynb b/notebooks/analysis_travel_trip.ipynb new file mode 100644 index 00000000..471ee7b7 --- /dev/null +++ b/notebooks/analysis_travel_trip.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d59f052-76ef-41c8-ae22-d76d2e3f7cb1", + "metadata": {}, + "source": [ + "# Calling the cleaned dataframe from the respective notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f1d024cc-bea2-4cb3-bb4d-fb7fa0c48b1b", + "metadata": 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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_clean
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...1200Flight600.02023520235MayMayUnited States
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...800Flight500.02023620236JuneJuneCanada
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...1000Flight700.02023720237JulyJulySouth Korea
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2000Flight1000.02023820238AugustAugustUnited Kingdom
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...700Train200.02023920239SeptemberSeptemberVietnamese
..................................................................
132Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...2500Private Car2000.02023820238AugustAugustBrazil
133Vancouver, Canada136CanadaVancouver2023-08-012023-08-016.0emma wilson29.0Female...5000Airplane3000.02023820238AugustAugustCanada
134Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...2000Train1000.02023920239SeptemberSeptemberChina
135Barcelona, Spain138SpainBarcelona2023-09-012023-09-017.0sofia rodriguez25.0Female...6000Airplane2500.02023920239SeptemberSeptemberSpain
136Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...7000Train2500.0202310202310OctoberOctoberNew Zealander
\n", + "

137 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "132 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "133 Vancouver, Canada 136 Canada Vancouver \n", + "134 Bangkok, Thailand 137 Thailand Bangkok \n", + "135 Barcelona, Spain 138 Spain Barcelona \n", + "136 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-01 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-01 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-01 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "132 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "133 2023-08-01 2023-08-01 6.0 emma wilson 29.0 \n", + "134 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "135 2023-09-01 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "136 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_cost transportation_type \\\n", + "0 Male ... 1200 Flight \n", + "1 Female ... 800 Flight \n", + "2 Male ... 1000 Flight \n", + "3 Female ... 2000 Flight \n", + "4 Female ... 700 Train \n", + ".. ... ... ... ... \n", + "132 Male ... 2500 Private Car \n", + "133 Female ... 5000 Airplane \n", + "134 Male ... 2000 Train \n", + "135 Female ... 6000 Airplane \n", + "136 Male ... 7000 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600.0 2023 5 2023 5 \n", + "1 500.0 2023 6 2023 6 \n", + "2 700.0 2023 7 2023 7 \n", + "3 1000.0 2023 8 2023 8 \n", + "4 200.0 2023 9 2023 9 \n", + ".. ... ... ... ... ... \n", + "132 2000.0 2023 8 2023 8 \n", + "133 3000.0 2023 8 2023 8 \n", + "134 1000.0 2023 9 2023 9 \n", + "135 2500.0 2023 9 2023 9 \n", + "136 2500.0 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name traveler_nationality_clean \n", + "0 May May United States \n", + "1 June June Canada \n", + "2 July July South Korea \n", + "3 August August United Kingdom \n", + "4 September September Vietnamese \n", + ".. ... ... ... \n", + "132 August August Brazil \n", + "133 August August Canada \n", + "134 September September China \n", + "135 September September Spain \n", + "136 October October New Zealander \n", + "\n", + "[137 rows x 22 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_complete_df = pd.read_parquet(\"../data/clean/travel_trip_complete.parquet\")\n", + "travel_trip_complete_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ec9b9e2-f3f1-4f73-a64c-473495fc02b3", + "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/analysis_travel_trip_christos.ipynb b/notebooks/analysis_travel_trip_christos.ipynb new file mode 100644 index 00000000..08d14cc4 --- /dev/null +++ b/notebooks/analysis_travel_trip_christos.ipynb @@ -0,0 +1,4109 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d59f052-76ef-41c8-ae22-d76d2e3f7cb1", + "metadata": {}, + "source": [ + "# Calling the cleaned dataframe from the respective notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "f1d024cc-bea2-4cb3-bb4d-fb7fa0c48b1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_clean
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...1200Flight600.02023520235MayMayUnited States
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...800Flight500.02023620236JuneJuneCanada
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...1000Flight700.02023720237JulyJulySouth Korea
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2000Flight1000.02023820238AugustAugustUnited Kingdom
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...700Train200.02023920239SeptemberSeptemberVietnamese
..................................................................
132Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...2500Private Car2000.02023820238AugustAugustBrazil
133Vancouver, Canada136CanadaVancouver2023-08-012023-08-016.0emma wilson29.0Female...5000Airplane3000.02023820238AugustAugustCanada
134Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...2000Train1000.02023920239SeptemberSeptemberChina
135Barcelona, Spain138SpainBarcelona2023-09-012023-09-017.0sofia rodriguez25.0Female...6000Airplane2500.02023920239SeptemberSeptemberSpain
136Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...7000Train2500.0202310202310OctoberOctoberNew Zealander
\n", + "

137 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "132 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "133 Vancouver, Canada 136 Canada Vancouver \n", + "134 Bangkok, Thailand 137 Thailand Bangkok \n", + "135 Barcelona, Spain 138 Spain Barcelona \n", + "136 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-01 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-01 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-01 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "132 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "133 2023-08-01 2023-08-01 6.0 emma wilson 29.0 \n", + "134 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "135 2023-09-01 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "136 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_cost transportation_type \\\n", + "0 Male ... 1200 Flight \n", + "1 Female ... 800 Flight \n", + "2 Male ... 1000 Flight \n", + "3 Female ... 2000 Flight \n", + "4 Female ... 700 Train \n", + ".. ... ... ... ... \n", + "132 Male ... 2500 Private Car \n", + "133 Female ... 5000 Airplane \n", + "134 Male ... 2000 Train \n", + "135 Female ... 6000 Airplane \n", + "136 Male ... 7000 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600.0 2023 5 2023 5 \n", + "1 500.0 2023 6 2023 6 \n", + "2 700.0 2023 7 2023 7 \n", + "3 1000.0 2023 8 2023 8 \n", + "4 200.0 2023 9 2023 9 \n", + ".. ... ... ... ... ... \n", + "132 2000.0 2023 8 2023 8 \n", + "133 3000.0 2023 8 2023 8 \n", + "134 1000.0 2023 9 2023 9 \n", + "135 2500.0 2023 9 2023 9 \n", + "136 2500.0 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name traveler_nationality_clean \n", + "0 May May United States \n", + "1 June June Canada \n", + "2 July July South Korea \n", + "3 August August United Kingdom \n", + "4 September September Vietnamese \n", + ".. ... ... ... \n", + "132 August August Brazil \n", + "133 August August Canada \n", + "134 September September China \n", + "135 September September Spain \n", + "136 October October New Zealander \n", + "\n", + "[137 rows x 22 columns]" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_complete_df = pd.read_parquet(\"../data/clean/travel_trip_complete.parquet\")\n", + "travel_trip_complete_christos_df = travel_trip_complete_df.copy()\n", + "travel_trip_complete_christos_df" + ] + }, + { + "cell_type": "markdown", + "id": "5244f8d1-9425-4b55-b3fe-60951e491f36", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Adding continent information to my existing table, to check whether a trip is intercontinental vs intra-continental" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "31e91ee8-1c9d-4c42-9674-d05795749ec1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pycountry-convert in /opt/anaconda3/lib/python3.13/site-packages (0.7.2)\n", + "Requirement already satisfied: pprintpp>=0.3.0 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (0.4.0)\n", + "Requirement already satisfied: pycountry>=16.11.27.1 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (24.6.1)\n", + "Requirement already satisfied: pytest>=3.4.0 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (8.3.4)\n", + "Requirement already satisfied: pytest-mock>=1.6.3 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (3.15.1)\n", + "Requirement already satisfied: pytest-cov>=2.5.1 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (7.0.0)\n", + "Requirement already satisfied: repoze.lru>=0.7 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (0.7)\n", + "Requirement already satisfied: wheel>=0.30.0 in /opt/anaconda3/lib/python3.13/site-packages (from pycountry-convert) (0.45.1)\n", + "Requirement already satisfied: iniconfig in /opt/anaconda3/lib/python3.13/site-packages (from pytest>=3.4.0->pycountry-convert) (1.1.1)\n", + "Requirement already satisfied: packaging in /opt/anaconda3/lib/python3.13/site-packages (from pytest>=3.4.0->pycountry-convert) (24.2)\n", + "Requirement already satisfied: pluggy<2,>=1.5 in /opt/anaconda3/lib/python3.13/site-packages (from pytest>=3.4.0->pycountry-convert) (1.5.0)\n", + "Requirement already satisfied: coverage>=7.10.6 in /opt/anaconda3/lib/python3.13/site-packages (from coverage[toml]>=7.10.6->pytest-cov>=2.5.1->pycountry-convert) (7.13.0)\n" + ] + } + ], + "source": [ + "!pip install pycountry-convert" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "017e9c38-ca71-4710-9e36-04a84727f3b3", + "metadata": {}, + "outputs": [], + "source": [ + "import pycountry\n", + "import pycountry_convert as pc\n", + "import pandas as pd\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "65226305-f8f3-4166-af82-60d4ad81cb34", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import pycountry\n", + "import pycountry_convert as pc\n", + "\n", + "def country_to_alpha2(country_name: str):\n", + " if pd.isna(country_name):\n", + " return None\n", + " try:\n", + " return pycountry.countries.lookup(country_name).alpha_2\n", + " except LookupError:\n", + " return None\n", + "\n", + "def alpha2_to_continent(alpha2: str):\n", + " if alpha2 is None:\n", + " return None\n", + " try:\n", + " continent_code = pc.country_alpha2_to_continent_code(alpha2)\n", + " return pc.convert_continent_code_to_continent_name(continent_code)\n", + " except KeyError:\n", + " return None\n", + "\n", + "def country_to_continent(country_name: str):\n", + " alpha2 = country_to_alpha2(country_name)\n", + " return alpha2_to_continent(alpha2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f43afe4d-9efc-4711-acec-78edc6da2fb1", + "metadata": {}, + "outputs": [], + "source": [ + "travel_trip_complete_christos_df[\"traveler_nationality_continent\"] = travel_trip_complete_christos_df[\"traveler_nationality_clean\"].apply(country_to_continent)\n", + "travel_trip_complete_christos_df[\"destination_continent\"] = travel_trip_complete_christos_df[\"destination_country\"].apply(country_to_continent)\n", + "\n", + "travel_trip_complete_christos_df[\"is_intercontinental_trip\"] = (\n", + " travel_trip_complete_christos_df[\"traveler_nationality_continent\"] != travel_trip_complete_christos_df[\"destination_continent\"]\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c03ba4b1-c31d-4b9b-8aa2-3c90ed14c13f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...end_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_cleantraveler_nationality_continentdestination_continentis_intercontinental_trip
132Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...2023820238AugustAugustBrazilSouth AmericaSouth AmericaFalse
133Vancouver, Canada136CanadaVancouver2023-08-012023-08-016.0emma wilson29.0Female...2023820238AugustAugustCanadaNorth AmericaNorth AmericaFalse
134Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...2023920239SeptemberSeptemberChinaAsiaAsiaFalse
135Barcelona, Spain138SpainBarcelona2023-09-012023-09-017.0sofia rodriguez25.0Female...2023920239SeptemberSeptemberSpainEuropeEuropeFalse
136Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...202310202310OctoberOctoberNew ZealanderNoneOceaniaTrue
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5 rows × 25 columns

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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "132 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "133 Vancouver, Canada 136 Canada Vancouver \n", + "134 Bangkok, Thailand 137 Thailand Bangkok \n", + "135 Barcelona, Spain 138 Spain Barcelona \n", + "136 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "132 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "133 2023-08-01 2023-08-01 6.0 emma wilson 29.0 \n", + "134 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "135 2023-09-01 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "136 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... end_year end_month start_year start_month \\\n", + "132 Male ... 2023 8 2023 8 \n", + "133 Female ... 2023 8 2023 8 \n", + "134 Male ... 2023 9 2023 9 \n", + "135 Female ... 2023 9 2023 9 \n", + "136 Male ... 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name traveler_nationality_clean \\\n", + "132 August August Brazil \n", + "133 August August Canada \n", + "134 September September China \n", + "135 September September Spain \n", + "136 October October New Zealander \n", + "\n", + " traveler_nationality_continent destination_continent \\\n", + "132 South America South America \n", + "133 North America North America \n", + "134 Asia Asia \n", + "135 Europe Europe \n", + "136 None Oceania \n", + "\n", + " is_intercontinental_trip \n", + "132 False \n", + "133 False \n", + "134 False \n", + "135 False \n", + "136 True \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df.tail(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f9b871ab-9f15-4c4e-b717-b7e7c1be81d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(71)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df.destination_country.value_counts().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6ee1b5c8-bd17-4583-94aa-ca9af54ca200", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we need to fill the NaN values in the destination_country column\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "# use only rows where country is known\n", + "city_country_map = (\n", + " df.dropna(subset=[\"destination_country\"])\n", + " .groupby(\"destination_city\")[\"destination_country\"]\n", + " .agg(lambda s: s.mode().iloc[0]) # most frequent country per city\n", + " .to_dict()\n", + ")\n", + "\n", + "len(city_country_map) # just to inspect how many cities you mapped\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "38545241-78c5-4fe0-8eeb-5b350a93bf48", + "metadata": {}, + "outputs": [], + "source": [ + "mask_missing_country = df[\"destination_country\"].isna()\n", + "\n", + "df.loc[mask_missing_country, \"destination_country\"] = (\n", + " df.loc[mask_missing_country, \"destination_city\"]\n", + " .map(city_country_map)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "4252bd75-5744-4959-b4cc-bef2e155fce9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(26)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"destination_continent\"] = df[\"destination_country\"].apply(country_to_continent)\n", + "\n", + "df[\"destination_continent\"].isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0570bd59-cb41-4967-b858-2bf253425715", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination_city destination_country\n", + "London UK 7\n", + "Phuket Thai 2\n", + "Bangkok Thai 1\n", + "Honolulu Hawaii 1\n", + "Edinburgh Scotland 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[df[\"destination_continent\"].isna(), [\"destination_city\", \"destination_country\"]].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "4f33ba78-0d92-42b6-be37-a6d06b2a0da7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(14)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = travel_trip_complete_christos_df\n", + "\n", + "# manual mapping for the remaining weird country labels\n", + "manual_country_fix_2 = {\n", + " \"UK\": \"United Kingdom\",\n", + " \"Thai\": \"Thailand\",\n", + " \"Hawaii\": \"United States\",\n", + " \"Scotland\": \"United Kingdom\",\n", + "}\n", + "\n", + "# only touch the rows that still have no continent\n", + "mask_missing_continent = df[\"destination_continent\"].isna()\n", + "\n", + "df.loc[mask_missing_continent, \"destination_country\"] = (\n", + " df.loc[mask_missing_continent, \"destination_country\"]\n", + " .replace(manual_country_fix_2)\n", + ")\n", + "\n", + "# recompute continent\n", + "df[\"destination_continent\"] = df[\"destination_country\"].apply(country_to_continent)\n", + "\n", + "# check remaining NaNs\n", + "df[\"destination_continent\"].isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "492c7a34-a026-41e7-8e4e-5ff38a5d8e6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(14)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# how many missing in destination_country\n", + "df[\"destination_country\"].isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6c30d546-b773-42c4-b33f-d8a2782dc641", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination_city\n", + "Hawaii 1\n", + "Japan 1\n", + "Thailand 1\n", + "France 1\n", + "Australia 1\n", + "Brazil 1\n", + "Greece 1\n", + "Egypt 1\n", + "Mexico 1\n", + "Italy 1\n", + "Spain 1\n", + "Canada 1\n", + "Santorini 1\n", + "Phnom Penh 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc[df[\"destination_country\"].isna(), \"destination_city\"].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "f0ada346-a4f6-4fa1-89ed-48a5e5ea6ae7", + "metadata": {}, + "outputs": [], + "source": [ + "manual_city_to_country_fix = {\n", + " \"Hawaii\": \"United States\",\n", + " \"Japan\": \"Japan\",\n", + " \"Thailand\": \"Thailand\",\n", + " \"France\": \"France\",\n", + " \"Australia\": \"Australia\",\n", + " \"Brazil\": \"Brazil\",\n", + " \"Greece\": \"Greece\",\n", + " \"Egypt\": \"Egypt\",\n", + " \"Mexico\": \"Mexico\",\n", + " \"Italy\": \"Italy\",\n", + " \"Spain\": \"Spain\",\n", + " \"Canada\": \"Canada\",\n", + " \"Santorini\": \"Greece\", # island in Greece\n", + " \"Phnom Penh\": \"Cambodia\", # capital of Cambodia\n", + "}\n", + "\n", + "mask = df[\"destination_country\"].isna()\n", + "df.loc[mask, \"destination_country\"] = (\n", + " df.loc[mask, \"destination_city\"].map(manual_city_to_country_fix)\n", + ")\n", + "\n", + "df[\"destination_continent\"] = df[\"destination_country\"].apply(country_to_continent)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "3c9c573e-e8aa-4365-8cd0-1da465a3a96b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"destination_country\"].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ac712d0f-ea8e-4d8b-87bf-2c40321f361a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "destination_continent\n", + "Europe 47\n", + "Asia 46\n", + "North America 21\n", + "Oceania 14\n", + "South America 6\n", + "Africa 3\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "df = travel_trip_complete_christos_df # if not already\n", + "\n", + "# recompute destination_continent from the now-clean destination_country\n", + "df[\"destination_continent\"] = df[\"destination_country\"].apply(country_to_continent)\n", + "\n", + "# quick sanity check\n", + "print(df[\"destination_continent\"].isna().sum())\n", + "print(df[\"destination_continent\"].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89e88e98-8135-4c4c-bfb0-e9706a70c467", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c7c153f6-8263-4449-b72c-fc30dd718915", + "metadata": {}, + "outputs": [], + "source": [ + "# now we do the same with traveler_nationality_continent" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "a74c9027-e989-471f-971e-f95645c37060", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_nationality\n", + "Australian 8\n", + "Indian 4\n", + "Vietnamese 3\n", + "Dutch 2\n", + "Mexican 2\n", + "South African 2\n", + "Moroccan 1\n", + "French 1\n", + "Indonesian 1\n", + "New Zealander 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_nat = (\n", + " travel_trip_complete_christos_df\n", + " .loc[travel_trip_complete_christos_df[\"traveler_nationality_continent\"].isna(), \"traveler_nationality\"]\n", + " .dropna()\n", + " .value_counts()\n", + ")\n", + "missing_nat\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "c65caa55-0a1e-4def-8bf6-418ad6738037", + "metadata": {}, + "outputs": [], + "source": [ + "manual_nat_fix = {\n", + " \"Australian\": \"Australia\",\n", + " \"Indian\": \"India\",\n", + " \"Vietnamese\": \"Vietnam\",\n", + " \"Dutch\": \"Netherlands\",\n", + " \"Mexican\": \"Mexico\",\n", + " \"South African\": \"South Africa\",\n", + " \"Moroccan\": \"Morocco\",\n", + " \"French\": \"France\",\n", + " \"Indonesian\": \"Indonesia\",\n", + " \"New Zealander\": \"New Zealand\",\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "2b715f1c-2757-4321-b8bc-fa6b2a7a58aa", + "metadata": {}, + "outputs": [], + "source": [ + "df = travel_trip_complete_christos_df # just shorter name\n", + "\n", + "# clean nationality\n", + "df[\"traveler_nationality_clean\"] = (\n", + " df[\"traveler_nationality\"]\n", + " .astype(\"string\")\n", + " .str.strip()\n", + " .replace(manual_nat_fix)\n", + ")\n", + "\n", + "# recompute continents ONLY from the cleaned text\n", + "df[\"traveler_nationality_continent\"] = df[\"traveler_nationality_clean\"].apply(country_to_continent)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "48970663-82ab-4b2e-a106-b89f4f5c4ada", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "91\n", + "traveler_nationality_continent\n", + "Asia 19\n", + "Oceania 9\n", + "Europe 8\n", + "North America 6\n", + "Africa 3\n", + "South America 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "print(df[\"traveler_nationality_continent\"].isna().sum())\n", + "print(df[\"traveler_nationality_continent\"].value_counts())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d8c5b4fc-61d8-483c-a4f9-33d38aaae311", + "metadata": {}, + "outputs": [], + "source": [ + "more_nat_fix = {\n", + " \"American\": \"United States\",\n", + " \"Korean\": \"South Korea\", # ambiguous, but common usage\n", + " \"British\": \"United Kingdom\",\n", + " \"Canadian\": \"Canada\",\n", + " \"Spanish\": \"Spain\",\n", + " \"Chinese\": \"China\",\n", + " \"Brazilian\": \"Brazil\",\n", + " \"Italian\": \"Italy\",\n", + " \"South Korean\": \"South Korea\",\n", + " \"Emirati\": \"United Arab Emirates\",\n", + " \"Japanese\": \"Japan\",\n", + " \"Scottish\": \"United Kingdom\",\n", + " \"German\": \"Germany\",\n", + " \"Taiwanese\": \"Taiwan\",\n", + " \"UK\": \"United Kingdom\",\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "539f09fd-43cf-4a6d-b1da-3812a0ac63f1", + "metadata": {}, + "outputs": [], + "source": [ + "manual_nat_fix.update(more_nat_fix)\n", + "\n", + "df[\"traveler_nationality_clean\"] = (\n", + " df[\"traveler_nationality\"]\n", + " .astype(\"string\")\n", + " .str.strip()\n", + " .replace(manual_nat_fix)\n", + ")\n", + "\n", + "df[\"traveler_nationality_continent\"] = (\n", + " df[\"traveler_nationality_clean\"].apply(country_to_continent)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "74be564d-4e21-4b47-ae32-37295c021391", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"traveler_nationality_continent\"].isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3720d48e-5dcd-45b0-b930-05b7a60b96f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(137)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"destination_continent\"].value_counts().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "17be7a7f-3e3d-41df-b5a0-bb6bfa68423d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "is_intercontinental_trip\n", + "False 81\n", + "True 56\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[\"is_intercontinental_trip\"] = (\n", + " df[\"traveler_nationality_continent\"] != df[\"destination_continent\"]\n", + ")\n", + "\n", + "df[\"is_intercontinental_trip\"].value_counts()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "08c99e5b-6a64-409d-bb37-45e15036434e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...end_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_cleantraveler_nationality_continentdestination_continentis_intercontinental_trip
0London, UK1United KingdomLondon2023-05-012023-05-017.0john smith35.0Male...2023520235MayMayUnited StatesNorth AmericaEuropeTrue
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...2023620236JuneJuneCanadaNorth AmericaAsiaTrue
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...2023720237JulyJulySouth KoreaAsiaAsiaFalse
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2023820238AugustAugustUnited KingdomEuropeNorth AmericaTrue
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...2023920239SeptemberSeptemberVietnamAsiaAsiaFalse
\n", + "

5 rows × 25 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city start_date \\\n", + "0 London, UK 1 United Kingdom London 2023-05-01 \n", + "1 Phuket, Thailand 2 Thailand Phuket 2023-06-01 \n", + "2 Bali, Indonesia 3 Indonesia Bali 2023-07-01 \n", + "3 New York, USA 4 USA New York 2023-08-01 \n", + "4 Tokyo, Japan 5 Japan Tokyo 2023-09-01 \n", + "\n", + " end_date duration_days traveler_name traveler_age traveler_gender ... \\\n", + "0 2023-05-01 7.0 john smith 35.0 Male ... \n", + "1 2023-06-01 5.0 jane doe 28.0 Female ... \n", + "2 2023-07-01 7.0 david lee 45.0 Male ... \n", + "3 2023-08-01 14.0 sarah johnson 29.0 Female ... \n", + "4 2023-09-01 7.0 kim nguyen 26.0 Female ... \n", + "\n", + " end_year end_month start_year start_month start_month_name \\\n", + "0 2023 5 2023 5 May \n", + "1 2023 6 2023 6 June \n", + "2 2023 7 2023 7 July \n", + "3 2023 8 2023 8 August \n", + "4 2023 9 2023 9 September \n", + "\n", + " end_month_name traveler_nationality_clean traveler_nationality_continent \\\n", + "0 May United States North America \n", + "1 June Canada North America \n", + "2 July South Korea Asia \n", + "3 August United Kingdom Europe \n", + "4 September Vietnam Asia \n", + "\n", + " destination_continent is_intercontinental_trip \n", + "0 Europe True \n", + "1 Asia True \n", + "2 Asia False \n", + "3 North America True \n", + "4 Asia False \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df = df\n", + "travel_trip_complete_christos_df.head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "f8853150-3e86-4ba0-93da-38adffffcd38", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Creating traveler_age clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "8919d9a1-21af-49f9-94e5-2f93822eb00d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_age_segment\n", + "26-33 Young Professionals 72\n", + "34-40 Young Parents 25\n", + "41-50 Money & Energy Group 25\n", + "18-25 Students-Early Professionals 13\n", + "51-60 Rich but Tired 2\n", + "61+ Retired-Elderly 0\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "age_bins = [0, 26, 34, 41, 51, 61, 120]\n", + "age_labels = [\n", + " \"18-25 Students-Early Professionals\", # [0,26) -> effectively 18–25 in your data\n", + " \"26-33 Young Professionals\", # [26,34)\n", + " \"34-40 Young Parents\", # [34,41)\n", + " \"41-50 Money & Energy Group\", # [41,51)\n", + " \"51-60 Rich but Tired\", # [51,61)\n", + " \"61+ Retired-Elderly\" # [61,120)\n", + "]\n", + "\n", + "travel_trip_complete_christos_df[\"traveler_age_segment\"] = pd.cut(\n", + " travel_trip_complete_christos_df[\"traveler_age\"],\n", + " bins=age_bins,\n", + " labels=age_labels,\n", + " right=False\n", + ")\n", + "\n", + "travel_trip_complete_christos_df[\"traveler_age_segment\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "622c53e2-ee32-4135-a665-8fbb863fcad0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df[\"traveler_age_segment\"].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "51e12e89-7c10-4cd8-8878-fe5a3577d594", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "destination_continent\n", + "Europe 47\n", + "Asia 46\n", + "North America 21\n", + "Oceania 14\n", + "South America 6\n", + "Africa 3\n", + "Name: count, dtype: int64\n" + ] + }, + { + "data": { + "text/html": [ + "
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destination_cityduration_daystraveler_agetraveler_age_segment
14Marrakech7.026.026-33 Young Professionals
33Egypt7.037.034-40 Young Parents
128Cape Town9.045.041-50 Money & Energy Group
\n", + "
" + ], + "text/plain": [ + " destination_city duration_days traveler_age traveler_age_segment\n", + "14 Marrakech 7.0 26.0 26-33 Young Professionals\n", + "33 Egypt 7.0 37.0 34-40 Young Parents\n", + "128 Cape Town 9.0 45.0 41-50 Money & Energy Group" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = travel_trip_complete_christos_df\n", + "\n", + "print(df[\"destination_continent\"].value_counts())\n", + "\n", + "df[df[\"destination_continent\"] == \"Africa\"][\n", + " [\"destination_city\", \"duration_days\", \"traveler_age\", \"traveler_age_segment\"]\n", + "]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "7e081546-6fe8-4a5a-9088-cf6a55ac4f8f", + "metadata": {}, + "outputs": [], + "source": [ + "# exporting the enhanced table\n", + "import os\n", + "os.makedirs(\"../data/clean\", exist_ok=True)\n", + "\n", + "# 1) Human / sharing version\n", + "travel_trip_complete_christos_df.to_csv(\"../data/clean/travel_trip_enhanced.csv\", index=False)\n", + "\n", + "# 2) Analysis version (keeps dtypes)\n", + "travel_trip_complete_christos_df.to_parquet(\"../data/clean/travel_trip_enhanced.parquet\", index=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e2879df4-fc87-49ec-ba32-12af07d54190", + "metadata": {}, + "source": [ + "# Setting up some default colouring and ordering functions for my plots later" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "aeb40de2-ba6e-45e1-bda2-be5f632edc73", + "metadata": {}, + "outputs": [], + "source": [ + "# brand colours\n", + "brand_palette = [\"#b22d3e\", \"#026453\", \"#ddac34\", \"#1480d8\"]\n", + "\n", + "sns.set_theme(style=\"whitegrid\") # or other style\n", + "sns.set_palette(brand_palette)\n", + "\n", + "plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(color=brand_palette)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "029a23bd-5e71-49b5-9bb5-696073bc5e0e", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from pandas.api.types import CategoricalDtype\n", + "\n", + "age_order = [\n", + " \"18-25 Students-Early Professionals\",\n", + " \"26-33 Young Professionals\",\n", + " \"34-40 Young Parents\",\n", + " \"41-50 Money & Energy Group\",\n", + " \"51-60 Rich but Tired\",\n", + " \"61+ Retired-Elderly\",\n", + "]\n", + "\n", + "age_cat = CategoricalDtype(categories=age_order, ordered=True)\n", + "\n", + "travel_trip_complete_christos_df[\"traveler_age_segment\"] = (\n", + " travel_trip_complete_christos_df[\"traveler_age_segment\"]\n", + " .astype(age_cat)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "cc105ad8-6e4d-4287-a7c9-6da88ee215e5", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Hypothesis testing" + ] + }, + { + "cell_type": "markdown", + "id": "6abd12a6-7d8d-4fab-83b7-79d6323ebb5d", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Hypothesis #1: Seasonality patterns vary between continents Europe and Asia" + ] + }, + { + "cell_type": "markdown", + "id": "c4bcd33c-b780-4252-a9a5-ffbdcea8451c", + "metadata": {}, + "source": [ + "To do that we need to use the series:\n", + "- start_month_name\n", + "- destination_continent\n", + "\n", + "What we are going to look is whether different continents follow a different \"demand curve\" across the 12 months of the year. Also, we are going to examine monthly standard deviation and autocorrelation.\n", + "\n", + "Interpretation: if confirmed, campaigns might be scheduled per continet.\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0ff3b886-50b3-49ab-8120-d133f5262c1d", + "metadata": {}, + "outputs": [], + "source": [ + "# we need to a pivot table, with index by destination_continent, columns the months fo the year, and values the count() of the trips' month" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1e898d8f-a22b-4616-b84b-9b2b5bd1a51b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination_continent start_month_name\n", + "Europe April 2\n", + " August 4\n", + " December 2\n", + " February 4\n", + " July 7\n", + " June 7\n", + " March 5\n", + " May 6\n", + " November 2\n", + " October 4\n", + " September 4\n", + "Asia April 1\n", + " August 5\n", + " December 1\n", + " February 5\n", + " January 5\n", + " July 4\n", + " June 2\n", + " March 1\n", + " May 7\n", + " November 2\n", + " October 3\n", + " September 10\n", + "Name: start_month_name, dtype: int64" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hypothesis_1_group_df = travel_trip_complete_christos_df.groupby([\"destination_continent\", \"start_month_name\"])[\"start_month_name\"].count()\n", + "hypothesis_1_group__filtered_df = hypothesis_1_group_df.loc[[\"Europe\", \"Asia\"]]\n", + "hypothesis_1_group__filtered_df" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "16fe16df-098e-4397-8ce4-cb5091dbbfcb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import calendar\n", + "\n", + "# aggregate only Europe & Asia, by continent + numeric month\n", + "hyp1 = (\n", + " travel_trip_complete_christos_df\n", + " .query(\"destination_continent in ['Europe', 'Asia']\")\n", + " .groupby([\"destination_continent\", \"start_month\"])\n", + " .size()\n", + " .reset_index(name=\"trip_count\")\n", + ")\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "\n", + "for continent in [\"Europe\", \"Asia\"]:\n", + " tmp = hyp1[hyp1[\"destination_continent\"] == continent].sort_values(\"start_month\")\n", + "\n", + " # bars\n", + " ax.bar(\n", + " tmp[\"start_month\"] + (0.15 if continent == \"Asia\" else -0.15), # small shift\n", + " tmp[\"trip_count\"],\n", + " width=0.3,\n", + " label=f\"{continent} trips\",\n", + " alpha=0.6,\n", + " )\n", + "\n", + " # line on top of bars (seasonality curve)\n", + " ax.plot(\n", + " tmp[\"start_month\"],\n", + " tmp[\"trip_count\"],\n", + " marker=\"o\",\n", + " linestyle=\"-\",\n", + " label=f\"{continent} trend\",\n", + " )\n", + "\n", + "ax.set_xticks(range(1, 13))\n", + "ax.set_xticklabels(calendar.month_abbr[1:13])\n", + "ax.set_xlabel(\"Start month\")\n", + "ax.set_ylabel(\"Number of trips\")\n", + "ax.set_title(\"Seasonality of trips: Europe vs Asia\")\n", + "ax.legend()\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "677c7225-f6f7-4e70-b5f1-c666a606a6a7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# now I want now to see how seasonality differs in Europe and Asia, only for traveler_age_segment \"Young professionals\" which as we see later, is the segment that travel the most in these two continets.\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "# keep only this age group\n", + "df_sub = df[df[\"traveler_age_segment\"] == \"26-33 Young Professionals\"]\n", + "\n", + "# focus on Europe and Asia\n", + "df_sub = df_sub[df_sub[\"destination_continent\"].isin([\"Europe\",\"Asia\"])]\n", + "\n", + "# monthly trip counts\n", + "monthly = (\n", + " df_sub.groupby([\"destination_continent\",\"start_month\"])\n", + " .size()\n", + " .reset_index(name=\"trip_count\")\n", + " .sort_values([\"destination_continent\",\"start_month\"])\n", + ")\n", + "\n", + "# brand palette\n", + "brand_palette = [\"#b22d3e\",\"#026453\",\"#ddac34\",\"#1480d8\"]\n", + "sns.set_theme(style=\"whitegrid\")\n", + "plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(color=brand_palette)\n", + "\n", + "plt.figure(figsize=(8,4))\n", + "sns.lineplot(\n", + " data=monthly,\n", + " x=\"start_month\",\n", + " y=\"trip_count\",\n", + " hue=\"destination_continent\",\n", + " marker=\"o\"\n", + ")\n", + "\n", + "plt.xlabel(\"Start month\")\n", + "plt.ylabel(\"Trip count\")\n", + "plt.title(\"Seasonality: Europe vs Asia (Young Professionals)\")\n", + "plt.xticks(range(1,13))\n", + "plt.tight_layout()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "6bd1468f-6794-417a-8a8f-6c68984aae78", + "metadata": {}, + "source": [ + "### Hypothesis #2: Different traveler demographics (age, nationality) show different destination and duration preferences." + ] + }, + { + "cell_type": "markdown", + "id": "fdb17de6-2689-4e48-9b7d-be30b4ece04b", + "metadata": {}, + "source": [ + "To do that we need to use series:\n", + "- traveler_age_segment\n", + "- traveler_nationality_continent\n", + "- is_intercontinental_trip\n", + "- duration_days\n", + "\n", + "What we are looking for, is if there are patterns that shows us that specific demographics, have specific destination and duration preferences, and whether duration correlates with destination as well. For example, do young travelers from Europe choose more exotic destinations over older travelers from the US?\n", + "\n", + "Test: group by demographic variables and compare distributions using non-parametric statistics.\n", + "\n", + "Interpretation: segmentation could justify differentiated offers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "bac10eef-bb79-4068-8df0-872bdfb75aad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destination_continenttraveler_age_segmenttraveler_nationality_continentduration_daysis_intercontinental_trip
0Europe34-40 Young ParentsNorth America7.0True
1Asia26-33 Young ProfessionalsNorth America5.0True
2Asia41-50 Money & Energy GroupAsia7.0False
3North America26-33 Young ProfessionalsEurope14.0True
4Asia26-33 Young ProfessionalsAsia7.0False
\n", + "
" + ], + "text/plain": [ + " destination_continent traveler_age_segment \\\n", + "0 Europe 34-40 Young Parents \n", + "1 Asia 26-33 Young Professionals \n", + "2 Asia 41-50 Money & Energy Group \n", + "3 North America 26-33 Young Professionals \n", + "4 Asia 26-33 Young Professionals \n", + "\n", + " traveler_nationality_continent duration_days is_intercontinental_trip \n", + "0 North America 7.0 True \n", + "1 North America 5.0 True \n", + "2 Asia 7.0 False \n", + "3 Europe 14.0 True \n", + "4 Asia 7.0 False " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df[\n", + " [\"destination_continent\",\n", + " \"traveler_age_segment\",\n", + " \"traveler_nationality_continent\",\n", + " \"duration_days\",\n", + " \"is_intercontinental_trip\"]\n", + "].head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "6db12f30-f654-4620-8a22-74eaa1174b13", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "is_intercontinental_trip\n", + "False 81\n", + "True 56\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df[\"duration_days\"].describe()\n", + "travel_trip_complete_christos_df[\"is_intercontinental_trip\"].value_counts(dropna=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "85743046-c89e-44e6-a82c-0795e3f69155", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination object\n", + "trip_id int64\n", + "destination_country object\n", + "destination_city object\n", + "start_date datetime64[ns]\n", + "end_date datetime64[ns]\n", + "duration_days float64\n", + "traveler_name object\n", + "traveler_age float64\n", + "traveler_gender object\n", + "traveler_nationality object\n", + "accommodation_type object\n", + "accommodation_cost int64\n", + "transportation_type object\n", + "transportation_cost float64\n", + "end_year Int64\n", + "end_month Int64\n", + "start_year Int64\n", + "start_month Int64\n", + "start_month_name object\n", + "end_month_name object\n", + "traveler_nationality_clean string[python]\n", + "traveler_nationality_continent object\n", + "destination_continent object\n", + "is_intercontinental_trip bool\n", + "traveler_age_segment category\n", + "dtype: object" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_christos_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "b810a7fd-a605-4cdd-9b06-9a13a4f3853c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "count 137.000000\n", + "mean 7.605839\n", + "std 1.601276\n", + "min 5.000000\n", + "25% 7.000000\n", + "50% 7.000000\n", + "75% 8.000000\n", + "max 14.000000\n", + "Name: duration_days, dtype: float64\n", + "is_intercontinental_trip\n", + "0.0 81\n", + "1.0 56\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "df = travel_trip_complete_christos_df # shorthand\n", + "\n", + "# duration should be numeric\n", + "df[\"duration_days\"] = pd.to_numeric(df[\"duration_days\"], errors=\"coerce\")\n", + "\n", + "# intercontinental should be numeric (0/1) so mean works\n", + "df[\"is_intercontinental_trip\"] = df[\"is_intercontinental_trip\"].astype(float)\n", + "\n", + "print(df[\"duration_days\"].describe())\n", + "print(df[\"is_intercontinental_trip\"].value_counts(dropna=False))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "053480b1-3013-4e34-b89c-056e06db043f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 1. Duration by age segment\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "sns.boxplot(\n", + " data=travel_trip_complete_christos_df,\n", + " x=\"traveler_age_segment\",\n", + " y=\"duration_days\"\n", + ")\n", + "\n", + "plt.xlabel(\"Age segment\")\n", + "plt.ylabel(\"Trip duration (days)\")\n", + "plt.title(\"Trip duration by traveler age segment\")\n", + "plt.xticks(rotation=30)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "a757bc26-014c-4388-9ef8-4a7dfff1655d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# value counts in percentages\n", + "age_counts_pct = travel_trip_complete_christos_df[\"traveler_age_segment\"].value_counts(normalize=True) * 100\n", + "\n", + "plt.figure(figsize=(8, 8))\n", + "age_counts_pct.plot(\n", + " kind=\"pie\",\n", + " autopct=\"%.1f%%\",\n", + " startangle=90,\n", + " ylabel=\"\" # remove default y-label\n", + ")\n", + "\n", + "plt.title(\"Traveler Age Segment Distribution (%)\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "91a3ea3d-fd7f-491b-b1ff-cef75c96baea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 2. traveler_nationality_continent per age segment\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "ct = pd.crosstab(\n", + " df[\"traveler_age_segment\"],\n", + " df[\"traveler_nationality_continent\"]\n", + ")\n", + "\n", + "ct_prop = ct.div(ct.sum(axis=1), axis=0)\n", + "\n", + "ax = ct_prop.plot(\n", + " kind=\"bar\",\n", + " stacked=True,\n", + " figsize=(10,5),\n", + " color=[\"#b22d3e\",\"#026453\",\"#ddac34\",\"#1480d8\"]\n", + ")\n", + "\n", + "# wrap x-axis labels\n", + "wrapped = [\n", + " \"\\n\".join(textwrap.wrap(str(lbl), width=14))\n", + " for lbl in ct_prop.index\n", + "]\n", + "ax.set_xticklabels(wrapped, rotation=0, ha=\"center\")\n", + "\n", + "plt.xlabel(\"Age segment\")\n", + "plt.ylabel(\"Proportion of travelers\")\n", + "plt.title(\"Traveler nationality continent by age segment\")\n", + "plt.tight_layout()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "b214eaab-b674-4bfd-9e84-a0236ebf4613", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 2. Destination continent by age\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "ct = pd.crosstab(\n", + " travel_trip_complete_christos_df[\"traveler_age_segment\"],\n", + " travel_trip_complete_christos_df[\"destination_continent\"]\n", + ")\n", + "\n", + "ct_prop = ct.div(ct.sum(axis=1), axis=0)\n", + "\n", + "ax = ct_prop.plot(kind=\"bar\", stacked=True, figsize=(10,5))\n", + "\n", + "# wrap x labels onto multiple lines\n", + "wrapped_labels = [\n", + " \"\\n\".join(textwrap.wrap(str(lbl), width=12)) # adjust width as needed\n", + " for lbl in ct_prop.index\n", + "]\n", + "ax.set_xticklabels(wrapped_labels, rotation=0, ha=\"center\")\n", + "\n", + "plt.xlabel(\"Age segment\")\n", + "plt.ylabel(\"Proportion of trips\")\n", + "plt.title(\"Destination continent proportions by age segment\")\n", + "plt.legend(title=\"Destination continent\")\n", + "plt.tight_layout()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "id": "81964de5-aff7-4e82-a894-ae35bbe2727f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "# rows = age segments, columns = nationality continents\n", + "ct_nat = pd.crosstab(\n", + " df[\"traveler_age_segment\"],\n", + " df[\"traveler_nationality_continent\"]\n", + ")\n", + "\n", + "# row-wise proportions (per age segment)\n", + "ct_nat_prop = ct_nat.div(ct_nat.sum(axis=1), axis=0)\n", + "\n", + "ax = ct_nat_prop.plot(\n", + " kind=\"bar\",\n", + " stacked=True,\n", + " figsize=(10,5),\n", + " color=[\"#b22d3e\", \"#026453\", \"#ddac34\", \"#1480d8\"] # brand colors\n", + ")\n", + "\n", + "# wrap x labels to avoid overlap\n", + "wrapped_labels = [\n", + " \"\\n\".join(textwrap.wrap(str(lbl), width=12))\n", + " for lbl in ct_nat_prop.index\n", + "]\n", + "ax.set_xticklabels(wrapped_labels, rotation=0, ha=\"center\")\n", + "\n", + "plt.xlabel(\"Age segment\")\n", + "plt.ylabel(\"Proportion of nationalities\")\n", + "plt.title(\"Traveler nationality continent proportions by age segment\")\n", + "plt.legend(title=\"Nationality continent\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "id": "cbab9b34-8376-4742-8086-7490c84b7011", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "# 1. Build contingency: rows = (age_segment, inter/intra), columns = destination_continent\n", + "ct = pd.crosstab(\n", + " [df[\"traveler_age_segment\"], df[\"is_intercontinental_trip\"]],\n", + " df[\"destination_continent\"]\n", + ")\n", + "\n", + "# 2. Row-wise proportions so each bar sums to 1\n", + "ct_prop = ct.div(ct.sum(axis=1), axis=0)\n", + "\n", + "# 3. Plot stacked bars (one bar per age×inter_status)\n", + "ax = ct_prop.plot(\n", + " kind=\"bar\",\n", + " stacked=True,\n", + " figsize=(12,6),\n", + " color=[\"#b22d3e\", \"#026453\", \"#ddac34\", \"#1480d8\"] # brand colors for continents\n", + ")\n", + "\n", + "# 4. Custom x-tick labels: \"age\\nintra\" / \"age\\ninter\"\n", + "labels = []\n", + "for (age_seg, is_inter) in ct_prop.index:\n", + " trip_type = \"intracontinental\" if is_inter is False else \"intercontinental\"\n", + " base = f\"{age_seg}\\n{trip_type}\"\n", + " labels.append(\"\\n\".join(textwrap.wrap(base, width=18))) # wrap onto multiple lines\n", + "\n", + "ax.set_xticklabels(labels, rotation=0, ha=\"center\")\n", + "\n", + "plt.xlabel(\"Age segment and trip type\")\n", + "plt.ylabel(\"Proportion of trips\")\n", + "plt.title(\"Destination continent proportions by age segment and trip type\")\n", + "plt.legend(title=\"Destination continent\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "aa43a54b-7653-44cf-a655-ff5a1676e329", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "# contingency: rows = age segments, columns = inter/intra flag\n", + "ct = pd.crosstab(\n", + " df[\"traveler_age_segment\"],\n", + " df[\"is_intercontinental_trip\"]\n", + ")\n", + "\n", + "# row-wise proportions\n", + "ct_prop = ct.div(ct.sum(axis=1), axis=0)\n", + "\n", + "# rename columns for readability\n", + "ct_prop = ct_prop.rename(columns={False: \"intracontinental\", True: \"intercontinental\"})\n", + "\n", + "ax = ct_prop.plot(\n", + " kind=\"bar\",\n", + " stacked=False,\n", + " figsize=(10,5),\n", + " color=[\"#026453\", \"#b22d3e\"] # e.g. green intra, red inter\n", + ")\n", + "\n", + "# wrap x labels\n", + "wrapped_labels = [\n", + " \"\\n\".join(textwrap.wrap(str(lbl), width=12))\n", + " for lbl in ct_prop.index\n", + "]\n", + "ax.set_xticklabels(wrapped_labels, rotation=0, ha=\"center\")\n", + "\n", + "plt.xlabel(\"Age segment\")\n", + "plt.ylabel(\"Proportion of trips\")\n", + "plt.title(\"Intercontinental vs intracontinental trips by age segment\")\n", + "plt.legend(title=\"Trip type\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "ce0420c3-8d9a-462d-9d33-9ec722df2a05", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# intercontinental vs intracontinental trips per nationality continent\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "ct = pd.crosstab(\n", + " travel_trip_complete_christos_df[\"traveler_nationality_continent\"],\n", + " travel_trip_complete_christos_df[\"is_intercontinental_trip\"]\n", + ")\n", + "\n", + "# rename 0/1 for clarity\n", + "ct = ct.rename(columns={0: \"intracontinental\", 1: \"intercontinental\"})\n", + "\n", + "ct.plot(kind=\"bar\", stacked=False, figsize=(9,4))\n", + "\n", + "plt.xlabel(\"Traveler nationality continent\")\n", + "plt.ylabel(\"Trip count\")\n", + "plt.title(\"Intercontinental vs intracontinental trips by nationality continent\")\n", + "plt.xticks(rotation=30)\n", + "plt.tight_layout()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "2d5a7753-7d8f-4869-bd03-34496f967a9a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 4. Nationality vs. Destination\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# contingency: nationality_continent x destination_continent\n", + "ct = pd.crosstab(\n", + " travel_trip_complete_christos_df[\"traveler_nationality_continent\"],\n", + " travel_trip_complete_christos_df[\"destination_continent\"]\n", + ")\n", + "\n", + "plt.figure(figsize=(8,5))\n", + "sns.heatmap(ct, annot=True, fmt=\"d\", cmap=\"viridis\")\n", + "plt.xlabel(\"Destination continent\")\n", + "plt.ylabel(\"Traveler nationality continent\")\n", + "plt.title(\"Nationality vs destination (trip counts)\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "4d08c0a6-25f0-42c3-8792-71d8652dc366", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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gwYMCxUBfHibzpNV69OiBmjVrYvLkyXnOq05ISECjRo3w4MED/PLLLzh+/DiioqJkiWxBk8wPPXv2DI6OjrnKnZycZPv/68NVCHJunMs5f3Z2Nlq0aIGdO3di4sSJCA8Px9mzZ/HPP/98UpyKjBo1CuHh4bhx4wYyMjKwevVqdO3aFQ4ODvkep6enhz59+mDXrl1ITk4G8H5uq6OjI3x9fWX1li1bhkmTJmH37t3w8fGBtbU1OnbsiFu3buXb/rNnz/KM4cOyR48eAQBq164NfX19uW3r1q2y+wwkEgnCw8Ph6+uLBQsWoGbNmrC1tcWoUaPw+vXrj/ZTfj72NdWW954i7969g76+PnR1dQsVZ14xjhs3DlOnTkXHjh2xZ88enDlzBlFRUahWrZpcPAV9nz169Ah79uzJ9R6oUqUKAOR7v4myx+Z1PQEBAVi0aBH++ecftGrVCjY2NmjatCnOnTv30f4ZPXo0bt26hdDQUADv76+oX78+atasKauj7Ne6oPJaFUUqlcp9DZ49eyb3S3WOD8tyvhe7du2aqy/nz58PIQSeP38ud0xe11QYBbmOnTt3onv37ihZsiQ2btyI06dPIyoqCgMGDJDdwK1sm/mxsrKCl5cXfHx88OOPP2LVqlUICQnJc45++fLlUbt2bbRv3x7btm1D06ZNMWLEiFyJv6GhIQDVfw5Q8cHVbEirSSQSzJ8/H82bN8eqVaty7d+9ezdSUlKwc+dO2Sg3ANnNj4VlY2ODxMTEXOUPHz4EAKVHM69cuYJLly4hODgY/fr1k5Xfvn37k+JUpFevXpg0aRJ+++031KtXD0lJSRgxYkSBju3fvz8WLlyILVu2wM/PDyEhIRgzZoxc0mdiYoKZM2di5syZePTokWyUvl27drh+/brCtm1sbJCUlJSr/MOynP793//+J/d1zYuzszOCgoIAADdv3sS2bdswY8YMpKenY+XKlQW65sLQlveeIiVKlEB6ejpSUlJgYmKi9PF5rVyzceNG9O3bF3PnzpUrf/r0KSwtLeXKCvI+K1GiBDw9PTFnzpw8Y8hJevOi7LF5XY+enh7GjRuHcePGITk5GWFhYfjxxx/h6+uLe/fu5btKS5MmTeDh4YFff/0VpqamuHDhAjZu3ChXp6Bf65xkLy0tTa5eQW6eV8TGxkaWqP+Xou/F5cuXK1zR6MNfAD7nqkYbN26Eq6srtm7dKnfeD/tKXXL+clCQv8TVqVMHBw8exJMnT+T6LOeXIVV9b1Pxw2SetF6zZs3QvHlzzJo1K9efo3N+eP93CTkhBFavXp2rHWVGX5o2bYpdu3bh4cOHch/669evh7GxsdLLCeYVJwD88ccfSrXzX/ldj6GhIQYPHoxff/0Vp06dQvXq1dGwYcMCtVu5cmXUrVsXa9euRVZWFtLS0tC/f3+F9e3t7eHv749Lly5h6dKl+S5F5+Pjg5CQEDx69Ej2YZaVlZVryTdfX1/o6ekhNjZWqT/ZV6hQAVOmTMGOHTtw4cIFWbkyX/uC0pb3niKVKlUCAMTGxsqm5nwqiUSS6z2+b98+PHjwAOXKlZMrL8j7rG3btti/fz/Kli0LKysrpWL5lGPzYmlpia5du+LBgwcYM2YM4uPjc01R+dCoUaMwdOhQvHz5Evb29rLVW3IU9Guds/rS5cuXUbFiRVm9kJCQQl+Pt7c3Fi1ahGvXrsldx5YtW+TqNWzYEJaWlrh27ZrcFCFVKOhfkfIjkUhgYGAgl8gnJSXlOVKuDkePHgWAXO/vDwkhEBkZCUtLy1x/Hbhz5w4AfPT9RF8uJvNULMyfPx+1atXC48ePZX8mB4DmzZvDwMAAPXv2xMSJE5GamooVK1bgxYsXudqoWrUqdu7ciRUrVqBWrVrQ0dGBl5dXnuebPn26bM7ttGnTYG1tjb/++gv79u3DggULYGFhoVT8lSpVQtmyZfHDDz9ACAFra2vs2bNH9if4wqhatSoiIiKwZ88eODo6wszMTO6Dfvjw4ViwYAHOnz+PP//8U6m2BwwYgCFDhuDhw4do0KCBXLsAULduXbRt2xaenp6wsrJCTEwMNmzYgPr16+c7WjllyhSEhISgSZMmmDZtGoyNjfHbb7/lmnPr4uKCWbNmYfLkybhz5w5atmwJKysrPHr0CGfPnpX9ZeDy5cv47rvv0K1bN5QvXx4GBgY4cuQILl++jB9++EGur7Zs2YKtW7fCzc0NhoaGqFq1qlJ98iFtee8p0rhxYwDvlwpVVTLftm1bBAcHo1KlSvD09MT58+excOFChQ/F+tj7bNasWQgNDUWDBg0watQoVKxYEampqYiPj8f+/fuxcuVKhW1/yrE52rVrBw8PD3h5ecHW1hZ3797F0qVL4ezsjPLly3+0P3r37o2AgAAcO3YMU6ZMgYGBgdz+gn6ta9eujYoVK2L8+PHIzMyElZUVdu3ahRMnTnw0BkXGjBmDNWvWoFWrVpg1axbs7e2xadMm2V/WcpYrNTU1xfLly9GvXz88f/4cXbt2hZ2dHZ48eYJLly7hyZMnWLFiRaFiyHnC66pVq2BmZgZDQ0O4uroq9fCktm3bYufOnRg+fDi6du2Ke/fuYfbs2XB0dPzotD9l/PHHHzh+/DhatGiB0qVLIyUlBcePH8fy5cvRoEEDdOjQQVa3Q4cOqFatGqpXrw4bGxs8fPgQwcHBiIyMxG+//ZZrecp//vkHurq6+Prrr1UWLxUzmrz7lkhZ/13N5kO9evUSAHKtZrNnzx5RrVo1YWhoKEqWLCkmTJggDhw4kGuVhOfPn4uuXbsKS0tLIZFIxH+/PfDBShhCCPHvv/+Kdu3aCQsLC2FgYCCqVasmt+qJEHmv9iFE3qukXLt2TTRv3lyYmZkJKysr0a1bN5GQkJDr3AVdzSY6Olo0bNhQGBsbCwB5rtbSuHFjYW1tLd6+fZtrX35evnwpjIyMBACxevXqXPt/+OEH4eXlJaysrIRUKhVubm5i7NixsiX38nPy5ElRr149IZVKhYODg5gwYYJYtWpVnqt17N69W/j4+Ahzc3MhlUqFs7Oz6Nq1q2w5vEePHgl/f39RqVIlYWJiIkxNTYWnp6dYsmSJ3NKM8fHxokWLFsLMzEy2dJ4Q+a9m8+TJE7lY8vq6aMt7T5FGjRrlWg3nY+fI73v0xYsXYuDAgcLOzk4YGxuLr776Shw/flx4e3vn+f782PtMCCGePHkiRo0aJVxdXYW+vr6wtrYWtWrVEpMnTxZv3ryR1curHwtybE5/LVy4MNe5f/75Z9GgQQNRokQJYWBgIMqUKSMGDhwo4uPjFfbZh/z9/YWenp64f/9+nvsL8rUWQoibN2+KFi1aCHNzc2FraytGjhwp9u3bl+dqNnmt+JXXz5ArV66IZs2aCUNDQ2FtbS0GDhwo1q1bJwCIS5cuydWNjIwUbdq0EdbW1kJfX1+ULFlStGnTRu69oeh7Jz9Lly4Vrq6uQldXV+59q8x1zJs3T7i4uAipVCoqV64sVq9eLYvlvwCIESNG5GrT2dk5z5XB/uvkyZOibdu2wsnJSRgYGAhjY2NRrVo1MXv2bJGSkiJXd/78+aJ27drCyspK6OrqChsbG+Hr65vnspRCvP8+/HC1IKL/kgjBpxAQfYkeP34MZ2dnjBw5EgsWLNB0OFQE7dixA35+frh79y5Kliyp6XCKnfT0dLi4uOCrr77Kd0WuomTw4MHYvHkznj17lusvCaR6sbGxKF++PA4dOoTmzZtrOhwqopjME31h7t+/jzt37mDhwoU4cuQIbt68yUSN8iSEQIMGDVCrVi38+uuvmg6n2Hjy5Alu3LiBtWvXIjg4GFFRUXKr2BQVs2bNgpOTE9zc3PDmzRvs3bsXf/75J6ZMmYJZs2ZpOrwvQv/+/XH//v1PmnJJxR/nzBN9Yf7880/MmjULLi4u+Ouvv5jIk0ISiQSrV69GSEgIsrOzZfOk6dPs27cP/fv3h6OjI37//fcimcgDgL6+PhYuXIj79+8jMzMT5cuXx+LFizF69GhNh/ZFyMzMRNmyZREQEKDpUKiI48g8EREREZGW4jALEREREZGWYjJPRERERKSlmMwTEREREWkpJvNERERERFqKq9mQ0lpaD9J0CEQFkpWcrOkQiIiKhdDs7Ro9f3ZSBZW3qeNwU+VtagJH5omIiIiItBRH5omIiIioSMtGtsrbLC4j2sXlOoiIiIiIvjgcmSciIiKiIi1LqH5kvrgkwcXlOoiIiIiomMqG0HQIRRan2RARERERaSmOzBMRERFRkaaOG2CLCybzRERERFSkZQlOs1GE02yIiIiIiLQUR+aJiIiIqEjjDbCKMZknIiIioiIti8m8QpxmQ0RERESkpTgyT0RERERFGqfZKMaReSIiIiIiLcWReSIiIiIq0rg0pWJM5omIiIioSOMjoxTjNBsiIiIiIi3FkXkiIiIiKtK4NKViTOaJiIiIqEjLYi6vEKfZEBERERFpKY7MExEREVGRxhtgFePIPBERERGRluLIPBEREREVaVmQaDqEIovJPBEREREVadm8AVYhTrMhIiIiItJSHJknIiIioiKN02wU48h8ESSEwODBg2FtbQ2JRILo6GiFdSUSCXbv3v3ZYiMiIiL63LIgUflWXDCZ16BTp05BV1cXLVu2lCs/ePAggoODsXfvXiQmJsLDw0NhG4mJiWjVqpW6Q9UqHvXLY8am7/DX1YU4+Hw16reurumQtB77VH3aDWuB9bG/Yd/bv/Bb1Hx4fFVJ0yFpPfap6rFPVY99SqrCZF6D1qxZg5EjR+LEiRNISEiQlcfGxsLR0RENGjSAg4MD9PRyz4ZKT08HADg4OEAqlX62mLWBoYkUcVfu4/dJmzQdSrHBPlUP7+4NMGxJf2yeuwPDak7ElRMxmLt/MmxLl9B0aFqLfap67FPVY58qL1tIVL4VF0zmNSQlJQXbtm3DsGHD0LZtWwQHBwMA/P39MXLkSCQkJEAikcDFxQUA0LhxY3z33XcYN24cSpQogebNmwPIPc3m/v376NGjB6ytrWFiYgIvLy+cOXMGwPtfEjp06AB7e3uYmpqidu3aCAsL+5yX/VmcC7uCdXN34+Tei5oOpdhgn6pHl7FtcXDNERwIOoKE6w+wYmwwntx7inbDWmg6NK3FPlU99qnqsU+Vx2k2ijGZ15CtW7eiYsWKqFixInr37o21a9dCCIFffvkFs2bNQqlSpZCYmIioqCjZMevWrYOenh5OnjyJP/74I1ebb968gbe3Nx4+fIiQkBBcunQJEydORHZ2tmx/69atERYWhosXL8LX1xft2rWT+6sAEX0eevp6qFDLDecPX5IrPx96GVXqV9RQVNqNfap67FPVY5+SqnE1Gw0JCgpC7969AQAtW7bEmzdvEB4ejmbNmsHMzAy6urpwcHCQO6ZcuXJYsGCBwjY3bdqEJ0+eICoqCtbW1rJjclSrVg3VqlWTvf7pp5+wa9cuhISE4LvvvlPl5RHRR1iUMIOuni5ePEqWK3/xKBlWDpYaiUnbsU9Vj32qeuzTwsni+LNC7BkNuHHjBs6ePYsePXoAAPT09ODn54c1a9bke5yXl1e++6Ojo1GjRg1ZIv+hlJQUTJw4Ee7u7rC0tISpqSmuX7+e78h8WloaXr16Jbdli6yPXCERFZT44EEoEokE4sNCUgr7VPXYp6rHPtUegYGBqF27NszMzGBnZ4eOHTvixo0bcnWEEJgxYwacnJxgZGSExo0b4+rVqx9te8eOHXB3d4dUKoW7uzt27dqldHxM5jUgKCgImZmZKFmyJPT09KCnp4cVK1Zg586dePHihcLjTExM8m3XyMgo3/0TJkzAjh07MGfOHBw/fhzR0dGoWrWq7GbavAQGBsLCwkJuu5Mane95iOjjXj59jazMLFh/MBJnaWeB5EcvNROUlmOfqh77VPXYp4WjyRtgIyMjMWLECPzzzz8IDQ1FZmYmWrRogZSUFFmdBQsWYPHixfj1118RFRUFBwcHNG/eHK9fv1bY7unTp+Hn54c+ffrg0qVL6NOnD7p37y6717GgmMx/ZpmZmVi/fj1+/vlnREdHy7ZLly7B2dkZf/31V6Hb9vT0RHR0NJ4/f57n/uPHj8Pf3x+dOnVC1apV4eDggPj4+HzbDAgIwMuXL+U2N8PqhY6RiN7LzMjEzfN3ULO5p1x5zWaeuHr6hoKjKD/sU9Vjn6oe+7RwNHkD7MGDB+Hv748qVaqgWrVqWLt2LRISEnD+/HkA70flly5dismTJ6Nz587w8PDAunXr8PbtW2zapHgVuKVLl6J58+YICAhApUqVEBAQgKZNm2Lp0qVK9Q2T+c9s7969ePHiBQYOHAgPDw+5rWvXrggKCip02z179oSDgwM6duyIkydP4s6dO9ixYwdOnz4N4P38+Z07d8p+eejVq5fs5lhFpFIpzM3N5TYdiW6hY/wcDE2kcPMoDTeP0gAAB+cScPMoDduSeU8/oo9jn6rHjiV70WpgU/j290GZSiUxdHE/2JUpgb0rD2s6NK3FPlU99qnqsU+LhrymEqelpX30uJcv3/8FJWdac1xcHJKSktCixf+tRiSVSuHt7Y1Tp04pbOf06dNyxwCAr69vvsfkhTfAfmZBQUFo1qwZLCwscu3r0qUL5s6diz59+hSqbQMDAxw+fBjff/89WrdujczMTLi7u+O3334DACxZsgQDBgxAgwYNUKJECUyaNAmvXr36pOspiipUd8aCPRNkr4fM8QMAhG46hZ+/W6upsLQa+1Q9IredgrmNKXpP7QprRyvEX7mHyW3m4nHCU02HprXYp6rHPlU99qnysoTqx58DAwMxc+ZMubLp06djxowZCo8RQmDcuHH46quvZA/1TEpKAgDY29vL1bW3t8fdu3cVtpWUlJTnMTntFZRE8G4LUlJL60GaDoGoQLKSkzUdAhFRsRCavV2j54+Mr6DyNus5/ptrJF4qleb7MM4RI0Zg3759OHHiBEqVKgUAOHXqFBo2bIiHDx/C0dFRVnfQoEG4d+8eDh48mGdbBgYGWLduHXr27Ckr++uvvzBw4ECkpqYW+Do4Mk9EREREX5yPJe4fGjlyJEJCQnDs2DFZIg9AtpR4UlKSXDL/+PHjXCPv/+Xg4JBrFP5jx+SFc+aJiIiIqEjT5A2wQgh899132LlzJ44cOQJXV1e5/a6urnBwcEBoaKisLD09HZGRkWjQoIHCduvXry93DAAcPnw432PywpF5IiIiIiIFRowYgU2bNuHvv/+GmZmZbDTdwsICRkZGkEgkGDNmDObOnYvy5cujfPnymDt3LoyNjdGrVy9ZO3379kXJkiURGBgIABg9ejS+/vprzJ8/Hx06dMDff/+NsLAwnDhxQqn4mMwTERERUZGmjhtgC2rFihUAgMaNG8uVr127Fv7+/gCAiRMn4t27dxg+fDhevHiBunXr4vDhwzAzM5PVT0hIgI7O/11HgwYNsGXLFkyZMgVTp05F2bJlsXXrVtStW1ep+HgDLCmNN8CStuANsEREqqHpG2APxbmrvE1f12sqb1MTOGeeiIiIiEhLcZoNERERERVpWRx/VojJPBEREREVaZqcM1/UsWeIiIiIiLQUR+aJiIiIqEjL5vizQuwZIiIiIiItxZF5IiIiIirSskTBn9j6pWEyT0RERERFGlezUYw9Q0RERESkpTgyT0RERERFWjaXplSIyTwRERERFWmcZqMYe4aIiIiISEtxZJ6IiIiIijSuZqMYk3kiIiIiKtL40CjF2DNERERERFqKI/NEREREVKRlcTUbhdgzRERERERaiiPzRERERFSkZYM3wCrCZJ6IiIiIijROs1GMPUNEREREpKU4Mk9ERERERRqfAKsYk3kiIiIiKtKy+dAohfhrDhERERGRluLIPBEREREVaZxmoxiTeVJaVnKypkModg49vKTpEIqlFn7+mg6h2NF7marpEIg+KutyjKZDIPpsmMwTERERUZGWzaUpFWIyT0RERERFWhYfGqUQf80hIiIiItJSHJknIiIioiKN02wUYzJPREREREUap9koxl9ziIiIiIi0FEfmiYiIiKhI4zQbxdgzRERERERaiiPzRERERFSkZXFkXiEm80RERERUpGXzBliF+GsOEREREVE+jh07hnbt2sHJyQkSiQS7d++W2y+RSPLcFi5cqLDN4ODgPI9JTU1VKjaOzBMRERFRkabpaTYpKSmoVq0a+vfvjy5duuTan5iYKPf6wIEDGDhwYJ51/8vc3Bw3btyQKzM0NFQqNibzRERERFSkZQvNTrNp1aoVWrVqpXC/g4OD3Ou///4bPj4+cHNzy7ddiUSS61hlcZoNEREREX1x0tLS8OrVK7ktLS3tk9t99OgR9u3bh4EDB3607ps3b+Ds7IxSpUqhbdu2uHjxotLnYzJPREREREVaFnRUvgUGBsLCwkJuCwwM/ORY161bBzMzM3Tu3DnfepUqVUJwcDBCQkKwefNmGBoaomHDhrh165ZS5+M0GyIiIiIq0tQxzSYgIADjxo2TK5NKpZ/c7po1a/DNN998dO57vXr1UK9ePdnrhg0bombNmli+fDmWLVtW4PMxmSciIiKiL45UKlVJ8v5fx48fx40bN7B161alj9XR0UHt2rU5Mk9ERERExUu2lswMDwoKQq1atVCtWjWljxVCIDo6GlWrVlXqOCbzRERERET5ePPmDW7fvi17HRcXh+joaFhbW6NMmTIAgFevXmH79u34+eef82yjb9++KFmypGxe/syZM1GvXj2UL18er169wrJlyxAdHY3ffvtNqdiYzBMRERFRkZal4aUpz507Bx8fH9nrnLn2/fr1Q3BwMABgy5YtEEKgZ8+eebaRkJAAHZ3/+wtDcnIyBg8ejKSkJFhYWKBGjRo4duwY6tSpo1RsEiGEUPJ66AvXXKebpkModg49vKTpEIqlFn7+mg6h2NF7qdyTCYk0IetyjKZDKHZCs7dr9PyjL+adIH+KX2psVnmbmqAdE5CIiIiIiCgXTrMhIiIioiItW3D8WREm80RERERUpGVBs3PmizL+mkNEREREpKU4Mk9ERERERZo6ngBbXHBkvhiLj4+HRCJBdHS0pkMhIiIiIjXgyLyWOHXqFBo1aoTmzZvj4MGDBTqmdOnSSExMRIkSJdQcXdHTblgLdBvfATaOloi/eh8rxq7FlRPXNR2WVli1EQg9BtxJAAylQA0P4PshgOv7Z2IgIxP45U/g2D/A/UTA1ASoX+t9Hbsv762mEj2/qY+Bg32wY/tZrPg1TNPhaK023WqjbbfasHOyBAAkxD7BX6sicO6kco9Gp//DPlUffk4phzfAKsae0RJr1qzByJEjceLECSQkJBToGF1dXTg4OEBP78v6nc27ewMMW9Ifm+fuwLCaE3HlRAzm7p8M29LMNAsi6hLQqxOwZQUQ9DOQmQUMHA+8ffd+f2oqcO0mMKwvsGM1sGw2EH8fGP6jZuPWVhUrOaJ1uxqIvf1I06FovaePXmHNslCM6vUHRvX6A9FRdzB9aU84l7XVdGhai32qHvycUl42JCrfigsm81ogJSUF27Ztw7Bhw9C2bVvZk8YA4MWLF/jmm29ga2sLIyMjlC9fHmvXrgWQe5pNVlYWBg4cCFdXVxgZGaFixYr45ZdfNHBF6tVlbFscXHMEB4KOIOH6A6wYG4wn956i3bAWmg5NK6xeCHRqBZR3BSqVA+b+ACQ+kuDqzff7zUyBNYuBVk3ej9ZXrwJMGQVcvSHBQ+ajSjE00kfAlPZYsnA/3rzmw5g+1ZljNxB14hYeJDzDg4RnWPdrOFLfpqNS1dKaDk1rsU/Vg59TpEpM5rXA1q1bUbFiRVSsWBG9e/fG2rVrkfPg3qlTp+LatWs4cOAAYmJisGLFCoXTarKzs1GqVCls27YN165dw7Rp0/Djjz9i27Ztn/Ny1EpPXw8Varnh/GH5J6qeD72MKvUraigq7fb6zft/LczyqZMCSCQC5qafJ6biYtQYX5w5HYsL5+M1HUqxo6MjgbevB6RGBoi5fE/T4RQL7FPV4OdU4WQJicq34uLLmn+hpYKCgtC7d28AQMuWLfHmzRuEh4ejWbNmSEhIQI0aNeDl5QUAcHFxUdiOvr4+Zs6cKXvt6uqKU6dOYdu2bejevbtar+FzsShhBl09Xbx4lCxX/uJRMqwcLDUSkzYTApj/G1CrqkAFt7zrpKUBi1cBbZu9nz9PBdO4iTvKV3DA8CFrNR1KseJSzg5L1g+CgYEe3r1Lx+xxm5Fw54mmw9Jq7FPV4udU4XDOvGLsmSLuxo0bOHv2LHr06AEA0NPTg5+fH9asWQMAGDZsGLZs2YLq1atj4sSJOHXqVL7trVy5El5eXrC1tYWpqSlWr16d7xz8tLQ0vHr1Sm7LFlmqu0A1+f9/uJCRSCSyv2ZQwc1eCty4Ayyalvf+jEzg+1lAdjYwbexnDU2r2dqaYcTI5gj8KQQZ6UX/+0mb3I9/huF+KzCm72rs2xaF72d1Rhk3zu/+FOxT9eDnFKmK0iPzCQkJKF26NCQS+T9PCCFw7949lClTRmXB0ftR+czMTJQsWVJWJoSAvr4+Xrx4gVatWuHu3bvYt28fwsLC0LRpU4wYMQKLFi3K1da2bdswduxY/Pzzz6hfvz7MzMywcOFCnDlzRuH5AwMD5UbzAcAVlVEWVVR3kSr08ulrZGVmwfqD0Q1LOwskP3qpmaC01E9LgaMngQ3LAQe73PszMoGx09+vaLN2CUfllVG+oiOsrE2wYtUAWZmung6qViuDjp280Kr5fGRn80O9MDIzs5B47zkA4Na1h6hQpSQ69qqHZT/t0XBk2ot9qlr8nCocrjOvmNIj866urnjyJPef154/fw5XV1eVBEXvZWZmYv369fj5558RHR0t2y5dugRnZ2f89ddfAABbW1v4+/tj48aNWLp0KVatWpVne8ePH0eDBg0wfPhw1KhRA+XKlUNsbGy+MQQEBODly5dymysqqfxaVSUzIxM3z99BzeaecuU1m3ni6ukbGopKuwjxfkQ+9DiwdilQyjF3nZxE/u6D9zfDWll87ii128Xz8fjWfzWGfBsk225cf4jwsCsY8m0QE3lVkkigb8AZpSrFPv0k/JwiVVP6u1EIkWtUHgDevHkDQ0NDlQRF7+3duxcvXrzAwIEDYWEhny117doVQUFBePz4MWrVqoUqVaogLS0Ne/fuReXKlfNsr1y5cli/fj0OHToEV1dXbNiwAVFRUfn+EiaVSiGVSuXKdCS6n35xarRjyV5MWj8SN8/FIub0TbQe3Ax2ZUpg78rDmg5NK8xaAuwLB36dA5gYAU+evS83M32/7nxmJjBm2vvlKVfMA7Ky/q+OhTlgoK+52LXFu3fpiI+THxRJfZeBVy/f5SqngvMf2QxRJ27h6aOXMDI2gHfLqvD0csGUERs0HZrWYp+qBz+nlFeclpJUtQIn8+PGjQPwfk7X1KlTYWxsLNuXlZWFM2fOoHr16ioP8EsWFBSEZs2a5UrkAaBLly6YO3cuOnXqhICAAMTHx8PIyAiNGjXCli1b8mxv6NChiI6Ohp+fHyQSCXr27Inhw4fjwIED6r6Uzypy2ymY25ii99SusHa0QvyVe5jcZi4eJzzVdGhaYcvf739g9hstXz73B4FOrYBHT4AjJ9/X6TRQvs66pQJ1anyOKIlys7I2wcQ5nWFVwgxv36Qi7uYjTBmxARf/yf8vkKQY+1Q9+DmlPE6zUUwiCni3hY+PDwAgMjIS9evXh4GBgWyfgYEBXFxcMH78eJQvX149kVKR0Vynm6ZDKHYOPbz08UqktBZ+/poOodjRe8n18Knoy7oco+kQip3Q7O0aPX/PfwarvM3N9fKelqxtCjwyf/ToUQBA//798csvv8Dc3FxtQRERERER5eDSlIopPWc+5+miRERERESfA6fZKKZ0Mp+SkoJ58+YhPDwcjx8/RnZ2ttz+O3fuqCw4IiIiIiJSTOlk/ttvv0VkZCT69OkDR0fHPFe2ISIiIiJSFa5mo5jSyfyBAwewb98+NGzYUB3xEBERERHJ4TQbxZS+m8DKygrW1tbqiIWIiIiIiJSgdDI/e/ZsTJs2DW/fvlVHPEREREREcrKFROVbcaH0NJuff/4ZsbGxsLe3h4uLC/T15R/3eOHCBZUFR0REREREiimdzHfs2FENYRARERER5a04jaSrmtLJ/PTp09URBxERERFRnpjMK1aox2klJyfjzz//REBAAJ4/fw7g/fSaBw8eqDQ4IiIiIiJSTOmR+cuXL6NZs2awsLBAfHw8Bg0aBGtra+zatQt3797F+vXr1REnEREREX2huM68YkqPzI8bNw7+/v64desWDA0NZeWtWrXCsWPHVBocERERERFXs1FM6WQ+KioKQ4YMyVVesmRJJCUlqSQoIiIiIiL6OKWn2RgaGuLVq1e5ym/cuAFbW1uVBEVERERElKM4jaSrmtIj8x06dMCsWbOQkZEBAJBIJEhISMAPP/yALl26qDxAIiIiIiLKm9LJ/KJFi/DkyRPY2dnh3bt38Pb2Rrly5WBmZoY5c+aoI0YiIiIi+oJxzrxiSk+zMTc3x4kTJ3DkyBFcuHAB2dnZqFmzJpo1a6aO+IiIiIjoC1eckm9VK9Q68wDQpEkTjB8/HhMnTmQiT0RERETF1rFjx9CuXTs4OTlBIpFg9+7dcvv9/f0hkUjktnr16n203R07dsDd3R1SqRTu7u7YtWuX0rEpPTIPAOHh4QgPD8fjx4+RnZ0tt2/NmjWFaZKIiIiIKE9CwyPzKSkpqFatGvr376/wHtGWLVti7dq1stcGBgb5tnn69Gn4+flh9uzZ6NSpE3bt2oXu3bvjxIkTqFu3boFjUzqZnzlzJmbNmgUvLy84OjpCIuGfPYiIiIhIfTT90KhWrVqhVatW+daRSqVwcHAocJtLly5F8+bNERAQAAAICAhAZGQkli5dis2bNxe4HaWT+ZUrVyI4OBh9+vRR9lAiIiIiomIpIiICdnZ2sLS0hLe3N+bMmQM7OzuF9U+fPo2xY8fKlfn6+mLp0qVKnVfpZD49PR0NGjRQ9jAiIiIiokJRxw2waWlpSEtLkyuTSqWQSqVKt9WqVSt069YNzs7OiIuLw9SpU9GkSROcP39eYXtJSUmwt7eXK7O3t1f6IaxK3wD77bffYtOmTcoeRkRERERUZAQGBsLCwkJuCwwMLFRbfn5+aNOmDTw8PNCuXTscOHAAN2/exL59+/I97sPp6kIIpaewKz0yn5qailWrViEsLAyenp7Q19eX27948WJlmyQiIiIiUkgdN8AGBARg3LhxcmWFGZXPi6OjI5ydnXHr1i2FdRwcHHKNwj9+/DjXaP3HKJ3MX758GdWrVwcAXLlyRW4fb4YlIiIiIlVTxzSbwk6pKYhnz57h3r17cHR0VFinfv36CA0NlZs3f/jwYaWnsyudzB89elTZQ4iIiIiItNabN29w+/Zt2eu4uDhER0fD2toa1tbWmDFjBrp06QJHR0fEx8fjxx9/RIkSJdCpUyfZMX379kXJkiVlU3lGjx6Nr7/+GvPnz0eHDh3w999/IywsDCdOnFAqtkKtM09ERERE9Lloep35c+fOwcfHR/Y6Z3pOv379sGLFCvz7779Yv349kpOT4ejoCB8fH2zduhVmZmayYxISEqCj83+3qzZo0ABbtmzBlClTMHXqVJQtWxZbt25Vao15oIDJfOfOnREcHAxzc3N07tw537o7d+5UKgAiIiIiovyoY5qNMho3bgwhhML9hw4d+mgbERERucq6du2Krl27fkpoBUvmLSwsZPPhzc3NOTeeSMVa+PlrOoRiKXFcuqZDKHZK90/UdAhERPQfBUrm//to2uDgYHXFQkRERESUSz6D4l88pdeZb9KkCZKTk3OVv3r1Ck2aNFFFTEREREREMtmQqHwrLpRO5iMiIpCenvtP16mpqTh+/LhKgiIiIiIioo8r8Go2ly9flv3/2rVrcovcZ2Vl4eDBgyhZsqRqoyMiIiKiL56mV7MpygqczFevXh0SiQQSiSTP6TRGRkZYvny5SoMjIiIiIiLFCpzMx8XFQQgBNzc3nD17Fra2trJ9BgYGsLOzg66urlqCJCIiIqIvl6aXpizKCpzMOzs7AwCys7PVFgwRERER0Ye4mo1ihXoC7M2bNxEREYHHjx/nSu6nTZumksCIiIiIiCh/Sifzq1evxrBhw1CiRAk4ODjIPUBKIpEwmSciIiIileINsIopncz/9NNPmDNnDiZNmqSOeIiIiIiI5DCZV0zpdeZfvHiBbt26qSMWIiIiIiJSgtLJfLdu3XD48GF1xEJERERElEu2kKh8Ky6UnmZTrlw5TJ06Ff/88w+qVq0KfX19uf2jRo1SWXBERERERKSY0sn8qlWrYGpqisjISERGRsrtk0gkTOaJiIiISKW4NKViSifzcXFx6oiDiIiIiChPvAFWMaXnzP+XEAKCvyoREREREWlEoZL59evXo2rVqjAyMoKRkRE8PT2xYcMGVcdGRERERAQhJCrfigulp9ksXrwYU6dOxXfffYeGDRtCCIGTJ09i6NChePr0KcaOHauOOImIiIjoC8V5IIopncwvX74cK1asQN++fWVlHTp0QJUqVTBjxgwm80REREREn4nSyXxiYiIaNGiQq7xBgwZITExUSVBERERERDmK07QYVVN6zny5cuWwbdu2XOVbt25F+fLlVRIUEREREZGMUMNWTCg9Mj9z5kz4+fnh2LFjaNiwISQSCU6cOIHw8PA8k3wiIiIiIlIPpZP5Ll264MyZM1iyZAl2794NIQTc3d1x9uxZ1KhRQx0xEhEREdEXjNNsFFM6mQeAWrVqYePGjaqOhYiIiIiIlKB0Mr9//37o6urC19dXrvzQoUPIzs5Gq1atVBYcERERERGfUaqY0jfA/vDDD8jKyspVLoTADz/8oJKgiIiIiIhy8KFRiimdzN+6dQvu7u65yitVqoTbt2+rJCgiIiIiIvo4pZN5CwsL3LlzJ1f57du3YWJiopKgtJm/vz8kEkmurWXLlpoO7YvSblgLrI/9Dfve/oXfoubD46tKmg6p2Oj5TX2ERf6IYd8103QoWqWWjTN+q9sTR32/x9UOM9DEQfF7cnq1trjaYQb6uNX7jBFqP4/65TFj03f46+pCHHy+GvVbV9d0SMUC+1U9+DmlJCFR/VZMKJ3Mt2/fHmPGjEFsbKys7Pbt2/j+++/Rvn17lQanrVq2bInExES5bfPmzYVqSwiBzMxMFUdYvHl3b4BhS/pj89wdGFZzIq6ciMHc/ZNhW7qEpkPTehUrOaJ1uxqIvf1I06FoHSNdfdx4+QhzLu/Pt14Th0rwtCqFR+9efabIig9DEynirtzH75M2aTqUYoX9qnr8nFKeEKrfigulk/mFCxfCxMQElSpVgqurK1xdXVG5cmXY2Nhg0aJF6ohR60ilUjg4OMhtVlZWiI+Ph0QiQXR0tKxucnIyJBIJIiIiAAARERGQSCQ4dOgQvLy8IJVKcfz4caSlpWHUqFGws7ODoaEhvvrqK0RFRcnayTlu3759qFatGgwNDVG3bl38+++/crGdOnUKX3/9NYyMjFC6dGmMGjUKKSkpn6NbPpsuY9vi4JojOBB0BAnXH2DF2GA8ufcU7Ya10HRoWs3QSB8BU9pjycL9ePM6VdPhaJ0Tj29j2fUjCEuMUVjHztAMkz1bY+L5HcgU2Z8xuuLhXNgVrJu7Gyf3XtR0KMUK+1X1+DlFqlSoaTanTp3Cvn37MHz4cHz//fcIDw/HkSNHYGlpqYYQv0wTJ05EYGAgYmJi4OnpiYkTJ2LHjh1Yt24dLly4gHLlysHX1xfPnz+XO27ChAlYtGgRoqKiYGdnh/bt2yMjIwMA8O+//8LX1xedO3fG5cuXsXXrVpw4cQLfffedJi5RLfT09VChlhvOH74kV34+9DKq1K+ooaiKh1FjfHHmdCwunI/XdCjFkgQSzKvZGWtvn0Ts6yeaDoeI1ISfU4XEJ8AqVKh15iUSCVq0aIEWLfgbZF727t0LU1NTubJJkyahT58+BW5j1qxZaN68OQAgJSUFK1asQHBwsGzpz9WrVyM0NBRBQUGYMGGC7Ljp06fLjlu3bh1KlSqFXbt2oXv37li4cCF69eqFMWPGAADKly+PZcuWwdvbGytWrIChoeGnXHaRYFHCDLp6unjxKFmu/MWjZFg5WGokpuKgcRN3lK/ggOFD1mo6lGJrYPmGyBTZ2HjnjKZDISI14ucUqVqhknnKn4+PD1asWCFXZm1tjVevCj4H1svLS/b/2NhYZGRkoGHDhrIyfX191KlTBzEx8n+yr1+/vtw5K1asKKtz/vx53L59G3/99ZesjhAC2dnZiIuLQ+XKlXPFkZaWhrS0NLmybJEFHYluga9FEz6cCyeRSCCK0wS5z8jW1gwjRjbHpPGbkZGee1la+nTuFo7o41YPXSP/0HQoRPSZ8HNKOcVpKUlVYzKvBiYmJihXrlyu8jdv3gCA3DdrzhSYvNrIkVNfIpF/IwshcpXlJadOdnY2hgwZglGjRuWqU6ZMmTyPDQwMxMyZM+XKXFEZZVHlo+fVhJdPXyMrMwvWH4xuWNpZIPnRS80EpeXKV3SElbUJVqwaICvT1dNB1Wpl0LGTF1o1n4/sbH4AfYpaNs6wlpogrPlYWZmejg4meLRAn7L10CJ0qeaCIyKV4udUIfFjRiGl58xT4dna2gIAEhMTZWX/vRlWkXLlysHAwAAnTpyQlWVkZODcuXO5RtP/+ecf2f9fvHiBmzdvolKl98td1axZE1evXkW5cuVybQYGBnmeOyAgAC9fvpTbXFF0l8/KzMjEzfN3ULO5p1x5zWaeuHr6hoai0m4Xz8fjW//VGPJtkGy7cf0hwsOuYMi3QUzkVSDk3iV0OroCXSJWyrZH715h7e1TGHx6g6bDIyIV4ueUdjp27BjatWsHJycnSCQS7N69W7YvIyMDkyZNQtWqVWFiYgInJyf07dsXDx8+zLfN4ODgPJczT01VbpEJjsyrQVpaGpKSkuTK9PT0UKJECdSrVw/z5s2Di4sLnj59iilTpny0PRMTEwwbNgwTJkyAtbU1ypQpgwULFuDt27cYOHCgXN1Zs2bBxsYG9vb2mDx5MkqUKIGOHTsCeD9vv169ehgxYgQGDRoEExMTxMTEIDQ0FMuXL8/z3FKpFFKpVK6sqE+x2bFkLyatH4mb52IRc/omWg9uBrsyJbB35WFNh6aV3r1LR3yc/A2Zqe8y8Orlu1zlpJixrgHKmFjLXpcytkQlcwe8zHiHxHcv8TLjnVz9TJGNp6lvEP/m2ecOVWsZmkjh5Gone+3gXAJuHqXx+kUKnjx4ns+RlB/2q+rxc0p5mp5mk5KSgmrVqqF///7o0qWL3L63b9/iwoULmDp1KqpVq4YXL15gzJgxaN++Pc6dO5dvu+bm5rhxQ/6XOGXvYVQ6mW/cuDEGDBiAbt26wcjISNnDvwgHDx6Eo6OjXFnFihVx/fp1rFmzBgMGDICXlxcqVqyIBQsWFOhG4nnz5iE7Oxt9+vTB69ev4eXlhUOHDsHKyipXvdGjR+PWrVuoVq0aQkJCZKPunp6eiIyMxOTJk9GoUSMIIVC2bFn4+fmp7uKLgMhtp2BuY4reU7vC2tEK8VfuYXKbuXic8FTTodEXrIqlE4K/8pe9nlT1/YPkdidEY/LF3ZoJqpipUN0ZC/b834IAQ+a8/9kWuukUfv6ON28XFvtV9fg5VQga/iNwq1atZIuQfMjCwgKhoaFyZcuXL0edOnWQkJCgcCoz8H4qtIODwyfFJhFK3m3x/fff46+//sK7d+/QvXt3DBw4EPXq8SmFmhYREQEfHx+8ePFC7UuENtfpptb2v0SiUQ1Nh1AsJY5L13QIxU7p/g80HQLRR2UlJ2s6hGInNHu7Rs/vsn6eytuM7/tDoY6TSCTYtWuXbOZDXsLCwtCiRQskJyfD3Nw8zzrBwcH49ttvUbJkSWRlZaF69eqYPXs2atRQLidQes78zz//jAcPHmD9+vV48uQJvv76a7i7u2PRokV49IhPhSQiIiIiVZOofEtLS8OrV6/ktg9X8CuM1NRU/PDDD+jVq5fCRB4AKlWqhODgYISEhGDz5s0wNDREw4YNcevWLaXOV6gbYHV1ddGhQwfs3r0bDx48QK9evTB16lSULl0aHTt2xJEjRwrTLBERERHRZxEYGAgLCwu5LTAw8JPazMjIQI8ePZCdnY3ff/8937r16tVD7969Ua1aNTRq1Ajbtm1DhQoVFN7HqMgn3QB79uxZrF27Fps3b4adnR38/f2RmJiIdu3aYdiwYVi0aNGnNE9KaNy4MdenJSIiouJJDSlOQEAAxo0bJ1f24aIfysjIyED37t0RFxeHI0eO5DsqnxcdHR3Url1b6ZF5pZP5x48fY8OGDVi7di1u3bqFdu3aYcuWLfD19ZWtZ969e3d07NiRyTwRERERfTo1JPN5rdhXWDmJ/K1bt3D06FHY2Ngo3YYQAtHR0ahatapSxymdzJcqVQply5bFgAED4O/vL1s7/b/q1KmD2rVrK9s0EREREVGR8+bNG9y+fVv2Oi4uDtHR0bC2toaTkxO6du2KCxcuYO/evcjKypItUW5tbS1bVbBv374oWbKkbCrPzJkzUa9ePZQvXx6vXr3CsmXLEB0djd9++02p2JRO5sPDw9GoUaN865ibm+Po0aPKNk1ERERElJuG15k/d+4cfHx8ZK9zpuf069cPM2bMQEhICACgevXqcscdPXoUjRs3BgAkJCRAR+f/bldNTk7G4MGDkZSUBAsLC9SoUQPHjh1DnTp1lIpN6WR++vTp2LlzZ67lD1+9esWbX4mIiIhI5TR9W+DH7k0syH2LERERcq+XLFmCJUuWfGpoyq9mExkZifT03Gs3p6am4vjx458cEBERERERFUyBR+YvX74M4P1vHteuXZPNBQKArKwsHDx4ECVLllR9hERERET0ZeOCfQoVOJmvXr06JBIJJBIJmjRpkmu/kZGR0utiEhERERF9lIbnzBdlBU7m4+LiIISAm5sbzp49K7eKjYGBAezs7KCrq6uWIImIiIiIKLcCJ/POzs4AgOzsbLUFQ0RERET0IQmn2ShUoGQ+JCQErVq1gr6+vmzpHUXat2+vksCIiIiIiCh/BUrmO3bsiKSkJNjZ2aFjx44K60kkEmRlZakqNiIiIiIi3gCbjwIl8/+dWsNpNkRERET0WfEGWIWUXmeeiIiIiIiKhgKNzC9btqzADY4aNarQwRARERER5cJpNgoVKJkv6KNmJRIJk3kiIiIiUi0m8woVKJmPi4tTdxxERERERKSkAq8zT0RERESkERyZV6hQyfz9+/cREhKChIQEpKeny+1bvHixSgIjIiIiIqL8KZ3Mh4eHo3379nB1dcWNGzfg4eGB+Ph4CCFQs2ZNdcRIRERERF8yLk2pkNJLUwYEBOD777/HlStXYGhoiB07duDevXvw9vZGt27d1BEjEREREX3BJEL1W3GhdDIfExODfv36AQD09PTw7t07mJqaYtasWZg/f77KAyQiIiIiorwpncybmJggLS0NAODk5ITY2FjZvqdPn6ouMiIiIiIi4P0NsKreigml58zXq1cPJ0+ehLu7O9q0aYPvv/8e//77L3bu3Il69eqpI0YiIiIiIsqD0sn84sWL8ebNGwDAjBkz8ObNG2zduhXlypUr8MOliIiIiIjo0ymdzLu5ucn+b2xsjN9//12lARERERER/VdxumFV1Qr90Kj09HQ8fvwY2dnZcuVlypT55KCIiIiIiOjjlE7mb968iYEDB+LUqVNy5UIISCQSZGVlqSw4oi/F45pGmg6hWDLfItV0CMXOky7umg6h2LELf6DpEIqf5GRNR0CqxnXmFVI6me/fvz/09PSwd+9eODo6QiJh5xIRERGRGnGajUJKJ/PR0dE4f/48KlWqpI54iIiIiIiogJRO5t3d3bmePBERERF9PhyZV0jph0bNnz8fEydOREREBJ49e4ZXr17JbUREREREqiQRqt+KC6VH5ps1awYAaNq0qVw5b4AlIiIiIvq8lE7mjx49qo44iIiIiIjyVoxG0lVN6WTe29tbHXEQEREREeWNybxChXpoVHJyMoKCghATEwOJRAJ3d3cMGDAAFhYWqo6PiIiIiIgUUPoG2HPnzqFs2bJYsmQJnj9/jqdPn2Lx4sUoW7YsLly4oI4YiYiIiOgLxhtgFVN6ZH7s2LFo3749Vq9eDT2994dnZmbi22+/xZgxY3Ds2DGVB0lERERERLkpncyfO3dOLpEHAD09PUycOBFeXl4qDY6IiIiICEKi6QiKLKWn2ZibmyMhISFX+b1792BmZqaSoIiIiIiIZIQatmJC6WTez88PAwcOxNatW3Hv3j3cv38fW7ZswbfffouePXuqI0YiIiIiIsqD0sn8okWL0LlzZ/Tt2xcuLi5wdnaGv78/unbtivnz56sjRiIiIiL6gmn6Bthjx46hXbt2cHJygkQiwe7du+X2CyEwY8YMODk5wcjICI0bN8bVq1c/2u6OHTvg7u4OqVQKd3d37Nq1S7nAUIhk3sDAAL/88gtevHiB6OhoXLx4Ec+fP8eSJUsglUqVDoCIiIiIKF8anmaTkpKCatWq4ddff81z/4IFC7B48WL8+uuviIqKgoODA5o3b47Xr18rbPP06dPw8/NDnz59cOnSJfTp0wfdu3fHmTNnlIpNIoQoRrOG6HNortNN0yEUO49GN9B0CMWSSVK2pkModtJNlR4Doo+wC3+g6RCKncz4u5oOodgJzd6u0fOXD1yi8jZvBYwt1HESiQS7du1Cx44dAbwflXdycsKYMWMwadIkAEBaWhrs7e0xf/58DBkyJM92/Pz88OrVKxw4cEBW1rJlS1hZWWHz5s0FjqdAq9l07twZwcHBMDc3R+fOnfOtu3PnzgKfnIiIiIjoY9SxLnxaWhrS0tLkyqRSqdIzTeLi4pCUlIQWLVrItePt7Y1Tp04pTOZPnz6NsWPlf6Hw9fXF0qVLlTp/gYZYLCwsIJG8XxLI3NwcFhYWCjciIiIioqIuMDAwVx4bGBiodDtJSUkAAHt7e7lye3t72T5Fxyl7TF4KNDK/du1a2f+Dg4OVOgERERER0SdRw8h8QEAAxo0bJ1f2Kfd/5gx85xBC5CpTxTEfUnryY5MmTZCcnJyr/NWrV2jSpImyzRERERER5U8NN8BKpVKYm5vLbYVJ5h0cHAAg14j648ePc428f3icssfkRelkPiIiAunp6bnKU1NTcfz4cWWbIyIiIiLSWq6urnBwcEBoaKisLD09HZGRkWjQQPECF/Xr15c7BgAOHz6c7zF5KdA0GwC4fPmy7P/Xrl2T+00iKysLBw8eRMmSJZU6ORERERHRx6jjBlhlvHnzBrdv35a9jouLQ3R0NKytrVGmTBmMGTMGc+fORfny5VG+fHnMnTsXxsbG6NWrl+yYvn37omTJkrJ5+aNHj8bXX3+N+fPno0OHDvj7778RFhaGEydOKBVbgZP56tWrQyKRQCKR5DmdxsjICMuXL1fq5ERERERERd25c+fg4+Mje50z175fv34IDg7GxIkT8e7dOwwfPhwvXrxA3bp1cfjwYZiZmcmOSUhIgI7O/02KadCgAbZs2YIpU6Zg6tSpKFu2LLZu3Yq6desqFVuB15m/e/cuhBBwc3PD2bNnYWtrK9tnYGAAOzs76OrqKnVy0k5cZ171uM68enCdedXjOvOqx3XmVY/rzKuepteZrzhb9evM35hauHXmi5oCj8w7OzsDALKz+eFIRERERJ8RH3GqUIGT+Q9du3YNCQkJuW6Gbd++/ScH9Tn4+/sjOTkZu3fv1nQonyQiIgI+Pj548eIFLC0tNR0OEREREX1GSifzd+7cQadOnfDvv/9CIpEgZ5ZOzpqYWVlZSrXn7++PdevWITAwED/88IOsfPfu3ejUqRMKOAtIofj4eLi6uuLixYuoXr36J7WV4927d3BycoJEIsGDBw9gZGSkknYLo0GDBkhMTOQDuz7QblgLdBvfATaOloi/eh8rxq7FlRPXNR2W1tLVkWCYb320qVUJNmYmePo6BX+fvYpVoWfwid+iXzRbK1MM79UI9au5Qmqgh4TEF5i76hBuxD3WdGhaaU/gADiVyP2zcNvRaMzfdFQDEWm/7sOaoKFvVZRys0V6aiauXYjHmvn78CDuiaZD03r8nFKOpm+ALcqUnvw4evRouLq64tGjRzA2NsbVq1dx7NgxeHl5ISIiolBBGBoaYv78+Xjx4kWhjlckryU0VWHHjh3w8PCAu7s7du7cqZZzFERGRgYMDAzg4OCg9AMGijPv7g0wbEl/bJ67A8NqTsSVEzGYu38ybEuX0HRoWmtAk9ro1sATc3ceRcd567Bkz3H4+3ihV6Mamg5Na5mZSPHHzB7IzMzGuPk70XN8MJZvjMSblLSPH0x56jNnM1p8/4dsG7Z4BwAg7NwtDUemvarWccOeDScxtsty/Nj3D+jq6WDO+sGQGhloOjStxs+pQlDDOvPFhdLJ/OnTpzFr1izY2tpCR0cHOjo6+OqrrxAYGIhRo0YVKohmzZrBwcHho4/Q3bFjB6pUqQKpVAoXFxf8/PPPcvtdXFzw008/wd/fHxYWFhg0aBBcXV0BADVq1IBEIkHjxo3ljlm0aBEcHR1hY2ODESNGICMj46PxBgUFoXfv3ujduzeCgoJy7ZdIJPjjjz/Qtm1bGBsbo3Llyjh9+jRu376Nxo0bw8TEBPXr10dsbKzccXv27EGtWrVgaGgINzc3zJw5E5mZmXLtrly5Eh06dICJiQl++uknREREQCKRyD3I6+TJk/D29oaxsTGsrKzg6+sr+0Xp4MGD+Oqrr2BpaQkbGxu0bds2VxzarsvYtji45ggOBB1BwvUHWDE2GE/uPUW7YS00HZrW8nRxxNErsTh+LQ4PX7xC6KVbOH3jLtxLK/dgC/o/vdvVwaNnrzHnj0O4FpuEpKevcO5qAh48fqnp0LRW8pt3ePbqrWxr5OmKe4+Tcf7mfU2HprWm9v8TYTvOIeHWI8RdT8SSiVthX9IK5T1KaTo0rcbPKVIlpZP5rKwsmJqaAgBKlCiBhw8fAnh/g+yNGzcKFYSuri7mzp2L5cuX4/79vH/onj9/Ht27d0ePHj3w77//YsaMGZg6dSqCg4Pl6i1cuBAeHh44f/48pk6dirNnzwIAwsLCkJiYKDeSfvToUcTGxuLo0aNYt24dgoODc7X3odjYWJw+fRrdu3dH9+7dcerUKdy5cydXvdmzZ6Nv376Ijo5GpUqV0KtXLwwZMgQBAQE4d+4cAOC7776T1T906BB69+6NUaNG4dq1a/jjjz8QHByMOXPmyLU7ffp0dOjQAf/++y8GDBiQ67zR0dFo2rQpqlSpgtOnT+PEiRNo166dbPpTSkoKxo0bh6ioKISHh0NHRwedOnUqNjc26+nroUItN5w/fEmu/HzoZVSpX1FDUWm/i3EPUbdCaTjbWgIAKjiVQA03J5y4FqfZwLRYo1plcf3OI8wZ3Rb7Vg7DusA+aN+kqqbDKjb0dHXQum5l/H3yiqZDKVaMzQwBAK9fvtVwJNqLn1OFxJF5hZSeM+/h4YHLly/Dzc0NdevWxYIFC2BgYIBVq1bBzc2t0IF06tQJ1atXx/Tp0/Mc7V68eDGaNm2KqVOnAgAqVKiAa9euYeHChfD395fVa9KkCcaPHy97HR8fDwCwsbGRPW43h5WVFX799Vfo6uqiUqVKaNOmDcLDwzFo0CCFca5ZswatWrWClZUVAKBly5ZYs2YNfvrpJ7l6/fv3R/fu3QEAkyZNQv369TF16lT4+voCeD9dqX///rL6c+bMwQ8//IB+/foBANzc3DB79mxMnDgR06dPl9Xr1auXXBIfFyefTC1YsABeXl74/fffZWVVqlSR/b9Lly5y9YOCgmBnZ4dr167Bw8ND4XVrC4sSZtDV08WLR8ly5S8eJcPKwVIjMRUHa8KjYGpogL9/8EeWyIauRAfL95/EgYuF+wWeACc7C3RqVg1b9p/Hur/Pwr2sA8b180FGRhYOHL+m6fC0nk+NcjA1lmLPSfalKg2e3B5Xou7g7s2kj1emPPFzqnA4Z14xpUfmp0yZIhvF/emnn3D37l00atQI+/fvx7Jlyz4pmPnz52PdunW4di33D9+YmBg0bNhQrqxhw4a4deuW3E23Xl5eBT5flSpV5NbGd3R0xOPHim88y8rKwrp169C7d29ZWe/evbFu3bpcN/56enrK/m9v/34qQtWqVeXKUlNT8erVKwDv//Iwa9YsmJqayrZBgwYhMTERb9/+3wjIx64vZ2RekdjYWPTq1Qtubm4wNzeXTUNKSEjIs35aWhpevXolt2UL5W5y1oQPb8r8783apLyWNSqgba3K+GHjfvT4+S9M2XwI/XxqoX1td02HprV0dCS4Gf8YK7eewM34x9gdfhl/H/kXnZpV03RoxUKHr6rg1JV4PH2ZoulQio3hMzvBtZIj5o/+S9OhFAv8nCJVUXpkPmdkGXg/enzt2jU8f/4cVlZWn3wT5tdffw1fX1/8+OOPcqPtACCEyNV+Xm96ExOTAp9PX19f7rVEIsl3usmhQ4fw4MED+Pn5yZVnZWXh8OHDaNWqVZ5t58SdV1nO+bKzszFz5kx07tw513kNDQ1l///Y9X1sZZ127dqhdOnSWL16NZycnJCdnQ0PDw+FNwsHBgZi5syZcmWuqIyyqJJnfU17+fQ1sjKzYP3B6IalnQWSH3EucmGNa/c1gsKjcPDiTQDArcRncLQyw8CmtRESxZHPwnj6IgVx95/JlcU/eA6fOuU1FFHx4WBthjqVy2DC73s0HUqxMWx6R9RrWgUTevyOp0n8Wfop+DlVSPw9RyGVPMrP2tpaZaupzJs3D3v27MGpU6fkyt3d3XHixAm5slOnTqFChQr5PnnWwOD9HffKLpmZl6CgIPTo0QPR0dFy2zfffJPn1CBl1KxZEzdu3EC5cuVybf999O/HeHp6Ijw8PM99z549Q0xMDKZMmYKmTZuicuXKH11BKCAgAC9fvpTbXFFJqWv7nDIzMnHz/B3UbO4pV16zmSeunuaUkMIyNNDL9ctzdnbuX7Cp4P69+QBlnKzkyso4WiHp6WsNRVR8tG9YBS9evcOJf3lPhyoMm9EJDXyr4ofeK/Ho/nNNh6P1+DlVOBKh+q24UHpkPiUlBfPmzUN4eDgeP36cayQ7r5tBlVG1alV88803WL58uVz5999/j9q1a2P27Nnw8/PD6dOn8euvv8rNDc+LnZ0djIyMcPDgQZQqVQqGhoaFWpP9yZMn2LNnD0JCQnLNLe/Xrx/atGmDJ0+ewNbWVum2AWDatGlo27YtSpcujW7dukFHRweXL1/Gv//+m2s+fn4CAgJQtWpVDB8+HEOHDoWBgQGOHj2Kbt26wdraGjY2Nli1ahUcHR2RkJAgt7Z/XqRSKaRSqVyZjkTxL09FwY4lezFp/UjcPBeLmNM30XpwM9iVKYG9Kw9rOjStFXn1DgY1r4PE5NeITXyGSqVs0adxTew+c1XToWmtLfvPY9XMnujXoQ7C/7kJ97IO6NDEE/P+5Pv0U0gk75P5vaevISu7GH1aa8iIWZ3RuH0NzBq8Fu/epMGqhBkAIOX1O6SnZX7kaFKEn1OkSkon899++y0iIyPRp08fODo6qmVkbvbs2di2bZtcWc2aNbFt2zZMmzYNs2fPhqOjI2bNmpVrOs6H9PT0sGzZMsyaNQvTpk1Do0aNCrUe/vr162FiYpLnfHQfHx+YmZlhw4YNGDdunNJtA++nL+3duxezZs3CggULoK+vj0qVKuHbb79Vqp0KFSrg8OHD+PHHH1GnTh0YGRmhbt266NmzJ3R0dLBlyxaMGjUKHh4eqFixIpYtW5ZruU5tF7ntFMxtTNF7aldYO1oh/so9TG4zF48Tnmo6NK0VuPMovmvVAJO7NIG1qTGevHqD/536FysP/6Pp0LRWzJ1H+GFxCIb1+Ar9O9dH4pOXWLrhKA6f5ENjPkXdymXgaGPOVWxUpG3vBgCABVuGy5X/PGELwnac00RIxQI/pwqBv5srJBFK3m1haWmJffv25boZlb4czXW6aTqEYufR6AaaDqFYMkkqHkuuFiXppiqZnUn/YRf+QNMhFDuZ8Xc1HUKxE5q9XaPnd5+8ROVtXpszVuVtaoLSI/NWVlawtrZWRyxERERERLlxZF4hpYdYZs+ejWnTpsktl0hEREREpC68AVYxpUfmf/75Z8TGxsLe3h4uLi65lne8cOGCyoIjIiIiIiLFlE7mO3bsqIYwiIiIiIgUKEYj6aqmdDI/ffp0dcRBRERERJQ3JvMKcVkCIiIiIiItpfTIPBERERHR51ScblhVNY7MExERERFpKY7MExEREVHRxpF5hZQamc/IyICbmxuuXbumrniIiIiIiORwnXnFlErm9fX1kZaWBolEoq54iIiIiIiogJSeMz9y5EjMnz8fmZmZ6oiHiIiIiEieUMNWTCg9Z/7MmTMIDw/H4cOHUbVqVZiYmMjt37lzp8qCIyIiIiIqTsm3qimdzFtaWqJLly7qiIWIiIiIiJSgdDK/du1adcRBRERERJQn3q2pWKHWmc/MzERYWBj++OMPvH79GgDw8OFDvHnzRqXBERERERGRYkqPzN+9exctW7ZEQkIC0tLS0Lx5c5iZmWHBggVITU3FypUr1REnEREREX2pOGdeIaVH5kePHg0vLy+8ePECRkZGsvJOnTohPDxcpcEREREREXGdecWUHpk/ceIETp48CQMDA7lyZ2dnPHjwQGWBERERERFR/pRO5rOzs5GVlZWr/P79+zAzM1NJUEREREREMsVoJF3VlJ5m07x5cyxdulT2WiKR4M2bN5g+fTpat26tytiIiIiIiPjQqHwoPTK/ZMkS+Pj4wN3dHampqejVqxdu3bqFEiVKYPPmzeqIkYiIiIiI8qD0yLyTkxOio6Mxfvx4DBkyBDVq1MC8efNw8eJF2NnZqSNGIiIiIvqCafIGWBcXF0gkklzbiBEj8qwfERGRZ/3r16+rqDfkKT0yDwBGRkYYMGAABgwYoOp4iIiIiIjkaXBaTFRUlNz9oleuXEHz5s3RrVu3fI+7ceMGzM3NZa9tbW3VEl+BkvmQkJACN9i+fftCB0NEREREVJR8mITPmzcPZcuWhbe3d77H2dnZwdLSUo2RvVegZL5jx44FakwikeS50g0RERERUWGpY134tLQ0pKWlyZVJpVJIpVKFx6Snp2Pjxo0YN24cJBJJvu3XqFEDqampcHd3x5QpU+Dj46OSuD9UoDnz2dnZBdqYyBMRERGRNggMDISFhYXcFhgYmO8xu3fvRnJyMvz9/RXWcXR0xKpVq7Bjxw7s3LkTFStWRNOmTXHs2DEVX8F7EiFEoX/XSU1NhaGhoSrjIS3QXCf/OWKkvEejG2g6hGLJJClb0yEUO+mmSq+bQB9hF84HLqpaZvxdTYdQ7IRmb9fo+WsMX6LyNv9ZMlzpkXlfX18YGBhgz549Sp2rXbt2kEgkSk1dLyilfypnZWVh9uzZKFmyJExNTXHnzh0AwNSpUxEUFKTyAImIiIjoy6aO1WykUinMzc3ltvwS+bt37yIsLAzffvut0vHXq1cPt27d+pQuUEjp1WzmzJmDdevWYcGCBRg0aJCsvGrVqliyZAkGDhyo0gCJvgT2v5zSdAhEpCHCs7KmQyh2dD/DTYf05Vm7di3s7OzQpk0bpY+9ePEiHB0d1RBVIZL59evXY9WqVWjatCmGDh0qK/f09FTb+plERERE9AXT8BNbs7OzsXbtWvTr1w96evLpc0BAAB48eID169cDAJYuXQoXFxdUqVJFdsPsjh07sGPHDrXEpnQy/+DBA5QrVy5XeXZ2NjIyMlQSFBERERGRjIaT+bCwMCQkJOT5jKXExEQkJCTIXqenp2P8+PF48OABjIyMUKVKFezbtw+tW7dWS2xKJ/NVqlTB8ePH4ezsLFe+fft21KhRQ2WBEREREREVBS1atICiNWOCg4PlXk+cOBETJ078DFG9p3QyP336dPTp0wcPHjxAdnY2du7ciRs3bmD9+vXYu3evOmIkIiIioi+YOtaZLy6UXs2mXbt22Lp1K/bv3w+JRIJp06YhJiYGe/bsQfPmzdURIxERERER5UHpkXng/Rqbvr6+qo6FiIiIiCg3jswrpPTIfP/+/REeHq5w3hARERERkSpJhFD5Vlwoncw/e/YMbdq0QalSpfD999/j4sWL6oiLiIiIiIg+QulkPiQkBElJSZg+fTrOnz8PLy8vuLu7Y+7cuYiPj1dDiERERET0RRNq2IoJpZN5ALC0tMTgwYMRERGBu3fvon///tiwYUOe688TEREREX0KiVD9VlwUKpnPkZGRgXPnzuHMmTOIj4+Hvb29quIiIiIiIqKPKFQyf/ToUQwaNAj29vbo168fzMzMsGfPHty7d0/V8RERERHRl47TbBRSemnKUqVK4dmzZ/D19cUff/yBdu3awdDQUB2xEREREREVq2kxqqZ0Mj9t2jR069YNVlZW6oiHiIiIiIgKSOlkfvDgweqIg4iIiIgobxyZV6hQT4CNiorC9u3bkZCQgPT0dLl9O3fuVElgRERERESUP6VvgN2yZQsaNmyIa9euYdeuXcjIyMC1a9dw5MgRWFhYqCNGIiIiIvqCcWlKxZRO5ufOnYslS5Zg7969MDAwwC+//IKYmBh0794dZcqUUUeMRERERPQl42o2CimdzMfGxqJNmzYAAKlUipSUFEgkEowdOxarVq1SeYBERERERJQ3pZN5a2trvH79GgBQsmRJXLlyBQCQnJyMt2/fqjY6IiIiIvricZqNYkrfANuoUSOEhoaiatWq6N69O0aPHo0jR44gNDQUTZs2VUeMRERERPQlE8Uo+1YxpZP5X3/9FampqQCAgIAA6Ovr48SJE+jcuTOmTp2q8gCJiIiIiChvSiXzmZmZ2LNnD3x9fQEAOjo6mDhxIiZOnKiW4IiIiIiIitO0GFVTas68np4ehg0bhrS0NHXFQ0REREREBaT0DbB169bFxYsX1RELEREREVFuXJpSIaXnzA8fPhzff/897t+/j1q1asHExERuv6enp8qCIyIiIiKSZGs6gqKrwMn8gAEDsHTpUvj5+QEARo0aJdsnkUgghIBEIkFWVpbqo6RCCQ4OxpgxY5CcnKzpUIiIiIhIDQo8zWbdunVITU1FXFxcru3OnTuyf78E9+7dw8CBA+Hk5AQDAwM4Oztj9OjRePbsmaZDk+Pn54ebN29qOgyNaDesBdbH/oZ9b//Cb1Hz4fFVJU2HpPXYp+rBflU99qnqtOlWGyu2DceOEz9ix4kfsWTdIHg1LK/psLSeR/3ymLHpO/x1dSEOPl+N+q2razqkoo/TbBQqcDIv/v/6ns7Ozvluxd2dO3fg5eWFmzdvYvPmzbh9+zZWrlyJ8PBw1K9fH8+fP9d0iDJGRkaws7PTdBifnXf3Bhi2pD82z92BYTUn4sqJGMzdPxm2pUtoOjStxT5VD/ar6rFPVevpo1dYsywUo3r9gVG9/kB01B1MX9oTzmVtNR2aVjM0kSLuyn38PmmTpkPRGnxolGJK3QArkUjUFYfWGDFiBAwMDHD48GF4e3ujTJkyaNWqFcLCwvDgwQNMnjwZAJCWloaJEyeidOnSkEqlKF++PIKCgmTtXLt2Da1bt4apqSns7e3Rp08fPH36VLb/4MGD+Oqrr2BpaQkbGxu0bdsWsbGxsv3x8fGQSCTYuXMnfHx8YGxsjGrVquH06dOyOsHBwbC0tJS9jo2NRYcOHWBvbw9TU1PUrl0bYWFhauwtzegyti0OrjmCA0FHkHD9AVaMDcaTe0/RblgLTYemtdin6sF+VT32qWqdOXYDUSdu4UHCMzxIeIZ1v4Yj9W06KlUtrenQtNq5sCtYN3c3Tu7lgiL06ZRK5itUqABra+t8t+Ls+fPnOHToEIYPHw4jIyO5fQ4ODvjmm2+wdetWCCHQt29fbNmyBcuWLUNMTAxWrlwJU1NTAEBiYiK8vb1RvXp1nDt3DgcPHsSjR4/QvXt3WXspKSkYN24coqKiEB4eDh0dHXTq1AnZ2fJ3gEyePBnjx49HdHQ0KlSogJ49eyIzMzPP+N+8eYPWrVsjLCwMFy9ehK+vL9q1a4eEhAQV95Tm6OnroUItN5w/fEmu/HzoZVSpX1FDUWk39ql6sF9Vj32qXjo6Enj7ekBqZICYy/c0HQ59aYRQ/VZMKLWazcyZM2FhYaGuWIq8W7duQQiBypUr57m/cuXKePHiBaKiorBt2zaEhoaiWbNmAAA3NzdZvRUrVqBmzZqYO3eurGzNmjUoXbo0bt68iQoVKqBLly5ybQcFBcHOzg7Xrl2Dh4eHrHz8+PFo06YNgPdfnypVquD27duoVCn3HNFq1aqhWrVqstc//fQTdu3ahZCQEHz33XeF6JGix6KEGXT1dPHiUbJc+YtHybBysNRITNqOfaoe7FfVY5+qh0s5OyxZPwgGBnp49y4ds8dtRsKdJ5oOi4j+P6WS+R49enyRc7ALKue+gri4OOjq6sLb2zvPeufPn8fRo0dlI/X/FRsbiwoVKiA2NhZTp07FP//8g6dPn8pG5BMSEuSS+f8uBero6AgAePz4cZ7JfEpKCmbOnIm9e/fi4cOHyMzMxLt37/IdmU9LS8v1kLBskQUdia7CY4qCD3/hzllxiQqPfaoe7FfVY5+q1v34ZxjutwKmZob4qqk7vp/VGRO/XcOEnj6r4jTHXdUKnMxzvjxQrlw5SCQSXLt2DR07dsy1//r167CysoKxsXG+7WRnZ6Ndu3aYP39+rn05CXm7du1QunRprF69Gk5OTsjOzoaHhwfS09Pl6uvr68v+n/M1+nAqTo4JEybg0KFDWLRoEcqVKwcjIyN07do1V5v/FRgYiJkzZ8qVuaIyyqJKvteoKS+fvkZWZhasPxiFs7SzQPKjl5oJSsuxT9WD/ap67FP1yMzMQuK994s73Lr2EBWqlETHXvWw7Kc9Go6MvihM5hVSejWbL5mNjQ2aN2+O33//He/evZPbl5SUhL/++gt+fn6oWrUqsrOzERkZmWc7NWvWxNWrV+Hi4oJy5crJbSYmJnj27BliYmIwZcoUNG3aVDZ951MdP34c/v7+6NSpE6pWrQoHBwfEx8fne0xAQABevnwpt7mi6C7zlpmRiZvn76Bmc/mHl9Vs5omrp29oKCrtxj5VD/ar6rFPPxOJBPoGSj9zkojUpMDJfHZ2NqfYAPj111+RlpYGX19fHDt2DPfu3cPBgwfRvHlzlCxZEnPmzIGLiwv69euHAQMGYPfu3YiLi0NERAS2bdsG4P2KOM+fP0fPnj1x9uxZ3LlzB4cPH8aAAQOQlZUFKysr2NjYYNWqVbh9+zaOHDmCcePGfXLs5cqVw86dOxEdHY1Lly6hV69eCkfxc0ilUpibm8ttRX2KzY4le9FqYFP49vdBmUolMXRxP9iVKYG9Kw9rOjStxT5VD/ar6rFPVct/ZDNUqeEMeydLuJSzQ7/vmsLTywVH9l/WdGhazdBECjeP0nDzeL8qkINzCbh5lIZtyeK9kMin4NKUivFXayWVL18e586dw4wZM+Dn54dnz57BwcEBHTt2xPTp02Ur+qxYsQI//vgjhg8fjmfPnqFMmTL48ccfAQBOTk44efIkJk2aBF9fX6SlpcHZ2RktW7aEjo4OJBIJtmzZglGjRsHDwwMVK1bEsmXL0Lhx40+KfcmSJRgwYAAaNGiAEiVKYNKkSXj16tWndkmRE7ntFMxtTNF7aldYO1oh/so9TG4zF48Tnn78YMoT+1Q92K+qxz5VLStrE0yc0xlWJczw9k0q4m4+wpQRG3Dxn9iPH0wKVajujAV7JsheD5njBwAI3XQKP3+3VlNhFW2cIaKQRHD+DCmpuU43TYdARFRs6HrmvUIafYKERE1HUOwcfL5ao+dv1HGhyts8vnvCxytpAaXWmSciIiIi+tw0Oc1mxowZkEgkcpuDg0O+x0RGRqJWrVowNDSEm5sbVq5c+Yk9oBin2RARERFR0abheSRVqlRBWFiY7LWuruL7B+Pi4tC6dWsMGjQIGzduxMmTJzF8+HDY2trmeo6QKjCZJyIiIiLKh56e3kdH43OsXLkSZcqUwdKlSwG8f6jouXPnsGjRIrUk85xmQ0RERERFmjqm2aSlpeHVq1dy24cPysxx69YtODk5wdXVFT169MCdO3cUxnr69Gm0aNFCrszX1xfnzp1DRkaGSvsFYDJPRERERF+gwMBAWFhYyG2BgYG56tWtWxfr16/HoUOHsHr1aiQlJaFBgwZ49uxZnu0mJSXB3t5ersze3h6ZmZl4+lT1K2txmg0RERERFW3Zqp80HxAQkOs5PlKpNFe9Vq1ayf5ftWpV1K9fH2XLlsW6desUPgdIIpHIvc5ZPPLDclVgMk9ERERERZsaboCVSqV5Ju8fY2JigqpVq+LWrVt57ndwcEBSUpJc2ePHj6GnpwcbG5tCxZofTrMhIiIiIiqgtLQ0xMTEwNHRMc/99evXR2hoqFzZ4cOH4eXlBX19fZXHw2SeiIiIiIo0Ta4zP378eERGRiIuLg5nzpxB165d8erVK/Tr1w/A++k6ffv2ldUfOnQo7t69i3HjxiEmJgZr1qxBUFAQxo8fr+puAcBpNkRERERU1AnNLTR///599OzZE0+fPoWtrS3q1auHf/75B87OzgCAxMREJCQkyOq7urpi//79GDt2LH777Tc4OTlh2bJlalmWEmAyT0RERESk0JYtW/LdHxwcnKvM29sbFy5cUFNE8pjMExEREVGRpsy0mC8N58wTEREREWkpjswTERERUdHGkXmFmMwTERERUZEm0eANsEUdp9kQEREREWkpjswTERERUdGWrekAii4m80RERERUpHGajWKcZkNEREREpKU4Mk9ERERERRsH5hXiyDwRERERkZbiyDwRERERFW2cM68Qk3kiIiIiKtIkzOUV4jQbIiIiIiItxZF5IiIiIiraOM1GISbzRERERFSkSfjQKIU4zYaIiIiISEtxZJ6IiIiIijZOs1GIyTwRERERFW3M5RViMk9ERKRBWZdjNB1CsSMa1dB0CESfDZN5IiIiIirSJJxmoxBvgCUiIiIi0lIcmSciIiKioo0j8woxmSciIiKioo3rzCvEaTZERERERFqKI/NEREREVKTxBljFmMwTERERUdHGZF4hTrMhIiIiItJSHJknIiIioqKNI/MKcWSeiIiIiEhLcWSeiIiIiIo2Lk2pEJN5IiIiIirSuJqNYpxmQ0RERESkpTgyT0RERERFG0fmFWIyT0RERERFG5N5hTjNhoiIiIhISzGZJyIiIqKiTQjVbwUUGBiI2rVrw8zMDHZ2dujYsSNu3LiR7zERERGQSCS5tuvXr39qT+TCZJ6IiIiISIHIyEiMGDEC//zzD0JDQ5GZmYkWLVogJSXlo8feuHEDiYmJsq18+fIqj49z5omIiIioaNPgOvMHDx6Ue7127VrY2dnh/Pnz+Prrr/M91s7ODpaWlmqMjiPzRERERFTESYRQ+VZYL1++BABYW1t/tG6NGjXg6OiIpk2b4ujRo4U+Z344Mk9EREREX5y0tDSkpaXJlUmlUkilUoXHCCEwbtw4fPXVV/Dw8FBYz9HREatWrUKtWrWQlpaGDRs2oGnTpoiIiPjoaL6ymMwTERERUdGmhqUpAwMDMXPmTLmy6dOnY8aMGQqP+e6773D58mWcOHEi37YrVqyIihUryl7Xr18f9+7dw6JFi5jMExEREdEXJlv1yXxAQADGjRsnV5bfqPzIkSMREhKCY8eOoVSpUkqfr169eti4caPSx30Mk3kiIiIi+uJ8bEpNDiEERo4ciV27diEiIgKurq6FOt/Fixfh6OhYqGPzw2SeiIiIiIo2DT4BdsSIEdi0aRP+/vtvmJmZISkpCQBgYWEBIyMjAO9H+R88eID169cDAJYuXQoXFxdUqVIF6enp2LhxI3bs2IEdO3aoPD4m80RERERUtGkwmV+xYgUAoHHjxnLla9euhb+/PwAgMTERCQkJsn3p6ekYP348Hjx4ACMjI1SpUgX79u1D69atVR6fRAgN9o4GRUREwMfHBy9evFD7+p/q5uLigjFjxmDMmDGf5XzNdbp9lvMQEREVhmhUQ9MhFDthkT9q9Pytyk9UeZsHbi1QeZuaoNF15h8/fowhQ4agTJkykEqlcHBwgK+vL06fPq3S8zRu3Fjlie7gwYOhq6uLLVu2qLTdwoiKisLgwYM1HUaR0m5YC6yP/Q373v6F36Lmw+OrSpoOSeuxT9WD/ap67FPVY5+qT89v6iMs8kcM+66ZpkMp2oRQ/VZMaDSZ79KlCy5duoR169bh5s2bCAkJQePGjfH8+XNNhvVRb9++xdatWzFhwgQEBQVpLI709HQAgK2tLYyNjTUWR1Hj3b0Bhi3pj81zd2BYzYm4ciIGc/dPhm3pEpoOTWuxT9WD/ap67FPVY5+qT8VKjmjdrgZibz/SdCikxTSWzCcnJ+PEiROYP38+fHx84OzsjDp16iAgIABt2rSR1UtISECHDh1gamoKc3NzdO/eHY8e/d+b3t/fHx07dpRre8yYMbJ5Tf7+/oiMjMQvv/wCiUQCiUSC+Ph4Wd3z58/Dy8sLxsbGaNCgAW7cuPHR2Ldv3w53d3cEBATg5MmTcu39N6a5c+fC3t4elpaWmDlzJjIzMzFhwgRYW1ujVKlSWLNmjdxxDx48gJ+fH6ysrGBjY4MOHTrItZ3TbmBgIJycnFChQgUA76fZLF26VK5vBw8eDHt7exgaGsLDwwN79+4FADx79gw9e/ZEqVKlYGxsjKpVq2Lz5s0fvWZt0mVsWxxccwQHgo4g4foDrBgbjCf3nqLdsBaaDk1rsU/Vg/2qeuxT1WOfqoehkT4CprTHkoX78eZ1qqbDKfqyheq3YkJjybypqSlMTU2xe/fuXE/fyiGEQMeOHfH8+XNERkYiNDQUsbGx8PPzK/B5fvnlF9SvXx+DBg1CYmIiEhMTUbp0adn+yZMn4+eff8a5c+egp6eHAQMGfLTNoKAg9O7dGxYWFmjdujXWrl2bq86RI0fw8OFDHDt2DIsXL8aMGTPQtm1bWFlZ4cyZMxg6dCiGDh2Ke/fuAXg/2u/j4wNTU1McO3YMJ06cgKmpKVq2bCkbgQeA8PBwxMTEIDQ0VJag/1d2djZatWqFU6dOYePGjbh27RrmzZsHXV1dAEBqaipq1aqFvXv34sqVKxg8eDD69OmDM2fOFLhPizI9fT1UqOWG84cvyZWfD72MKvUrKjiK8sM+VQ/2q+qxT1WPfao+o8b44szpWFw4H6/pULSDyFb9VkxobDUbPT09BAcHY9CgQVi5ciVq1qwJb29v9OjRA56engCAsLAwXL58GXFxcbIEfMOGDahSpQqioqJQu3btj57HwsICBgYGMDY2hoODQ679c+bMgbe3NwDghx9+QJs2bZCamgpDQ8M827t16xb++ecf7Ny5EwDQu3dvjBo1CtOnT4eOzv/9bmRtbY1ly5ZBR0cHFStWxIIFC/D27Vv8+OP7G0gCAgIwb948nDx5Ej169MCWLVugo6ODP//8ExKJBMD7u6QtLS0RERGBFi3ej4CYmJjgzz//hIGBQZ7xhYWF4ezZs4iJiZGN3Lu5ucn2lyxZEuPHj5e9HjlyJA4ePIjt27ejbt26H+3Pos6ihBl09XTx4lGyXPmLR8mwcrDUSEzajn2qHuxX1WOfqh77VD0aN3FH+QoOGD4k92AgkbI0Pmf+4cOHCAkJga+vLyIiIlCzZk0EBwcDAGJiYlC6dGm5kXR3d3dYWloiJiZGJTHk/OIAQLaQ/+PHjxXWDwoKgq+vL0qUeD9XsHXr1khJSUFYWJhcvSpVqsgl9/b29qhatarsta6uLmxsbGTnOn/+PG7fvg0zMzPZXy2sra2RmpqK2NhY2XFVq1ZVmMgDQHR0NEqVKiVL5D+UlZWFOXPmwNPTEzY2NjA1NcXhw4flllP6r7S0NLx69UpuyxZZCs9fVHx4X4tEIsEXunCTyrBP1YP9qnrsU9Vjn6qOra0ZRoxsjsCfQpCRXvQ/T4sM3gCrkMbXmTc0NETz5s3RvHlzTJs2Dd9++y2mT58Of39/CCFko9T/9d9yHR2dXD9QMjIyCnx+fX192f9z2szOzvtPL1lZWVi/fj2SkpKgp6cnVx4UFCQbPf+w3Zy28yrLOVd2djZq1aqFv/76K9d5bW1tZf83MTHJ93pyHl6gyM8//4wlS5Zg6dKlqFq1KkxMTDBmzBi5qTz/FRgYiJkzZ8qVuaIyyqJKvufRlJdPXyMrMwvWH4wYWdpZIPnRS80EpeXYp+rBflU99qnqsU9Vr3xFR1hZm2DFqv+b1qurp4Oq1cqgYycvtGo+H9nFaD63yrBPFNLoyHxe3N3dkZKSIvt/QkKCbF45AFy7dg0vX75E5cqVAbxPdBMTE+XaiI6OlnttYGCArKxP/+13//79eP36NS5evIjo6GjZtn37duzevRvPnj0rdNs1a9bErVu3YGdnh3LlysltFhYWBW7H09MT9+/fx82bN/Pcf/z4cXTo0AG9e/dGtWrV4Obmhlu3bilsLyAgAC9fvpTbXFF0lyTLzMjEzfN3ULO5p1x5zWaeuHr64zc3U27sU/Vgv6oe+1T12Keqd/F8PL71X40h3wbJthvXHyI87AqGfBvERJ6UprFk/tmzZ2jSpAk2btwomxe/fft2LFiwAB06dAAANGvWDJ6envjmm29w4cIFnD17Fn379oW3tze8vLwAAE2aNMG5c+ewfv163Lp1C9OnT8eVK1fkzuXi4oIzZ84gPj4eT58+VTjy/jFBQUFo06YNqlWrBg8PD9nWpUsX2NraYuPGjYXuj2+++QYlSpRAhw4dcPz4ccTFxSEyMhKjR4/G/fv3C9yOt7c3vv76a3Tp0gWhoaGIi4vDgQMHcPDgQQBAuXLlEBoailOnTiEmJgZDhgyRPZY4L1KpFObm5nKbjkS30Nf5OexYshetBjaFb38flKlUEkMX94NdmRLYu/KwpkPTWuxT9WC/qh77VPXYp6r17l064uOeyG2p7zLw6uU7xMc90XR4RRen2SiksWk2pqamqFu3LpYsWYLY2FhkZGSgdOnSGDRokOwmUYlEgt27d2PkyJH4+uuvoaOjg5YtW2L58uWydnx9fTF16lRMnDgRqampGDBgAPr27Yt///1XVmf8+PHo168f3N3d8e7dO8TFxSkd76NHj7Bv3z5s2rQp1z6JRILOnTsjKCgIo0ePLkRvAMbGxjh27BgmTZqEzp074/Xr1yhZsiSaNm0Kc3NzpdrasWMHxo8fj549eyIlJQXlypXDvHnzAABTp05FXFwcfH19YWxsjMGDB6Njx454+bL4/Lk0ctspmNuYovfUrrB2tEL8lXuY3GYuHic81XRoWot9qh7sV9Vjn6oe+5SoaJMI3sFCSmqu003TIRARESkkGtXQdAjFTljkjxo9f6tSo1Te5oH7y1TepiZo/AZYIiIiIqJ8cexZoSJ3AywRERERERUMR+aJiIiIqGgr5OIlXwIm80RERERUtHGajUKcZkNEREREpKU4Mk9ERERERRtH5hViMk9ERERERRufjKsQp9kQEREREWkpjswTERERUZEmBFezUYQj80REREREWooj80RERERUtHHOvEJM5omIiIioaONqNgpxmg0RERERkZbiyDwRERERFW3ZvAFWESbzRERERFS0cZqNQpxmQ0RERESkpTgyT0RERERFmuA0G4U4Mk9EREREpKU4Mk9ERERERRvnzCvEZJ6IiIiIijY+NEohTrMhIiIiItJSHJknIiIioqJN8AZYRZjMExEREVGRJjjNRiFOsyEiIiIi0lJM5omIiIioaBPZqt+U9Pvvv8PV1RWGhoaoVasWjh8/nm/9yMhI1KpVC4aGhnBzc8PKlSsLe/X5YjJPRERERJSPrVu3YsyYMZg8eTIuXryIRo0aoVWrVkhISMizflxcHFq3bo1GjRrh4sWL+PHHHzFq1Cjs2LFD5bFJhODCnaSc5jrdNB0CERGRQqJRDU2HUOyERf6o0fM31/VTeZuhWVsLXLdu3bqoWbMmVqxYISurXLkyOnbsiMDAwFz1J02ahJCQEMTExMjKhg4dikuXLuH06dOfFvgHODJPREREREWbBqfZpKen4/z582jRooVceYsWLXDq1Kk8jzl9+nSu+r6+vjh37hwyMjKUv/58cDUbIiIiIvripKWlIS0tTa5MKpVCKpXKlT19+hRZWVmwt7eXK7e3t0dSUlKebSclJeVZPzMzE0+fPoWjo6MKruA9JvOktNDs7ZoOoUDS0tIQGBiIgICAXN+YVDjsU9Vjn6oe+1Q92K+qxz4tOHXkHjNmzMDMmTPlyqZPn44ZM2bkWV8ikci9FkLkKvtY/bzKPxXnzFOx9erVK1hYWODly5cwNzfXdDjFAvtU9dinqsc+VQ/2q+qxTzWroCPz6enpMDY2xvbt29GpUydZ+ejRoxEdHY3IyMhcbX/99deoUaMGfvnlF1nZrl270L17d7x9+xb6+voquw7OmSciIiKiL45UKoW5ubncltdfSAwMDFCrVi2EhobKlYeGhqJBgwZ5tl2/fv1c9Q8fPgwvLy+VJvIAk3kiIiIionyNGzcOf/75J9asWYOYmBiMHTsWCQkJGDp0KAAgICAAffv2ldUfOnQo7t69i3HjxiEmJgZr1qxBUFAQxo8fr/LYOGeeiIiIiCgffn5+ePbsGWbNmoXExER4eHhg//79cHZ2BgAkJibKrTnv6uqK/fv3Y+zYsfjtt9/g5OSEZcuWoUuXLiqPjck8FVtSqRTTp0/nTUUqxD5VPfap6rFP1YP9qnrsU+0yfPhwDB8+PM99wcHBucq8vb1x4cIFNUfFG2CJiIiIiLQW58wTEREREWkpJvNERERERFqKyTxpHSEEBg8eDGtra0gkEkRHRyusK5FIsHv37s8W25ckPj7+o/1PpAx/f3907NhR02F8soiICEgkEiQnJ2s6lCInODgYlpaWmg7jkxWnr7GLiwuWLl2q6TDoEzCZpyLr1KlT0NXVRcuWLeXKDx48iODgYOzdu1d2R7kiiYmJaNWqlbpD1XqK+jo/pUuX/mj/Fyf+/v6QSCS5NmX6TFvkXOu8efPkynfv3q2SJxeq4xfBd+/ewcrKCtbW1nj37p3K2i2MBg0aIDExERYWFp/tnPfu3cPAgQPh5OQEAwMDODs7Y/To0Xj27Nlni6Eg/Pz8cPPmTZW09fjxYwwZMgRlypSBVCqFg4MDfH19cfr0aZW0n6Nx48YYM2aMStscPHgwdHV1sWXLFpW2WxhRUVEYPHiwpsOgT8BknoqsNWvWYOTIkThx4oTcck+xsbFwdHREgwYN4ODgAD293IsypaenAwAcHBy4SkABKOrr/Ojq6irs/+KqZcuWSExMlNs2b95cqLaEEMjMzFRxhKpjaGiI+fPn48WLFyptN+d7U9V27NgBDw8PuLu7Y+fOnWo5R0FkZGTAwMAADg4OKn9kuyJ37tyBl5cXbt68ic2bN+P27dtYuXIlwsPDUb9+fTx//vyzxFEQRkZGsLOzU0lbXbp0waVLl7Bu3TrcvHkTISEhaNy4cZG63ry8ffsWW7duxYQJExAUFKSxOHK+F21tbWFsbKyxOEgFBFER9ObNG2FmZiauX78u/Pz8xMyZM4UQQvTr108AkG3Ozs5CCCG8vb3FiBEjxNixY4WNjY34+uuvhRBCABC7du2StXvv3j3h5+cnrKyshLGxsahVq5b4559/hBBC3L59W7Rv317Y2dkJExMT4eXlJUJDQz/rdWuCor4WQojnz5+LXr16iRIlSghDQ0NRrlw5sWbNGiGEEHFxcQKAuHjxohBCiMzMTDFgwADh4uIiDA0NRYUKFcTSpUs1cUlq0a9fP9GhQ4c8933YF0II8eLFCwFAHD16VAghxNGjRwUAcfDgQVGrVi2hr68vjhw5IlJTU8XIkSOFra2tkEqlomHDhuLs2bOydnKO27t3r/D09BRSqVTUqVNHXL58WS6GkydPikaNGglDQ0NRqlQpMXLkSPHmzZtCX2vbtm1FpUqVxIQJE2Tlu3btEh9+bPzvf/8T7u7uwsDAQDg7O4tFixbJ7Xd2dhazZ88W/fr1E+bm5qJv375y38MAhLe3t+y8HTp0EAsXLhQODg7C2tpaDB8+XKSnp3805saNG4uVK1eKFStWCB8fn1z7AYiVK1eKNm3aCCMjI1GpUiVx6tQpcevWLeHt7S2MjY1FvXr1xO3bt+WOCwkJETVr1hRSqVS4urqKGTNmiIyMDLl2V6xYIdq3by+MjY3FtGnTZF+zFy9eyOqdOHFCfP3118LIyEhYWlqKFi1aiOfPnwshhDhw4IBo2LChsLCwENbW1qJNmza54shPy5YtRalSpcTbt2/lyhMTE4WxsbEYOnSoEEKI1NRUMWHCBFGqVClhYGAgypUrJ/78809Z/atXr4pWrVoJExMTYWdnJ3r37i2ePHki2/+xOHO+D3bs2CEaN24sjIyMhKenpzh16pSsztq1a4WFhYXsdWF/7uZ8f0VERORb7+7du6J9+/bCxMREmJmZiW7duomkpCTZ/ry+r0ePHi33nvzw/RoXFyf7GoeFhYlatWoJIyMjUb9+fXH9+vWPxh4cHCzq1asnkpOThZGRkYiLi5PbnxPTnDlzhJ2dnbCwsJC978aPHy+srKxEyZIlRVBQkNxx9+/fF927dxeWlpbC2tpatG/fXq7tnHbnzp0rHB0dZZ+fzs7OYsmSJXJ9O2jQIGFnZyekUqmoUqWK2LNnjxBCiKdPn4oePXqIkiVLCiMjI+Hh4SE2bdr00Wsm9WIyT0VSUFCQ8PLyEkIIsWfPHuHi4iKys7NFcnKymDVrlihVqpRITEwUjx8/FkK8T+ZNTU3FhAkTxPXr10VMTIwQQj6Zf/36tXBzcxONGjUSx48fF7du3RJbt26VfdBER0eLlStXisuXL4ubN2+KyZMnC0NDQ3H37t3P3wGfkaK+FkKIESNGiOrVq4uoqCgRFxcnQkNDRUhIiBAidwKbnp4upk2bJs6ePSvu3LkjNm7cKIyNjcXWrVs1cl2qpqpk3tPTUxw+fFjcvn1bPH36VIwaNUo4OTmJ/fv3i6tXr4p+/foJKysr8ezZM7njKleuLA4fPiwuX74s2rZtK1xcXGRJ7uXLl4WpqalYsmSJuHnzpjh58qSoUaOG8Pf3/6Rr3blzpzA0NBT37t0TQuRO5s+dOyd0dHTErFmzxI0bN8TatWuFkZGRWLt2rayOs7OzMDc3FwsXLhS3bt0St27dEmfPnpUlQv+vvTuPaupa+wD8C4gyJCgiAlJKVAQZ1TiCAqJQ1CsLBa8oDlAUq2jFudcBRa29VuvstXVAwaFVb7HiCKgVtU4MihNhEKFixQmHigOV5P3+oJxLJCAgVun3PmtlrWSfnT2dnJM3OzsnBQUFQl/LAv6xY8eSXC6n/fv3k66uLm3YsKHK9l6/fp0aNWpEDx8+pMLCQmrUqBHl5OSo5AFAZmZmtGvXLsrMzKQBAwaQVCqlXr16UVxcHKWnp1O3bt2oT58+wnPi4uJIX1+foqKiKCcnhxISEkgqlVJERIRKuc2bN6fIyEjKycmhvLy8CsH8xYsXqVGjRjRu3DhKS0ujq1ev0po1a4RA+ccff6SYmBjKysqiixcvkre3Nzk4OJBCoXjjviosLCSRSERfffWV2u0hISFkYGBASqWSBg8eTObm5rRnzx7Kycmho0eP0s6dO4mI6Pbt29SsWTOaOXMmyeVyunDhAnl6eqp8MHpTO8uOg7Zt29KBAwcoMzOTBg0aRBYWFsIHoNeD+dqed1+9ekVisZgmTZpEL1++VJtHqVRShw4dqEePHpSSkkLnzp0jmUwmBOpEbw7mHz9+TE5OThQSEkIFBQVUUFBAJSUlwj7u2rUrJSYm0rVr18jFxYWcnZ2rbDcRkYuLC61du5aIiPz8/Gju3Lkq2wMDA0kikdD48eMpIyODIiMjCQB5eXnRokWLKCsrixYuXEhaWlp08+ZNIiJ69uwZtWnThoKDg+ny5cuUnp5OAQEBZG1tTcXFxUK5YrGYRowYQVevXqUrV64QkWowr1AoqFu3bmRnZ0cJCQmUk5ND+/fvp0OHDhFR6QeGpUuX0sWLFyknJ4dWr15NmpqawqQYez84mGcfJGdnZ2FW99WrV9SsWTNhtmbFihXCjEIZNzc3at++fYVyygfz69evJ4lEIgQO1WFra0tr1qypXSfqiarG2tvbmz799FO1z1MXwL4uNDSU/Pz86rzN70NgYCBpamqSnp6eym3BggU1Cub37t0r5CkqKiItLS3asWOHkPbHH39QixYtaMmSJSrPKwu6iEoDOB0dHeGD0ogRI2jMmDEq7T116hRpaGjQixcvatXXsgCnW7duFBwcTEQVg/mAgADy9PRUee706dPJ1tZWeGxhYUEDBgxQyVPZaycwMJAsLCyopKRESPvnP/9J/v7+VbZ31qxZKnX4+PjQ7NmzVfIAoDlz5giPz549SwBUZjd/+OEH0tbWFh67uLhUCJK3bdtGpqamKuVOmjRJJc/rwfzQoUOpe/fuVfahvHv37hEAIdiqyrlz5yp8A1ne8uXLCQCdP3+eAFQ66x0eHk6ffPKJSlp+fj4BoMzMzGq1s2y/vj7bD0CYYHk9mFenuufdH3/8kQwMDEhbW5ucnZ1p5syZdOnSJWF7QkICaWpqCgFv+faUffv1pmCeqPT9JSwsTCVP+Zn5MgcPHiQAVR5zWVlZpKWlJXyQ++mnn8jc3Fzlg1vZcVA+zdramlxcXITHJSUlpKenRz/88AMRlU7KWFtbCxMxRETFxcWko6ND8fHxQrnGxsZCcF+mfDAfHx9PGhoale5zdfr160dTp06tdn5W93jNPPvgZGZmIikpCUOGDAEANGjQAP7+/ti8eXOVz+vUqVOV29PS0tChQwc0bdpU7fZnz55hxowZsLW1RZMmTSAWi5GRkVHtNeT10ZvGety4cdi5cyfat2+PGTNm4MyZM1WW991336FTp04wMjKCWCzGxo0b/1bj5+7ujrS0NJXb+PHja1RG+ddpTk4OXr16he7duwtpWlpa6NKlC+RyucrznJychPtNmzaFtbW1kCc1NRVRUVEQi8XCzcvLC0qlErm5ubXpquDrr79GdHQ00tPTK2yTy+UqbQeA7t27Izs7GwqFQm2f38TOzg6amprCY1NTU9y7d6/S/AqFAtHR0Rg+fLiQNnz4cERHR6u0AQAcHR2F+8bGxgAABwcHlbSXL1/i999/B1A6rgsWLFAZ15CQEBQUFOD58+fV7l9aWhp69+5d6facnBwEBASgVatW0NfXR8uWLQGgTo4d+vN/IXNzc6GpqQk3Nze1+VJTU3H8+HGVvrZt21ZoX03aWX6cTU1NAaDSffg2510/Pz/cvn0b+/btg5eXFxITEyGTyYR/4pTL5TA3N4e5ubnwnLJ6Xj++aqsmfQWAyMhIeHl5oVmzZgCAfv364dmzZzh69KhKPjs7O2ho/C9EMzY2VnmtampqwtDQUKgrNTUV169fh0QiEfZf06ZN8fLlS2H/AaWv94YNG1bavrS0NHz00UewsrJSu12hUGDRokVwdHSEoaEhxGIxEhIS/lbn+fro/88v11i9ERkZiZKSEpiZmQlpRAQtLa0qf4ynp6dXZbk6OjpVbp8+fTri4+PxzTffwNLSEjo6Ohg0aNA7+8Heh+BNY923b1/8+uuvOHjwII4ePYrevXtj/Pjx+OabbyqUtXv3bkyePBnLli2Dk5MTJBIJli5divPnz/+VXXqn9PT0YGlpWSG9qKgIwP8CJ6D0h5CVlVGmLP/rP5Qkomr9eLIsj1KpxGeffYaJEydWyPPxxx+/sZyquLq6wsvLC7NmzUJQUNAb21l+DMq86dgsT0tLS+WxSCSCUqmsNH98fDx+++03+Pv7q6QrFAokJCSoXM2qfNll7VaXVlafUqnE/Pnz4evrW6FebW1t4f7bnnu8vb1hbm6OjRs3okWLFlAqlbC3t6/WucfS0hIikQjp6elqL+uZkZEBAwODN/7AUalUwtvbG19//XWFbWVBanXbWdWYvu5tz7va2trw9PSEp6cn5s6di9GjR2PevHkICgqq9Dgqn66hoVHhNVvZsatOTfqqUCiwdetW3LlzR+XCAQqFApGRkfjkk0/UlltWdlXHhlKpRMeOHbFjx44K9RoZGQn33/a1umzZMqxYsQIrV66Eg4MD9PT0MGnSpL/1+2R9wME8+6CUlJRg69atWLZsmcqJDSidhVF3oqouR0dHbNq0CQ8fPlQ7O3/q1CkEBQVh4MCBAEoDtLy8vFrX96GrzlhPmDABRkZGCAoKQlBQEFxcXDB9+nS1wfypU6fg7OyM0NBQIa38jNDfWdmbZUFBATp06AAA1brsoqWlJRo2bIhffvkFAQEBAEoDiZSUlAqXwjt37pwQmD969AhZWVnCzKlMJsO1a9fUftCoC4sXL0b79u0rzNbZ2tril19+UUk7c+YMrKysVGbXX1c2M/j6zHltREZGYsiQIZg9e3aFNkdGRr7VpWllMhkyMzPfelwdHR1x7NgxzJ8/v8K2wsJCyOVyrF+/Hi4uLgBQYUyrYmhoCE9PT6xbtw6TJ09WCcbu3LmDHTt2YOTIkXBwcIBSqcSJEyfg4eFRoRyZTIaYmBhIpVK1V6h623ZWpq7Pu7a2tsJ/i9ja2uLmzZvIz88XZufT09Px5MkT2NjYACg9dq9evapSRlpamkrg3LBhwzp5rR46dAhPnz7FxYsXVY6PjIwMDBs2DIWFhTA0NKxV2TKZDLt27ULz5s2hr69f6zY6Ojri1q1byMrKUjs7f+rUKfj4+AjfhCmVSmRnZwvjyd4PXmbDPigHDhzAo0ePMGrUKNjb26vcBg0a9FaX8Ro6dChMTEwwYMAAnD59Gjdu3EBMTIxwTWJLS0vs2bMHaWlpuHTpEgICAqqcEazvqjPWc+fORWxsLK5fv45r167hwIEDlZ60LS0tkZKSgvj4eGRlZSE8PBzJycl/ca/ereLiYty5c0fl9uDBA+jo6KBbt25YvHgx0tPTcfLkScyZM+eN5enp6WHcuHGYPn064uLikJ6ejpCQEDx//hyjRo1SybtgwQIcO3YMV69eRVBQEJo1aybMxH7xxRc4e/Ysxo8fj7S0NGRnZ2Pfvn34/PPP66TfDg4OGDZsGNasWaOSPnXqVBw7dgwLFy5EVlYWoqOjsXbtWkybNq3K8po3bw4dHR3ExcXh7t27ePLkSa3adf/+fezfvx+BgYEVXsOBgYHYt28f7t+/X6uyAWDu3LnYunUrIiIicO3aNcjlcuzatata+7a8mTNnIjk5GaGhobh8+TIyMjLw7bff4sGDBzAwMIChoSE2bNiA69ev4+eff8aUKVNqVP7atWtRXFwMLy8vnDx5Evn5+YiLi4OnpyfMzMywaNEiSKVSBAYGIjg4GHv37kVubi4SExOxe/duAMD48ePx8OFDDB06FElJSbhx4wYSEhIQHBwMhUJRJ+1Up7bn3cLCQvTq1Qvbt2/H5cuXkZubi//+979YsmQJfHx8AAAeHh5wdHTEsGHDcOHCBSQlJWHkyJFwc3MTlkb16tULKSkp2Lp1K7KzszFv3rwKwb1UKsX58+eRl5eHBw8e1Pp9ITIyEv/4xz/Qrl07ldeqn58fjIyMsH379lqVCwDDhg1Ds2bN4OPjg1OnTiE3NxcnTpxAWFgYbt26Ve1y3Nzc4OrqCj8/Pxw5cgS5ubk4fPgw4uLiAJTuryNHjuDMmTOQy+X47LPPcOfOnVq3m9UNDubZByUyMhIeHh5q/2zFz88PaWlptT6RNmzYEAkJCWjevDn69esHBwcHLF68WJghWbFiBQwMDODs7Axvb294eXlBJpO9VX8+ZNUZ6wYNGmDmzJlwdHSEq6trlX9yMnbsWPj6+sLf3x9du3ZFYWGhyiz930FcXBxMTU1Vbj169ABQeq3+V69eoVOnTggLC8OXX35ZrTIXL14MPz8/jBgxAjKZDNevX0d8fDwMDAwq5AsLC0PHjh1RUFCAffv2CTPcjo6OOHHiBLKzs+Hi4oIOHTogPDxcWB5RFxYuXFhhOYJMJsPu3buxc+dO2NvbY+7cuViwYEGF5Tiva9CgAVavXo3169ejRYsWQvBVU1u3boWenp7a9eju7u6QSCTYtm1brcoGAC8vLxw4cABHjhxB586d0a1bNyxfvhwWFhY1KsfKygoJCQm4dOkSunTpAicnJ8TGxqJBgwbQ0NDAzp07kZqaCnt7e0yePBlLly6tUflt2rRBSkoKWrduDX9/f7Ru3RpjxoyBu7s7zp49K3wT+e2332LQoEEIDQ1F27ZtERISgmfPngEAWrRogdOnT0OhUMDLywv29vYICwtD48aNoaGhUSftVKe2512xWIyuXbtixYoVcHV1hb29PcLDwxESEoK1a9cC+N8/gBsYGMDV1RUeHh5o1aoVdu3aJZTj5eWF8PBwzJgxA507d8bTp08xcuRIlbqmTZsGTU1N2NrawsjIqFbrw+/evYuDBw/Cz8+vwjaRSARfX9+3mqzS1dXFyZMn8fHHH8PX1xc2NjYIDg7GixcvajxTHxMTg86dO2Po0KGwtbXFjBkzhG8mwsPDIZPJ4OXlhZ49ewoTZOz9EpG6BY6MMcY+CImJiXB3d8ejR4/QpEmT990cxhhjHxiemWeMMcYYY6ye4mCeMcYYY4yxeoqX2TDGGGOMMVZP8cw8Y4wxxhhj9RQH84wxxhhjjNVTHMwzxhhjjDFWT3EwzxhjjDHGWD3FwTxjjDHGGGP1FAfzjDH2AZBKpVi5cuU7rycvLw8ikQhpaWnvvK73ITExESKRCI8fP37fTWGMsb8EB/OMMQYgKCgIIpEIIpEIWlpaMDY2hqenJzZv3gylUlln9URFRan9J9fk5GSMGTOmzuoBSvv0+l+tm5ubo6CgAPb29nVa1/vQs2dPTJo0SSXN2dkZBQUFaNy48V/alsr2K2OMvWsczDPG2J/69OmDgoIC5OXl4fDhw3B3d0dYWBj69++PkpKSd1q3kZERdHV132kdAKCpqQkTExM0aNDgndf1PjRs2BAmJiYQiUTvuymMMfaX4GCeMcb+1KhRI5iYmMDMzAwymQyzZs1CbGwsDh8+jKioKCHfkydPMGbMGDRv3hz6+vro1asXLl26JGy/dOkS3N3dIZFIoK+vj44dOyIlJQWJiYn49NNP8eTJE+FbgIiICAAVl9mIRCJs2rQJAwcOhK6uLtq0aYN9+/YJ2xUKBUaNGoWWLVtCR0cH1tbWWLVqlbA9IiIC0dHRiI2NFepKTExUu8zmxIkT6NKlCxo1agRTU1P861//Uvnw0rNnT0ycOBEzZsxA06ZNYWJiIrS7Kps3b4adnZ1Q7oQJE4RtN2/ehI+PD8RiMfT19TF48GDcvXtXpf3t27fHtm3bIJVK0bhxYwwZMgRPnz4FUPqtw4kTJ7Bq1Sqhf3l5eRWW2ZTNmMfHx8PGxgZisVj40Fbeli1bYGNjA21tbbRt2xbr1q0TtpWN2Z49e+Du7g5dXV20a9cOZ8+eBYAq9ytjjL1zxBhjjAIDA8nHx0fttnbt2lHfvn2JiEipVFL37t3J29ubkpOTKSsri6ZOnUqGhoZUWFhIRER2dnY0fPhwksvllJWVRbt376a0tDQqLi6mlStXkr6+PhUUFFBBQQE9ffqUiIgsLCxoxYoVQp0A6KOPPqLvv/+esrOzaeLEiSQWi4U6/vjjD5o7dy4lJSXRjRs3aPv27aSrq0u7du0iIqKnT5/S4MGDqU+fPkJdxcXFlJubSwDo4sWLRER069Yt0tXVpdDQUJLL5fTTTz9Rs2bNaN68eUJb3NzcSF9fnyIiIigrK4uio6NJJBJRQkJCpeO5bt060tbWppUrV1JmZiYlJSUJ/VMqldShQwfq0aMHpaSk0Llz50gmk5Gbm5vw/Hnz5pFYLCZfX1+6cuUKnTx5kkxMTGjWrFlERPT48WNycnKikJAQoX8lJSV0/PhxAkCPHj0iIqItW7aQlpYWeXh4UHJyMqWmppKNjQ0FBAQIdW3YsIFMTU0pJiaGbty4QTExMdS0aVOKiooiIhLGrG3btnTgwAHKzMykQYMGkYWFBb169arK/coYY+8aB/OMMUZVB/P+/v5kY2NDRETHjh0jfX19evnypUqe1q1b0/r164mISCKRCIHg67Zs2UKNGzeukK4umJ8zZ47wuKioiEQiER0+fLjSPoSGhpKfn1+VfXo9mJ81axZZW1uTUqkU8vznP/8hsVhMCoWCiEqD+R49eqiU07lzZ/riiy8qbUuLFi1o9uzZarclJCSQpqYm3bx5U0i7du0aAaCkpCQiKg3mdXV16ffffxfyTJ8+nbp27So8dnNzo7CwMJWy1QXzAOj69esq/TM2NhYem5ub0/fff69SzsKFC8nJyYmI/jdmmzZtqtBeuVwu1KNuvzLG2LvGy2wYY+wNiEhYg52amoqioiIYGhpCLBYLt9zcXOTk5AAApkyZgtGjR8PDwwOLFy8W0mvK0dFRuK+npweJRIJ79+4Jad999x06deoEIyMjiMVibNy4ETdv3qxRHXK5HE5OTiprzLt3746ioiLcunVLbVsAwNTUVKUt5d27dw+3b99G7969K63T3Nwc5ubmQpqtrS2aNGkCuVwupEmlUkgkkmrVWRVdXV20bt1abTn3799Hfn4+Ro0apbI/v/zyywr7rfwYmJqaCn1ljLH36e/5CyjGGKtDcrkcLVu2BAAolUqYmpoiMTGxQr6yq5lEREQgICAABw8exOHDhzFv3jzs3LkTAwcOrFG9WlpaKo9FIpFwZZ3du3dj8uTJWLZsGZycnCCRSLB06VKcP3++RnWU/6BSPq2svuq05XU6Ojo1rlNdek3qrIq6csr6WFbexo0b0bVrV5V8mpqalZZT1s66vNIRY4zVBgfzjDFWhZ9//hlXrlzB5MmTAQAymQx37txBgwYNIJVKK32elZUVrKysMHnyZAwdOhRbtmzBwIED0bBhQygUirdu16lTp+Ds7IzQ0FAh7fWZ5OrUZWtri5iYGJVA+syZM5BIJDAzM6tV2yQSCaRSKY4dOwZ3d3e1dd68eRP5+fnC7Hx6ejqePHkCGxubatdTF2NpbGwMMzMz3LhxA8OGDat1OXW1XxljrKZ4mQ1jjP2puLgYd+7cwW+//YYLFy7gq6++go+PD/r374+RI0cCADw8PODk5IQBAwYgPj4eeXl5OHPmDObMmYOUlBS8ePECEyZMQGJiIn799VecPn0aycnJQpAqlUpRVFSEY8eO4cGDB3j+/Hmt2mppaYmUlBTEx8cjKysL4eHhSE5OVskjlUpx+fJlZGZm4sGDB3j16lWFckJDQ5Gfn4/PP/8cGRkZiI2Nxbx58zBlyhRoaNT+LSIiIgLLli3D6tWrkZ2djQsXLmDNmjUASsfQ0dERw4YNw4ULF5CUlISRI0fCzc0NnTp1qnYdUqkU58+fR15eHh48eFDrWfKIiAj8+9//xqpVq5CVlYUrV65gy5YtWL58eY3aUhf7lTHGaoqDecYY+1NcXBxMTU0hlUrRp08fHD9+HKtXr0ZsbKyw5EIkEuHQoUNwdXVFcHAwrKysMGTIEOTl5cHY2BiampooLCzEyJEjYWVlhcGDB6Nv376YP38+gNI/NRo7diz8/f1hZGSEJUuW1KqtY8eOha+vL/z9/dG1a1cUFhaqzNIDQEhICKytrYV19adPn65QjpmZGQ4dOoSkpCS0a9cOY8eOxahRozBnzpxatatMYGAgVq5ciXXr1sHOzg79+/dHdnY2gNIx3Lt3LwwMDODq6goPDw+0atUKu3btqlEd06ZNg6amJmxtbWFkZFTj3wuUGT16NDZt2oSoqCg4ODjAzc0NUVFRwtKq6qir/coYYzUlorKFg4wxxhhjjLF6hWfmGWOMMcYYq6c4mGeMMcYYY6ye4mCeMcYYY4yxeoqDecYYY4wxxuopDuYZY4wxxhirpziYZ4wxxhhjrJ7iYJ4xxhhjjLF6ioN5xhhjjDHG6ikO5hljjDHGGKunOJhnjDHGGGOsnuJgnjHGGGOMsXqKg3nGGGOMMcbqqf8DTv7WZ75E3sUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# and when looking only at the first two age clusters:\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# filter the dataframe to the first two age clusters\n", + "age_mask = travel_trip_complete_christos_df[\"traveler_age_segment\"].isin([\n", + " \"18-25 Students-Early Professionals\",\n", + " \"26-33 Young Professionals\",\n", + "])\n", + "df_sub = travel_trip_complete_christos_df[age_mask]\n", + "\n", + "# nationality_continent x destination_continent counts\n", + "ct = pd.crosstab(\n", + " df_sub[\"traveler_nationality_continent\"],\n", + " df_sub[\"destination_continent\"]\n", + ")\n", + "\n", + "plt.figure(figsize=(8,5))\n", + "sns.heatmap(ct, annot=True, fmt=\"d\", cmap=\"viridis\")\n", + "plt.xlabel(\"Destination continent\")\n", + "plt.ylabel(\"Traveler nationality continent\")\n", + "plt.title(\"Nationality vs destination (Travelers younger than 33)\")\n", + "plt.tight_layout()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "id": "01467373-ebcd-4801-a0ad-1b7386edd110", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# testing duration's correlation to inter vs intra" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "id": "101c9162-1896-4685-aea2-5a657bde5dc6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# and the same but this time per continent\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "sns.boxplot(\n", + " data=travel_trip_complete_christos_df,\n", + " x=\"destination_continent\",\n", + " y=\"duration_days\"\n", + ")\n", + "\n", + "plt.xlabel(\"Destination continent\")\n", + "plt.ylabel(\"Trip duration (days)\")\n", + "plt.title(\"Trip duration by destination continent\")\n", + "plt.xticks(rotation=30)\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "1ca37a3e-c510-4ba8-92b9-b877dc4071e6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(12,5))\n", + "sns.boxplot(\n", + " data=travel_trip_complete_christos_df,\n", + " x=\"destination_continent\",\n", + " y=\"duration_days\",\n", + " hue=\"traveler_age_segment\"\n", + ")\n", + "\n", + "plt.xlabel(\"Destination continent\")\n", + "plt.ylabel(\"Trip duration (days)\")\n", + "plt.title(\"Trip duration by destination continent and age segment\")\n", + "plt.xticks(rotation=30)\n", + "plt.tight_layout()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "53f0fb6c-c7f4-4e4d-9866-08fc9dd1a21c", + "metadata": {}, + "source": [ + "### Hypothesis #3: Travelers choose different accommodation types for difrerent trip durations." + ] + }, + { + "cell_type": "markdown", + "id": "1b1d7abe-b085-4103-b0b4-0557690c7c91", + "metadata": {}, + "source": [ + "To do that we need to use series:\n", + "- duration_days\n", + "- accommodation_type\n", + "\n", + "while controlling for destination.\n", + "\n", + "Interpretation: longer stays might be associated with specific lodging patterns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "a5ec6cc2-5056-45e0-b616-1bf553e420da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accommodation_type Airbnb Hostel Hotel Resort Vacation rental Villa\n", + "duration_days \n", + "5.0 1 1 7 1 0 0\n", + "6.0 3 1 10 0 1 1\n", + "7.0 13 5 25 7 1 3\n", + "8.0 4 7 10 3 0 0\n", + "9.0 4 5 4 1 2 0\n", + "10.0 4 3 2 1 0 0\n", + "11.0 1 2 0 1 0 1\n", + "13.0 0 0 1 0 0 0\n", + "14.0 0 0 1 0 0 0\n" + ] + } + ], + "source": [ + "result = travel_trip_complete_christos_df.groupby(\"duration_days\")[\"accommodation_type\"].value_counts().unstack(fill_value=0)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "208209a8-3dd9-4dbc-ad3b-a44b44be1fd7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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qKLHx8PBQgwYNtHr1avXp0yfKSKeVK1eqYsWKypIli65du6axY8fq119/1f3795U+fXo1btxY77//vpycnt209eeff2rcuHH6448/lCJFCtWsWVMDBw6Up6enHjx4oHHjxmn79u26deuWvL29VbNmTfXr2UrurlJAuSaSpEFfTNb+g3/qf51bqW7Tbpr5zXCVLllYZrNZ8xat0aLlG3T12k1lyZxBHdo0UvPGtSVJ+3//U+/2HKRvvv5MX0/5SRcvXVUO3yz6oGd7Va1UOsZzn/r9Au3Zd1iZM6fXzt2/q0Hdqvp0QDf9ceSEJkydo7+On1Fa79SqWqmU3u/RXqlSekqS3mjcVW81fUNHj53Wr3v/kLubqxrUraa+73XQ9Ru3VbdpN0nSuz0Hqfu7LdWjSytt37lfs+as0Kkz5xUeHi5/f3/17dtXFSpUiLe+tRa34AEAAAAAAJtr3ry5Ll++rN9//92y7Pbt29q5c6datGghSerWrZvu3LmjmTNnav369ercubOmT5+urVu3SpKCgoLUvn17pUuXTgsXLtSUKVO0d+9eDR48WJL00Ucf6ciRI5o0aZI2bNigjz/+WMuWLdOS5eskSVvXzJIkfdinkz7q+260GsdO+lHf/rBY3d9tqaVzJ6jtW/U1ctz3mrdoTZR2X0/5SQP7dtaCH8fKN1tmffz5BAUHh7zw3P84ekI+6by1eM7XateyoU6dPq8uvT5XhTLFtGTOeH01tI+Onfhb3XoPjTIP1tQZC1SqeCEt+mmc/te5lX5euFrrNu1U5kw+mjdr9LNaRn6ojm0b6diJs/rgo69U8/UKWrVqlRYvXiwfHx/1799foaGhce6v+EYABQAAAAAAbK5w4cLKnz+/AgMDLcsCAwOVOnVqVa9eXU+ePFGjRo00fPhwFShQQNmzZ1f79u2VMWNGnTx5UpK0aNEipUmTRl999ZX8/PxUokQJffHFF8qdO7ckqWLFiho5cqSKFi0qX19fNWjQQIUKFdKpM+ckyXK7nVfKlPJKlTJKfY8eB2vh0vXq2aW16tepopw5sqpV83pq3byevv9xSZRgqFe3tipbKkD58uTUe91a69HjYJ0++/JbIXt0aSXfbJmVM0dW/fjzCpUtVURdO7VQzhxZVaJYQY0a3ldH/zqlAwf/smxTsVxxtW3ZQDlzZFXrFvXkny+XDh0+LmdnZ6X1TiNJSpPaS56eKeTk5KSBfd9Vh3bNlD17duXPn19vv/22bt++rdu3b1vbbfGGW/AAAAAAAEC8aN68uaZMmaLPPvtMrq6uWrFihRo3bixXV1e5urqqXbt2Wr9+vX766SdduHBBJ06c0I0bNxQRESFJOnnypAoVKiRXV1fLPkuXLq3SpZ/d/tamTRtt3bpVK1eu1MWLF3Xq1CldunRJ2bPW+M/azp0PUnh4uIoXLRBlecniBTV7/qook33nzvXPpO6pUj4LssLCwl+473Rp00QJvI6f/FsXLl1V2ddbR2v79/kglS5ZWJL0Wq6ok8enSun5wuPk98utNKlTadbsxbp45b7Onz+v48ePS5LMcZw0PiEQQAEAAAAAgHjRsGFDjR49Wjt27FD27Nl1/PhxjRs3TpIUEhKitm3bKiQkRHXr1lWjRo00aNAgtW3b1rK9i4vLC5+UZxiGunfvrpMnT6phw4aqU6eO+vbtq0GDYjdheuQAp3/v3myO+P9jO1uWuT0XgP17+5h4uLtFeR0REaH6daqoS8fm0dqmTZva8nfXGI8T84EOHPpL3d8fqsoVSqtM+aqqX7++QkJC1LNnzxcXZkcEUAAAAAAAIF54e3urVq1aWr9+vTJnzqwSJUooT548kqSdO3fqr7/+0u7du5U+fXpJ0r1793T79m1L6JI3b14FBgbKbDbL2flZILRp0yYNHz5cEyZM0C+//KJFixapaNGikqSwsDBdvHhR2TJ7/2dtuXNlk4uzsw7+cVz++XJblh88fFzpfdIqdepUNnsf8ubJobPnLipH9iyWZecuXNa4ST/q/R7tot0eGJN/B2U//bxSpUsU1oQxg+SW+jVJ0pw5cyS9OLSyJ+aAAgAAAAAA8aZ58+bavn271q9fr+bN/xkBlDlzZknSqlWrdPnyZR04cEA9evRQWFiYZRLtNm3a6O7du/r888919uxZHThwQGPHjlXFihWVLVs2ubi4aN26dbp06ZKOHj2qDz74QDdv3lRoaJjlOJ6eHvr7fJDu3X8QpS6vVCnVrHEtffP9fK3ZsEMXL13VgiVrtXDpOnVo0+iFI6+s8XabRjpx8pyGj5qms+cu6cifpzRw8Ne6cPGycmbPGqt9eHp6SJJOn72gh48eK3MmH50+e0EH//hTQUFBWrp0qSZOnChJiXISckZAAQAAxEG+DG7/3SgZcbTzBYDELlXunEnumOXLl5eXl5du376tunXrWpYHBATo448/1o8//qgJEyYoU6ZMqlevnrJkyaLDhw9LkjJlyqRZs2Zp7NixatKkiVKnTq169eqpb9++8vDw0FdffaXJkyfr559/VoYMGVStWjV17NhRmzetl2EYMplMert1I/04d7nOXQjSR306R6ntoz6d5e2dWhOnztHtO/eUwzeLPu7XRc0b136lc/63ooX9NW3iYH3z7Xy16thfKTzcVaZUEfXr1VFubtFvu4uJd5rUatKwhr6e8pMuXrqqnl1b69bte3qvzxDJ5KS8efPqyy+/1IABA3TkyBHLSLPEwmQkxnFZdnL06FFJUpEiRexWQ9CWdgq9d9Jux09Ibt7+8q0x195l2A197ThqfHteR649tXcZCSIgs7u2dMtl7zLswpE+05Ljfq7NEYacnWz329CkwlHPm8+14yg1fIAOXTxn7zISTPEcuXVg0Bh7l2EXieFzbXbNpCfZ+iqnb0a5u8XhpiSTi1y9XpOTs33GkRhms0zOzv/dMJEIffC3DPMTe5eRIEzOHpZb8GztyZMnOnfunHLnzi0PD49o6+OSoySqEVARERGaMmWKFi9erAcPHqhkyZL6/PPPlTNnzGnrzZs3NXLkSO3evVuSVK5cOX388ceWYXwAAAC25Oxk0p0/pyos+Iq9S0kwrp5Zla5wD3uXAQAwwhX28G+ZnGL3Nd7k5CbXVL7/3TCWklL4hMQpUQVQU6dO1YIFCzRy5EhlypRJY8aMUZcuXbR69Wq5uUUf/t2nTx+ZzWb98MMPkqShQ4eqR48eWrZsWUKXDgAAHETw9V/t/tvzhOTm7U8ABQCJhREuwxxu7yoAqySaSchDQ0M1a9Ys9erVS1WrVlX+/Pk1fvx4Xb9+XZs2bYrW/sGDB9q/f7+6dOmiggULqmDBguratav++usv3b171w5nAAAAAAAAgJgkmgDqxIkTevz4scqVK2dZljp1ahUsWFD79++P1t7d3V2enp5asWKFHj16pEePHmnlypXKlSuX0qRJk5ClAwAAAAAA4CUSzS14165dkyRlyZIlyvKMGTPq6tWr0dq7u7trxIgRGjZsmEqVKiWTyaQMGTJo7ty5cnJKNLkaAAAAAACAw7M6gLp9+7b+/PNPVa1aVZK0efNmTZ8+XS4uLnr33XdVq1atOO0vJCREkqLN9eTu7q779+9Ha28Yhk6ePKnixYurc+fOMpvNGj9+vHr27Kn58+crVapUVp2XYRgKDg62attXYTKZlCJFigQ/bmIQEhIiR3oYI31NXzsCR+prR+5nib52JPS143DEvi6QxXYTNScFkefriH3tiCIiIhymn6Vnfe2og1Lio6/NZrMiIiIUEhKiiIiIaOsNw5DJFLsn5VoVQJ05c0Zt2rRRxowZVbVqVV24cEEffPCBJMnV1VXvv/++ZsyYoQoVKsR6n5GP8wsNDY3yaL+nT5/G+INizZo1mjdvnrZt22YJm6ZPn67XX39dS5cuVYcOHaw5NYWFhen48eNWbfsqUqRIoYIFCyb4cRODc+fOWQJIR0Bf09eOwJH62pH7WaKvHQl97Tgcqa89PT3l5++vOZ3ft3cpCc4cEaHz58/b5Zfv9uDIn+unT5/GGBwkV05OTg4bNsZHXz99+lTh4eH6+++/X9gmpofGxcSqAOqbb75RRESEevfuLUlasWKFzGazfvrpJxUqVEht27bVzJkz4xRARd56d+PGDeXIkcOy/MaNG8qfP3+09r///rty584dZaRTmjRplDt3bp0/f96a05L0LEDLmzev1dtbK7aJYXKUO3duh0vkHZWj9nW+DLH7gZwcRJ6rI/W1I3+mJfrakdDXjsPR+trZyUkjtt7Upbth9i4nwWRP66pPq2dQrly5HKqvHZW7u7vD9LNEX8dHX7u4uChHjhxyd3ePtu7MmTOx3481B9+/f7/at2+v2rVrS5J27typLFmyqEyZMpKkxo0ba+rUqXHaZ/78+ZUqVSrt3bvXEkA9ePBAx44dU7t27aK1z5Ili9auXaunT59a3oSQkBAFBQWpYcOG1pyWpGcXq6enp9XbI+4cNZ12RI7Y1+YIQ9ObZrV3GQnKHGE4ZF87KvracdDXjsMR+3rr6cc6cu2pvctIMAGZ3fVp9QwO2deOyFFvR3NE8dHXzs7OllFlz9+tFikugZ9VAdT9+/eVPXt2Sf+ERI0aNbKsT5kypcLC4vYbBDc3N7Vr105jx45VunTplC1bNo0ZM0aZM2dWrVq1ZDabdefOHXl5ecnDw0ONGzfWzJkz9cEHH+j9958NmZ0wYYLc3NzUtGlTa04LAGzO2cmkO39OVVjwFXuXkiBcPbMqXeEe9i4DAAAgeTK5yOQUu6/xJifHGYWPpMGqACpz5sy6cuXZl6ldu3bJMAxVrFjRsv7o0aPKmDFjnPfbu3dvhYeH67PPPtOTJ09UunRpzZw5U25ubgoKClKNGjU0cuRINW3aVBkzZtS8efM0ZswYdejQQU5OTipVqpTmz5+v1KlTW3NaABAvgq//qtB7J+1dRoJw8/YngAIAAIgPJhe5eL0mZ2f7PMzeHBEh5ziMsGnfvr0ePHiglStXxrh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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# df_pivot: counts\n", + "# convert to proportions per trip duration (row-wise)\n", + "df_prop = df_pivot.div(df_pivot.sum(axis=1), axis=0)\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "\n", + "df_prop.plot(kind=\"bar\", stacked=True, figsize=(12, 7))\n", + "\n", + "plt.title(\"Accommodation Type Proportion per Trip Duration\", fontsize=16)\n", + "plt.xlabel(\"Trip Duration (days)\", fontsize=14)\n", + "plt.ylabel(\"Proportion of Accommodation Types\", fontsize=14)\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title=\"Accommodation Type\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "41c13713-0c40-4e7a-a9fb-ec1ed44936d9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S9NZbb8lisTjaBQQEqHv37oqNjY331/i6devKy8vL8bpAgQKSpHr16jl9Yl/cyJcrV644bV+qVCnlyJEj3valS5dWUFDQA7ffsWOHbt++rbZt2ypTpkxO++zRo4fj07Ok+xOF22w2tW/f3qmmQoUKqWrVqk7b1qpVS7t27VLr1q2dll+/fl2BgYEKDw9XUty4cUO7du1SpUqV4n1CXeXKlVWpUiXt3r1bf//9t9O6uHm/4sSNkvnnIy+PIigoSJUqVdJPP/2kW7duOZYvW7ZMefPmjTeaq127dsqQIYPjdbZs2dSwYUNduHBBhw8flmEYWrNmjZ599lnlyZNHN27ccHxlypRJJUuW1I4dOxyPMP6X/fv3O65XpUqV9Nxzz6lRo0aORygnTZoUb5saNWo43bNJuT/iPPvss07X3WKxqGvXrpLk6HPJ6SP/ri2x1q5dK0nq1auXUx/z8vJyjGb69znUqFFD6dKlc7z29/dX/vz5491fD5KYa7B69WpJ90co/fN9vnPnjl566SXFxMRo8+bN8epKzDVYuXKloqOjnX5++Pv7q2bNmoqJiXH8bJTuz5O3fft2ValSxWkUmdVq1RtvvOG032PHjun48eOqXr267Ha7U901atSQj4+PNmzY8ND68ufPryVLluj7779Xjx49VKZMGXl5eemvv/7SV199pZdfflkXLlyQdP9x1G3btqls2bIKCAhwOmZoaKhy5crlOObOnTt18+ZNtWnTxmmEY2BgoNq0aZNgLcn92Zmca5E3b16VKFHCaVncNb9+/fpDrxsAIOXxCB4A4JHFxMRoxYoVkqQSJUo4fpmRpIoVK+rAgQOaN2+eXn75ZV28eFGGYST4se2ZM2dW5syZJcmxj4Ta/fPRr3PnzikgIEDZsmWL1y44ONhpX3GyZMni9NrT0zPB5XG/TP77sZi4GhO7vWEYjlolqWDBgvFq9fHxUe7cuXX69Gmntgk9ElKwYMF489Z4eXlp4cKF2rt3r86fP69z587p1q1bslgsjuMn1oULF2QYhuOXw4SOv3PnTl24cMEpKPn3+Xt7e0uKf/0eRdOmTbVjxw6tXbtWLVu21O+//67jx4/r3Xffjdc27v3/p2effVbS/esbFBSkW7du6datW/GCtn+6fPlygvfhP/17/hir1aq0adPq2WefdXpk7p/+fb2Scn/ESajtP88x7r+P2kcS67/O4UHH+nd/ku7fOwnN4ZSQxFyDuOv2oGBEkv766y+n14m9BnEBU+nSpZ3OrWzZslq9erUWLFigLl26yMPDQ+fOnVNsbKyjvn/6d3+Lq3n+/PmOeZIeVvN/KVGihCOQiYiI0NatWzVp0iQdO3ZMH3/8sSZPnqwzZ87Ibrdry5Yt/9knIiMjHfUl5lziJPdnZ3KuxYPuK0mJvrcAACmLAAoA8Mi2bNniGK3woF/wfv31V504ccIxsfDDRhYktp1hGA9sE/dLRtwvHXHifun5t8SO+PjnyI6kbB/3y9SD2tnt9ni1PqjdP125ckWtWrXSlStXVK5cOcdk7SVLltSQIUOS/OlrialTin9d/z2XjBnq1Kmj9OnTa/ny5WrZsqWWLVsmDw8PNWzYMF7bhK5l3D3h6enp+L5MmTKOuYoSklBw82/p06fXc889l9jTkCSnkW1S8u6PhM4xru/885f4pPaRf9eWWP8Vdj7oWI963yTmGsQde/LkyfLz80twP/8cgfPPbf/LkSNHdOTIEUlS3759E2zz119/adu2bapevbpiYmIeWPO/g8q4fta6dWvVrl07wX0/6GdZnIkTJ8rLyyveJON+fn6qW7euKleu7BhB+c9j1q1bVy1atHjgfj09Pf/zXB70cyy5PzuTcy1S4+cRACBpCKAAAI8sbgRAt27d4k20LN2fTHzLli2aP3++unfvLknxRnJI9ycnnzZtmpo3b66cOXM62lWpUsWp3YoVK7Rz50699dZbyp07t06dOqXLly/HCwpOnDghScqePfujn2QKiBvN9Oeff8b7JSoyMlLnz59Xrly5JP3fIygnTpyI96ji2bNnnV6PHz9eFy5c0IwZM+I9npecx9/iajh+/HiC648fPy6LxaJnnnkmyft+VN7e3nr55Zc1d+5cXb58WevXr1flypUTrOXMmTPxQqFTp05Juv94TsaMGeXv76/bt28nGB7t2LFDHh4e/znJc0pKyv0R59/3guR8jnH7Ta0+EncOx48fV9myZU09VpzEXIO4nydZs2aNN4H62bNnderUKfn7+yf52HE/+5o1a6YaNWrEW79x40YtWbJE8+bNU/Xq1ZUnTx5ZLBZHfQnVHCeuZknx7k+73a7169fHux/+beXKlY6AOqEPAggICFCOHDl09epVp2NGRUUl2Cd+/PFHBQYGytPT03FtT506Fe/nTkI/3x9FSlwLAIDr8acBAMAjuXbtmrZt26bAwED17NlTtWvXjvcVN/fLsmXLlCZNGpUsWVJbt26N90vKnDlztHr1aqVNm1bPPfec/Pz89P333ysiIsLRJjo6WtOmTdOmTZuUKVMm1a1bV5I0duxYp9EX4eHhmjZtmqxW6wP/Yp7a4j6iffbs2fHmt/nqq68UERHhOJ86derIy8tLX3/9taKjox3tTp06FW+umriPRg8JCXFavn79escv5//8SHsPD4//fCwuY8aMKl++vHbu3KmdO3c6rdu5c6d27dql8uXLOz4JK7U1a9ZMhmFozJgxunr1qpo0aZJguzlz5igqKsrx+sKFC1qxYoWCg4NVsGBBx71x/PhxxxxBcY4ePapu3bpp5MiRDx1lklKScn/EOXjwoONj5KX7v4x/9dVXslgsjk9kS6k+Ejei5L/unbhjTZw40emei42N1cSJE53apJTEXIN69epJkiZMmOD0+FVMTIwGDhyo7t27x5vr7WGio6O1cuVKeXp6qk+fPgn+7HvnnXfk5eWlrVu36tKlS8qQIYMqVaqkbdu26eTJk459GYahmTNnOu2/aNGiypEjh5YuXep4lDDOggUL9NZbbznNL5WQpk2bKiIiQkOHDnX6ORJn7969OnLkiOP6ZM6cWWXKlNHWrVu1d+9ep7Zbt25Vz549NW3aNEn379d06dLp+++/d5pn7t69ew98TC65UuJaPEhi7msAQMpgBBQA4JEsW7ZMsbGxatKkyQNHihQpUkTlypXTnj17tGrVKg0ePFht27bVq6++qtatWysoKEi//PKLY16fwoULS5IGDBigIUOGqEmTJmrcuLH8/Py0fPlyHT9+XGPGjJGnp6caNmyodevWaenSpbp48aJq1aqliIgILV26VGfOnFHfvn0fm7+Mp02bVkOGDNF7772nV155Rc2bN1emTJm0c+dObdiwQUWKFFHnzp0l3R8l8vbbb+vzzz/Xa6+9pkaNGunu3buaPXu20qVL5xRQ1KpVSxs3blTnzp316quvysvLS3v27NGaNWvk6+uryMhI3b592xEYZcqUSUePHtXcuXNVtmzZBOdKGjJkiFq1aqUuXbqoefPmKlCggE6cOKEFCxYoMDBQQ4YMSZ2LloDChQurUKFCWrZsmdKnT//A8OTChQt69dVX1aRJE929e1ffffedLBaLhg0b5mjTt29f7dmzR3379tX27dtVokQJXbp0SfPnz5fVak3V80zK/RHHx8dHXbt2VZs2bZQtWzatX79eu3fvVocOHRwjfVKqj8TNqTNv3jxdvXo1wcceK1SooObNm2vBggV67bXXHB9KsHr1av3xxx9q1aqVypUr96iXyklirkGlSpXUrFkzLVq0SK+99ppeeukleXt7a8WKFTp48KBatWqV4OjN//Ljjz/q1q1bqlu37gNHA2bJkkX169fXsmXLHEHJwIED1aJFCzVv3lytW7dWlixZtHHjRu3bt0/S/z2OZrVaNWLECHXr1k1NmjRRixYtlDt3bh06dEiLFy9W7ty51aNHj/+ssWPHjvr999+1atUq7d+/Xy+99JLy5Mmj6Oho7d+/X+vWrVNoaKjeeustxzZDhgxRmzZt1KFDBzVv3lwFCxbUqVOnNH/+fAUGBuq9996TJKVJk0bvv/++BgwYoCZNmjiC4UWLFuny5ctO5/KoUuJaPEjcfT1+/HiVL18+yY/SAgASjwAKAPBIli5dKovFolatWv1nuw4dOmjPnj2aP3++Fi9erEWLFmn8+PGaP3++IiMjlSdPHg0ZMkTNmzd3bNOiRQsFBQVpxowZmjJliqxWqwoVKqSZM2eqcuXKku7/YjJ58mR98803WrZsmUaPHi0/Pz8VK1ZMH3zwgapVq2bq+SfVK6+8oqCgIE2bNk3ffvutoqOjlTt3br311lvq2LGjU4jXqVMnBQUF6euvv9aYMWMUGBio9u3bKyoqSlOnTnW0a9q0qSIjI/Xdd99p1KhRSpMmjXLnzq1hw4bJbrfrww8/1LZt2xyBQf/+/TV69Gh9/PHH6t69e4IBVIECBbRkyRJNmjRJ69ev14IFC5QlSxY1a9ZMb7zxhksev/unpk2basSIEapfv/4D55t57733dPjwYU2cOFEWi0Xly5dXnz59nEaKPfPMM1q8eLGmTJmiTZs2aeXKlcqQIYPKly+vN954wxGGppak3B/S/YmlmzZtqkmTJunKlSvKly+fRo4cqWbNmjnapFQfqV+/vjZs2KAtW7Zo586dqlOnToLthg0bpuLFi2v+/PkaP368rFarQkNDNXr0aDVo0CD5F+cBEnMNJGnEiBEqWbKkFixYoAkTJshqtSpv3rwaMWJEvLaJsWTJEklK1M++ZcuWadGiRerVq5eCg4M1d+5cffnll5o9e7YMw1CFChU0ZswYvfHGG07zJD333HP6/vvvNWXKFC1evFh37txRtmzZ1KpVK3Xr1u2hE6V7enpq/PjxWrdunVasWKEVK1bo5s2b8vb2Vr58+fTOO++oTZs2Tn0oJCRES5Ys0eTJk7Vu3TrNnz9fWbJkUb169dSjRw/H48GS1LhxY/n7+2vatGkaP368/P399eKLLypHjhwaNWpUoua0S6xHvRYP0qVLF/3555+aMWOGfvvtNwIoADCRxUjqR+MAAAC42Jw5czR8+HAtXrxYRYsWdVq3ZMkSDRw4UJ988skDH897EoSEhKh8+fKaPXu2q0txGXe8BteuXVPmzJnjjQ7at2+fWrZsqV69eql3794uqi7xoqOjde/ePWXIkCHeuqlTp2rMmDH69ttvVaFCBRdUBwB4HDEHFAAAcCvR0dGaP3++ihUrFi98Ah537dq1U7169ZzmopLuf7iCJJUsWdIFVSVdWFiYKlasqPfff99peXR0tNauXStvb+9UH0EIAHi88QgeAABwC/v27dOcOXN05MgRnTp1Sl999ZWrSwKSrGnTpho1apQ6dOigevXqycPDQ7t27dLatWtVo0aNeJ/6+bjKkiWLqlevriVLlsgwDJUqVUrh4eFavXq1jh49qv79+yf4yXsAgKcXARQAAHALadKk0c8//ywPDw998MEHev75511dEpBknTt3VpYsWTRv3jyNGzdO0dHRypUrl/r166cOHTqk2MTdqWHs2LGaNWuWVq9erbVr18rLy0uhoaGaMGGCXnjhBVeXBwB4zDAHFAAAAAAAAEzFHFAAAAAAAAAwFQEUAAAAAAAATPXUzwG1f/9+GYYhLy8vV5cCAAAAAADgNmJiYmSxWFSqVKmHtn3qR0AZhiGmwXJPhmEoOjqa9w9IRfQ7IPXR74DURZ8DUh/9zn0lJVN56kdAxY18KlasmIsrQVKFh4fryJEjKlCggPz9/V1dDvBUoN8BqY9+B6Qu+hyQ+uh37uvQoUOJbvvUj4ACAAAAAACAuQigAAAAAAAAYCoCKAAAAAAAAJiKAAoAAAAAAACmIoACAAAAAACAqZ76T8EDAAAAAMAVbDabYmJiXF2Gy0VFRTn+6+HBOJnHhZeXl6xWa4rtjwAKAAAAAIBUZBiGLl++rFu3brm6lMeC3W6Xp6enLl68SAD1mAkMDFS2bNlksVgeeV8EUAAAAAAApKK48Clr1qzy9/dPkV/u3ZnNZlNUVJR8fHxSdMQNks8wDIWHh+vq1auSpKCgoEfeJwEUAAAAAACpxGazOcKnTJkyubqcx4LNZpMk+fr6EkA9Rvz8/CRJV69eVdasWR/5vWFsGwAAAAAAqSRuzid/f38XVwI8XNx9mhJzlRFAAQAAAACQyp72x+7gHlLyPiWAAgAAAAAATzXDMFxdwhOPAAoAAAAAgMdU//79FRISomnTprm6lMferl27FBISol27diVpuylTpujrr792vJ4wYYJCQkJSurx4BgwYoJCQkP/8qlmzpul1pBYmIQcAAAAA4DF09+5d/fDDDwoODtb333+vLl268OieCcaOHatevXo5Xr/66quqWrWq6cft0aOHWrRo4Xg9efJkHT58WBMnTnQs8/b2Nr2O1EIABQAAAADAY2j16tWy2WwaNGiQ2rVrp+3bt6dKMPK0y5Ytm7Jly2b6cXLnzq3cuXM7XmfMmFHe3t4qWbKk6cd2BR7BAwAAAADgMbR48WJVqFBBFSpUUL58+TR//vx4bVavXq0mTZqoRIkSev755zVq1ChFR0c71v/+++/q3LmzypQpo4oVK+rtt9/WpUuXHOuvXr2qgQMHqnr16ipevLiaNWumjRs3Oh0jJCRE8+bN04ABA1SmTBmVL19eI0aMUGRkpD777DNVrFhRFSpU0AcffKCoqKgkbzd48GCn7aKiojRp0iTVq1dPxYoV0wsvvKBp06bJbrc71TV//nzVrVtXxYsXV5s2bXTx4sV412fPnj3q1KmTypUrp6JFi6pmzZqaMGGCY19xj9pNnDjR8X1Cj+CtWbNGTZo0UalSpVS5cmV9+OGHCgsLc6yfMGGC6tSpoy1btqhBgwYqWrSo6tatq6VLlz7g3U2c48ePKyQkRAsWLHBafuXKFRUqVEhLly7VhQsXFBISotWrV6t79+4qUaKEqlev7nSecRYuXKj69euraNGiev755zVhwgTFxsY+Uo2J9VgFUJMnT1bbtm3/s83Nmzf17rvvqly5cipXrpwGDx6s8PDwVKoQAAAAAADznTx5Ur/99psaN24sSWrSpIk2b96sK1euONrMnz9f77zzjgoVKqSJEyeqW7dumjt3roYOHSpJOnr0qFq2bKmIiAh9+umnGjZsmA4fPqyOHTsqJiZG169fV7NmzbR79269/fbbmjBhgnLkyKGePXtqxYoVTvWMHj1a3t7emjhxoho2bKjZs2erUaNGunTpkkaNGqUWLVpo0aJFmj17dpK3W7x4sSNcMwxD3bt314wZM9SsWTNNnTpV9erV09ixYzVkyBDHfufMmaMhQ4aoatWqmjx5skqUKKHBgwc7Hfvo0aPq0KGDAgMDNWbMGE2ZMkWlS5fWxIkTtXr1aklyBDvNmjWLF/LEmTx5st5++22VKFFC48ePV8+ePbV+/Xq1bdtWkZGRjnbXrl3TsGHD1K5dO02bNk05c+bUgAEDdPLkyUS/7/9WsGBBlShRQsuXL3davnz5cvn6+qpu3bqOZUOHDlVAQIAmTJigRo0aafLkyfr8888d67/66isNHjxYlSpV0tSpU9W6dWtNnz5dH374YbLrS4rH5hG8WbNmafz48SpXrtx/tuvTp4+ioqI0a9Ys3b59Wx988IE++ugjffbZZ6lUKQAAAAAA5lq0aJHSpUun2rVrS5IaNWqksWPHauHCherVq5fsdrtj1M3IkSMd20VFRWnp0qWKjo7W5MmTlT59es2cOVM+Pj6S7j9e9tZbb+nYsWNau3atbty4obVr1ypXrlySpOrVq6tDhw76/PPP9fLLL8vD4/64lfz582vYsGGSpHLlymnRokWKiYnR6NGj5enpqapVq2rTpk3at2+f03kkZruNGzfqt99+kyRt3bpVP//8s0aNGqVXXnlFklS5cmX5+vpq3Lhxat++vfLnz6/Jkyerbt26GjRokCSpSpUqunv3rtMosaNHj+q5557TqFGjHOdRuXJlbdmyRXv27FGDBg0cj7tly5YtwUffwsLCNGXKFL366qtOAVhwcLBat26tJUuWqFWrVpKkiIgIjRw5UpUqVZIk5c2bVzVq1NBPP/2k/PnzJ/7N/5emTZvqww8/1Pnz5x3v07Jly/Tiiy/K399fN27ckCQVLlxYo0ePliRVq1ZN4eHhmjNnjnr06CGLxaIpU6aoefPmTtcsMDBQgwYN0uuvv66CBQsmu8bEcPkIqCtXrqhz584aN26c8uXL959t9+/fr927d+uTTz5RkSJFVKlSJQ0bNkzLly93SoEBAAAAAHBXsbGxWrFihWrXrq2oqCjdvn1bvr6+qlChghYuXCibzabTp0/r+vXrjoAqTocOHbR8+XJ5e3tr7969qlatmiN8kqTixYtr06ZNKlq0qHbv3q1SpUo5Qo04r7zyiq5du6ZTp045lpUqVcrxvaenpzJkyKCiRYvK0/P/xrUEBgbqzp07TvtK6na7d++W1WrVSy+9FK8m6f4n3Z06dUp///23atWq5dTmxRdfdHrdqFEjTZ8+XTExMTp+/Lh+/PFHTZgwQTabTTExMf++7Ak6cOCAoqOj1aBBA6flZcuWVY4cOeJ94t4/Q6y4eaQe9amt+vXry8/PzzEK6uDBgzp58qSaNGni1C7uGsWpW7euYmJidODAAe3fv18RERGqWbOmYmNjHV9xn7K3Y8eOR6oxMVw+AuqPP/5Q+vTptWLFCk2aNEl//fXXA9v++uuvypIli1NyWL58eVksFu3duzfeDQoAAAAAgLvZsmWLrl+/riVLlmjJkiXx1m/evFkZMmSQJGXKlOmB+7l169Z/rg8LC1POnDnjLc+cObMk6fbt245lAQEB8dr5+fk9+CSSuV1YWJgyZMjgFFBJUpYsWSRJd+7cccy9lDFjxgTbxImMjNTw4cO1fPlyxcbGKmfOnCpVqpQ8PT1lGMZDa4+rR/q/a/JPmTNnjhe4/fPc4kZdJfZYDxIQEKB69eppxYoV6tWrl5YuXao8efKobNmyTu2yZs3q9Dru+ty+fdsxF1TXrl0TPMbVq1cfqcbEcHkAVbNmTUfi9jBXrlxRUFCQ0zJvb28FBgY6TaIGAAAAAIC7WrRokXLkyKFPPvkk3ro+ffpo/vz5eu+99yTJ8fhVnFu3bumPP/5QyZIllTZt2njrJemnn35SaGio0qdPr+vXr8dbf+3aNUlyhFypKX369Lp586ZiY2OdQqi4gCRDhgyOuv7++2+nbW/duuX0euTIkVq/fr3Gjh2r5557Tv7+/pLkeEQusfVI0vXr1+M9Rnft2rV4o8fM0rRpUy1dulQHDx50zD/1b/8+/7jrkylTJsck76NHj1bevHnjbZtQwJbSXB5AJUVERIS8vb3jLffx8XGaMT+pDMN4oicyt1gsri7BFNHR0fLz81N0dPQTe46PmpTDNZ7U+1Gi3+Hx9aTejxL9Do+vJ/V+fBr6nES/c6WoqCjZ7XbZbDbZbLZ4669fv65t27apQ4cO8Ua4WCwWvfjii1qwYIG8vLyUIUMG/fjjj06Ph61YsUKffvqptm3bpjJlymjbtm2KjIx0/C79559/qmvXrpowYYLKli2r2bNn69y5c04joVasWKHMmTMrV65cjpEzhmE4faKaYRgJLpMUb9nDtvvnurJly2rGjBlavXq103nFPX5WqlQp5c6dW0FBQVq7dq3TY2ebNm1yOubevXtVvnx5x6AXu92uP/74Qzdu3JDNZnPU4OHh4VTTP8+jWLFi8vb21ooVK5zmrN67d68uXryoTp06yW63J3ju/74GD+t3cdcmofuidOnSypMnj0aNGqWbN2+qQYMGjnZxx9y4caPTk2Fr166Vn5+fihYtqujoaHl5eenSpUtObY4dO6bPPvtMPXr0iDeCSpLjOkVERDzw3BL7s9KtAihfX1+nj5OMExUV5UgykyMmJkZHjhx5lNIeW15eXipSuLCsnm71VieKn5+fAgMDXV2GaWyxsfrj8OFEP5u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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# counts\n", + "ct = pd.crosstab(\n", + " travel_trip_complete_christos_df[\"traveler_age_segment\"],\n", + " travel_trip_complete_christos_df[\"accommodation_type\"]\n", + ")\n", + "\n", + "# row-wise proportions\n", + "ct_prop = ct.div(ct.sum(axis=1), axis=0)\n", + "\n", + "ct_prop.plot(kind=\"bar\", stacked=True, figsize=(12,6))\n", + "\n", + "plt.title(\"Accommodation Type Proportion per Age Segment\", fontsize=14)\n", + "plt.xlabel(\"Age segment\", fontsize=12)\n", + "plt.ylabel(\"Proportion\", fontsize=12)\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title=\"Accommodation Type\")\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e1baa447-7fbe-4f57-a5ae-d610e7862ee3", + "metadata": {}, + "source": [ + "### Hypothesis #4: Transportation type preferences differ by traveler age." + ] + }, + { + "cell_type": "markdown", + "id": "0c4d068d-d871-4ff6-b379-c9175b520431", + "metadata": {}, + "source": [ + "To do that, we need to use series:\n", + "- transportation_type\n", + "- traveler_age\n", + "\n", + "Interpretation: age-targeted recommendations may be feasible.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "d192bd7f-ef62-4513-877c-e2467db8a8ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " traveler_age_segment transportation_type\n", + "0 34-40 Young Parents Flight\n", + "1 26-33 Young Professionals Flight\n", + "2 41-50 Money & Energy Group Flight\n", + "3 26-33 Young Professionals Flight\n", + "4 18-25 Students-Early Professionals Train\n", + ".. ... ...\n", + "132 34-40 Young Parents Private Car\n", + "133 26-33 Young Professionals Airplane\n", + "134 26-33 Young Professionals Train\n", + "135 18-25 Students-Early Professionals Airplane\n", + "136 34-40 Young Parents Train\n", + "\n", + "[137 rows x 2 columns]\n" + ] + } + ], + "source": [ + "travel_trip_complete_christos_df['traveler_age_segment'] = pd.cut(travel_trip_complete_christos_df['traveler_age'], bins=age_bins, labels=age_labels, right=True)\n", + "\n", + "output = travel_trip_complete_christos_df[['traveler_age_segment', 'transportation_type']]\n", + "\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "6482f855-117c-410b-abf1-38ce7da0788b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_typeAirplaneBusFerryFlightPrivate CarRental CarSubwayTrain
traveler_age_segment
18-25 Students-Early Professionals92020204
26-33 Young Professionals2931619118
34-40 Young Parents1310210011
41-50 Money & Energy Group100031204
51-60 Rich but Tired10000001
\n", + "
" + ], + "text/plain": [ + "transportation_type Airplane Bus Ferry Flight Private Car \\\n", + "traveler_age_segment \n", + "18-25 Students-Early Professionals 9 2 0 2 0 \n", + "26-33 Young Professionals 29 3 1 6 1 \n", + "34-40 Young Parents 13 1 0 2 1 \n", + "41-50 Money & Energy Group 10 0 0 3 1 \n", + "51-60 Rich but Tired 1 0 0 0 0 \n", + "\n", + "transportation_type Rental Car Subway Train \n", + "traveler_age_segment \n", + "18-25 Students-Early Professionals 2 0 4 \n", + "26-33 Young Professionals 9 1 18 \n", + "34-40 Young Parents 0 0 11 \n", + "41-50 Money & Energy Group 2 0 4 \n", + "51-60 Rich but Tired 0 0 1 " + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transport_count = pd.crosstab(travel_trip_complete_christos_df['traveler_age_segment'], travel_trip_complete_christos_df['transportation_type'])\n", + "transport_count" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "3baf308e-e3c7-43bc-9c8c-0d3dc7bdc642", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "data = {\n", + " 'Airplane': [9, 29, 13, 10, 1],\n", + " 'Train': [4, 18, 11, 4, 1],\n", + " 'Bus': [2, 3, 1, 0, 0],\n", + " 'Flight': [2, 6, 2, 3, 0],\n", + " 'Ferry': [0, 1, 0, 0, 0],\n", + " 'Private Car': [0, 1, 1, 1, 0],\n", + " 'Rental Car': [2, 9, 0, 2, 0],\n", + "}\n", + "\n", + "index = [\n", + " \"18-25 Students-Early Professionals\",\n", + " \"26-33 Young Professionals\",\n", + " \"34-40 Young Parents\",\n", + " \"41-50 Money & Energy Group\",\n", + " \"51-60 Rich but Tired\"\n", + "]\n", + "\n", + "# merge Flight into Airplane and drop Flight\n", + "data[\"Airplane\"] = [a + f for a, f in zip(data[\"Airplane\"], data[\"Flight\"])]\n", + "del data[\"Flight\"]\n", + "\n", + "transport_df = pd.DataFrame(data, index=index)\n", + "\n", + "brand_palette = [\"#b22d3e\", \"#026453\", \"#ddac34\", \"#1480d8\"]\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "ax = transport_df.plot(\n", + " kind='bar',\n", + " stacked=True,\n", + " figsize=(12, 7),\n", + " color=brand_palette # cycles through these 4 for all bars\n", + ")\n", + "\n", + "# wrap x-axis labels, horizontal\n", + "wrapped_labels = [\n", + " \"\\n\".join(textwrap.wrap(str(lbl), width=18))\n", + " for lbl in transport_df.index\n", + "]\n", + "ax.set_xticklabels(wrapped_labels, rotation=0, ha='center')\n", + "\n", + "plt.title(\"Count of Transportation Types per Traveler Age Segment\", fontsize=16)\n", + "plt.xlabel(\"Traveler Age Segment\", fontsize=12)\n", + "plt.ylabel(\"Count of Trips\", fontsize=12)\n", + "plt.legend(title='Transportation Type', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "id": "4900bce8-5f09-4049-8aa0-95d79d520664", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# but we rather use proportions\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import textwrap\n", + "\n", + "data = {\n", + " 'Airplane': [9, 29, 13, 10, 1],\n", + " 'Train': [4, 18, 11, 4, 1],\n", + " 'Bus': [2, 3, 1, 0, 0],\n", + " 'Flight': [2, 6, 2, 3, 0],\n", + " 'Ferry': [0, 1, 0, 0, 0],\n", + " 'Private Car': [0, 1, 1, 1, 0],\n", + " 'Rental Car': [2, 9, 0, 2, 0],\n", + "}\n", + "\n", + "index = [\n", + " \"18-25 Students-Early Professionals\",\n", + " \"26-33 Young Professionals\",\n", + " \"34-40 Young Parents\",\n", + " \"41-50 Money & Energy Group\",\n", + " \"51-60 Rich but Tired\"\n", + "]\n", + "\n", + "# merge Flight into Airplane and drop Flight\n", + "data[\"Airplane\"] = [a + f for a, f in zip(data[\"Airplane\"], data[\"Flight\"])]\n", + "del data[\"Flight\"]\n", + "\n", + "transport_df = pd.DataFrame(data, index=index)\n", + "\n", + "# row-wise proportions\n", + "transport_prop = transport_df.div(transport_df.sum(axis=1), axis=0)\n", + "\n", + "brand_palette = [\n", + " \"#b22d3e\", # Airplane\n", + " \"#026453\", # Train\n", + " \"#ddac34\", # Bus (unchanged)\n", + " \"#1480d8\", # Ferry (unchanged)\n", + " \"#800080\", # Private Car (purple)\n", + " \"#5f7f99\", # Rental Car (grey-blue)\n", + "]\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "ax = transport_prop.plot(\n", + " kind='bar',\n", + " stacked=True,\n", + " figsize=(12, 7),\n", + " color=brand_palette\n", + ")\n", + "\n", + "# wrap x-axis labels, horizontal\n", + "wrapped_labels = [\n", + " \"\\n\".join(textwrap.wrap(str(lbl), width=18))\n", + " for lbl in transport_prop.index\n", + "]\n", + "ax.set_xticklabels(wrapped_labels, rotation=0, ha='center')\n", + "\n", + "plt.title(\"Transportation Type Proportion per Traveler Age Segment\", fontsize=16)\n", + "plt.xlabel(\"Traveler Age Segment\", fontsize=12)\n", + "plt.ylabel(\"Proportion of Trips\", fontsize=12)\n", + "plt.legend(title='Transportation Type', bbox_to_anchor=(1.05, 1), loc='upper left')\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "id": "bf258869-887e-41c2-b45d-2f399994bc0b", + "metadata": {}, + "source": [ + "### Hypothesis #5: Accommodation and transportation prices in Europe follow seasonality patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "16538984-9232-4609-8c00-c634a18181ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# total trips per destination continent\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "totals = (\n", + " df.groupby(\"destination_continent\")\n", + " .size()\n", + " .reset_index(name=\"trip_count\")\n", + " .sort_values(\"trip_count\", ascending=False)\n", + ")\n", + "\n", + "plt.figure(figsize=(6,4))\n", + "plt.bar(\n", + " totals[\"destination_continent\"],\n", + " totals[\"trip_count\"],\n", + " color=[\"#b22d3e\", \"#026453\", \"#ddac34\", \"#1480d8\"]\n", + ")\n", + "\n", + "plt.xlabel(\"Destination continent\")\n", + "plt.ylabel(\"Total trips\")\n", + "plt.title(\"Total trips per continent\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "markdown", + "id": "8187877f-f14a-4afd-bcb2-50465b58b256", + "metadata": {}, + "source": [ + "To do that we need:\n", + "- A curve showing us the seasonlity curve per one region/country/city\n", + "- Show how accommodation and transportation prices move according that that curve.\n", + "\n", + "We will need to control for Europe, using:\n", + "- destination_continent == Yes\n", + "\n", + "Interpretation: pricing could be aligned with observable demand cycles. Therefore, we can propose good travel deals to price conscious travelers, outside the season.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 112, + "id": "c7cc45dc-0c43-4b69-9178-be4b6a589044", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# firstly, we will produce a normalized line chart:\n", + "# x: month\n", + "# y: three z-scored series:\n", + "# --seasonal demand index\n", + "# --mean accommodation price\n", + "# -- mean transportation price\n", + "\n", + "# remember, we will only work for trips in Europe, to keep it clean.\n", + "\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import calendar\n", + "\n", + "# 1. keep only trips to Europe\n", + "df_eu = df[df[\"destination_continent\"] == \"Europe\"].copy()\n", + "\n", + "# 2. monthly aggregates for Europe\n", + "monthly_eu = (\n", + " df_eu\n", + " .groupby(\"start_month\")\n", + " .agg(\n", + " demand=(\"start_month\", \"size\"),\n", + " mean_accommodation_cost=(\"accommodation_cost\", \"mean\"),\n", + " mean_transportation_cost=(\"transportation_cost\", \"mean\"),\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "# 3. z-score normalization per series, to rescale each series so they share a common scale and center, which makes their shapes directly comparable\n", + "for col in [\"demand\", \"mean_accommodation_cost\", \"mean_transportation_cost\"]:\n", + " monthly_eu[col + \"_z\"] = (monthly_eu[col] - monthly_eu[col].mean()) / monthly_eu[col].std()\n", + "\n", + "# 4. reshape for plotting\n", + "plot_df = monthly_eu.melt(\n", + " id_vars=\"start_month\",\n", + " value_vars=[\"demand_z\", \"mean_accommodation_cost_z\", \"mean_transportation_cost_z\"],\n", + " var_name=\"series\",\n", + " value_name=\"z_value\",\n", + ")\n", + "\n", + "label_map = {\n", + " \"demand_z\": \"Demand index (trips)\",\n", + " \"mean_accommodation_cost_z\": \"Accommodation cost\",\n", + " \"mean_transportation_cost_z\": \"Transportation cost\",\n", + "}\n", + "plot_df[\"series\"] = plot_df[\"series\"].map(label_map)\n", + "\n", + "# 5. line chart\n", + "plt.figure(figsize=(10,5))\n", + "sns.lineplot(\n", + " data=plot_df.sort_values(\"start_month\"),\n", + " x=\"start_month\",\n", + " y=\"z_value\",\n", + " hue=\"series\",\n", + " marker=\"o\",\n", + ")\n", + "\n", + "plt.xticks(\n", + " ticks=range(1, 13),\n", + " labels=calendar.month_abbr[1:13]\n", + ")\n", + "plt.xlabel(\"Start month\")\n", + "plt.ylabel(\"Z-scored value\")\n", + "plt.title(\"Normalized seasonality: demand vs prices in Europe\")\n", + "plt.axhline(0, color=\"grey\", linewidth=0.8)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "9127ebff-e59f-4c09-bb6f-a732f20ba020", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import calendar\n", + "\n", + "# 1. keep only trips to Europe\n", + "df_eu = df[df[\"destination_continent\"] == \"Asia\"].copy()\n", + "\n", + "# 2. monthly aggregates for Europe\n", + "monthly_eu = (\n", + " df_eu\n", + " .groupby(\"start_month\")\n", + " .agg(\n", + " demand=(\"start_month\", \"size\"),\n", + " mean_accommodation_cost=(\"accommodation_cost\", \"mean\"),\n", + " mean_transportation_cost=(\"transportation_cost\", \"mean\"),\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "# 3. z-score normalization per series, to rescale each series so they share a common scale and center, which makes their shapes directly comparable\n", + "for col in [\"demand\", \"mean_accommodation_cost\", \"mean_transportation_cost\"]:\n", + " monthly_eu[col + \"_z\"] = (monthly_eu[col] - monthly_eu[col].mean()) / monthly_eu[col].std()\n", + "\n", + "# 4. reshape for plotting\n", + "plot_df = monthly_eu.melt(\n", + " id_vars=\"start_month\",\n", + " value_vars=[\"demand_z\", \"mean_accommodation_cost_z\", \"mean_transportation_cost_z\"],\n", + " var_name=\"series\",\n", + " value_name=\"z_value\",\n", + ")\n", + "\n", + "label_map = {\n", + " \"demand_z\": \"Demand index (trips)\",\n", + " \"mean_accommodation_cost_z\": \"Accommodation cost\",\n", + " \"mean_transportation_cost_z\": \"Transportation cost\",\n", + "}\n", + "plot_df[\"series\"] = plot_df[\"series\"].map(label_map)\n", + "\n", + "# 5. line chart\n", + "plt.figure(figsize=(10,5))\n", + "sns.lineplot(\n", + " data=plot_df.sort_values(\"start_month\"),\n", + " x=\"start_month\",\n", + " y=\"z_value\",\n", + " hue=\"series\",\n", + " marker=\"o\",\n", + ")\n", + "\n", + "plt.xticks(\n", + " ticks=range(1, 13),\n", + " labels=calendar.month_abbr[1:13]\n", + ")\n", + "plt.xlabel(\"Start month\")\n", + "plt.ylabel(\"Z-scored value\")\n", + "plt.title(\"Normalized seasonality: demand vs prices in Asia\")\n", + "plt.axhline(0, color=\"grey\", linewidth=0.8)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "851e6c92-583b-4c3c-a358-f23d1995243c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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demandmean_accommodation_costmean_transportation_cost
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mean_accommodation_cost0.5036551.0000000.887682
mean_transportation_cost0.3866290.8876821.000000
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" + ], + "text/plain": [ + " demand mean_accommodation_cost \\\n", + "demand 1.000000 0.503655 \n", + "mean_accommodation_cost 0.503655 1.000000 \n", + "mean_transportation_cost 0.386629 0.887682 \n", + "\n", + " mean_transportation_cost \n", + "demand 0.386629 \n", + "mean_accommodation_cost 0.887682 \n", + "mean_transportation_cost 1.000000 " + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we also want to build a correlation table to seewhether the three European month-level series co-move\n", + "# assume monthly_eu already exists as in the previous code\n", + "# (with columns: demand, mean_accommodation_price, mean_transportation_price)\n", + "\n", + "corr = monthly_eu[[\n", + " \"demand\",\n", + " \"mean_accommodation_cost\",\n", + " \"mean_transportation_cost\"\n", + "]].corr(method=\"pearson\")\n", + "\n", + "corr\n" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "26fa754d-5adc-4f24-b9b4-f70a642ed830", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.colors import TwoSlopeNorm, LinearSegmentedColormap\n", + "\n", + "# Spearman correlations\n", + "corr_s = monthly_eu[[\n", + " \"demand\",\n", + " \"mean_accommodation_cost\",\n", + " \"mean_transportation_cost\"\n", + "]].corr(method=\"spearman\")\n", + "\n", + "# red (-1) -> white (0) -> green (1)\n", + "cmap = LinearSegmentedColormap.from_list(\n", + " \"red_white_green\",\n", + " [\"red\", \"white\", \"green\"]\n", + ")\n", + "\n", + "norm = TwoSlopeNorm(vmin=-1, vcenter=0.0, vmax=1)\n", + "\n", + "plt.figure(figsize=(6, 5))\n", + "sns.heatmap(\n", + " corr_s,\n", + " annot=True,\n", + " fmt=\".2f\",\n", + " cmap=cmap,\n", + " norm=norm,\n", + " square=False,\n", + " cbar=True,\n", + " annot_kws={\"size\": 12}\n", + ")\n", + "\n", + "plt.xticks(rotation=45, ha=\"right\", fontsize=11)\n", + "plt.yticks(rotation=0, fontsize=11)\n", + "plt.title(\"Spearman correlations (monthly, Europe)\", fontsize=13)\n", + "plt.tight_layout()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "8a79fa0e-6170-44f5-a9ea-e778f37b7777", + "metadata": {}, + "source": [ + "# Extra Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "id": "2acef47a-a4ff-4ed7-b9e2-5eea667aeec9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destination_citytrip_count
27Paris14
37Tokyo12
4Bali12
35Sydney12
31Rome9
5Bangkok8
25New York8
21London7
6Barcelona6
30Rio de Janeiro5
\n", + "
" + ], + "text/plain": [ + " destination_city trip_count\n", + "27 Paris 14\n", + "37 Tokyo 12\n", + "4 Bali 12\n", + "35 Sydney 12\n", + "31 Rome 9\n", + "5 Bangkok 8\n", + "25 New York 8\n", + "21 London 7\n", + "6 Barcelona 6\n", + "30 Rio de Janeiro 5" + ] + }, + "execution_count": 135, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "top10_cities = (\n", + " df.groupby(\"destination_city\")\n", + " .size()\n", + " .reset_index(name=\"trip_count\")\n", + " .sort_values(\"trip_count\", ascending=False)\n", + " .head(10)\n", + ")\n", + "\n", + "top10_cities\n" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "953ca840-255b-4d6a-a857-325727e2b749", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# now for these cities I would like to see a scatter plot showing the correlation between average trip duration and average cost per day, where average cost per day = (accommodation cost + transportation cost) divided by trip duration.\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = travel_trip_complete_christos_df.copy()\n", + "\n", + "# average cost per day\n", + "df[\"avg_cost_per_day\"] = (\n", + " (df[\"accommodation_cost\"] + df[\"transportation_cost\"]) / df[\"duration_days\"]\n", + ")\n", + "\n", + "# top 10 cities\n", + "top10 = (\n", + " df.groupby(\"destination_city\")\n", + " .size()\n", + " .sort_values(ascending=False)\n", + " .head(10)\n", + " .index\n", + ")\n", + "\n", + "df_top10 = df[df[\"destination_city\"].isin(top10)]\n", + "\n", + "# aggregates\n", + "city_stats = (\n", + " df_top10.groupby(\"destination_city\")\n", + " .agg(\n", + " mean_duration=(\"duration_days\", \"mean\"),\n", + " mean_cost_per_day=(\"avg_cost_per_day\", \"mean\"),\n", + " trip_count=(\"duration_days\", \"size\")\n", + " )\n", + " .reset_index()\n", + ")\n", + "\n", + "city_stats[\"bubble_size\"] = city_stats[\"trip_count\"] * 50\n", + "\n", + "plt.figure(figsize=(10,6))\n", + "\n", + "plt.scatter(\n", + " city_stats[\"mean_duration\"],\n", + " city_stats[\"mean_cost_per_day\"],\n", + " s=city_stats[\"bubble_size\"],\n", + " color=\"#b22d3e\",\n", + " alpha=0.8\n", + ")\n", + "\n", + "# label offset (tune these if needed)\n", + "x_offset = 0.3\n", + "y_offset = 0.3\n", + "\n", + "for _, row in city_stats.iterrows():\n", + " plt.text(\n", + " row[\"mean_duration\"] + x_offset,\n", + " row[\"mean_cost_per_day\"] + y_offset,\n", + " row[\"destination_city\"],\n", + " fontsize=9,\n", + " ha=\"left\",\n", + " va=\"center\"\n", + " )\n", + "\n", + "plt.xlabel(\"Average trip duration (days)\")\n", + "plt.ylabel(\"Average cost per day\")\n", + "plt.title(\"Top 10 Cities: Avg Duration vs Avg Cost per Day\")\n", + "plt.tight_layout()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "cb2c907c-729a-45cd-b0f7-bc4a0869f1c7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# top traveling nationalities in trip count.\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = travel_trip_complete_christos_df\n", + "\n", + "# --- compute top 5 ---\n", + "nat_counts = (\n", + " df.groupby(\"traveler_nationality_clean\")\n", + " .size()\n", + " .sort_values(ascending=False)\n", + ")\n", + "\n", + "top5 = nat_counts.head(5)\n", + "others = nat_counts.iloc[5:].sum()\n", + "\n", + "# combined series\n", + "pie_series = pd.concat([top5, pd.Series({\"Others\": others})])\n", + "\n", + "# --- plot ---\n", + "plt.figure(figsize=(7,7))\n", + "\n", + "plt.pie(\n", + " pie_series,\n", + " labels=pie_series.index,\n", + " autopct='%1.1f%%', # percentage values\n", + " pctdistance=0.8, # move percentages slightly outward\n", + " labeldistance=1.1, # move labels outside the pie\n", + " colors=[\"#b22d3e\",\"#026453\",\"#ddac34\",\"#1480d8\",\"#999999\",\"#CCCCCC\"][:len(pie_series)]\n", + ")\n", + "\n", + "plt.title(\"Traveler nationality distribution (Top 5 + Others)\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "fb2d8c4c-1868-452c-9795-2fdd0fca8641", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# I want a bar plot, proportion style, with x axis the top 5 traveler_nationalities, and values the percentage of different cities they have visited, in order from most expensive (average cost per day) to least expensive\n", + "df = travel_trip_complete_christos_df.copy()\n", + "\n", + "df[\"avg_cost_per_day\"] = (\n", + " (df[\"accommodation_cost\"] + df[\"transportation_cost\"]) / df[\"duration_days\"]\n", + ")\n", + "\n", + "top5_nats = (\n", + " df.groupby(\"traveler_nationality_clean\")\n", + " .size()\n", + " .sort_values(ascending=False)\n", + " .head(5)\n", + " .index\n", + ")\n", + "\n", + "df_top5 = df[df[\"traveler_nationality_clean\"].isin(top5_nats)]\n", + "\n", + "city_costs = (\n", + " df.groupby(\"destination_city\")[\"avg_cost_per_day\"]\n", + " .mean()\n", + " .sort_values(ascending=False)\n", + ")\n", + "\n", + "# expensive → cheap order\n", + "city_order = city_costs.index.tolist()\n", + "\n", + "ct = pd.crosstab(\n", + " df_top5[\"traveler_nationality_clean\"],\n", + " df_top5[\"destination_city\"]\n", + ")\n", + "ct_prop = ct.div(ct.sum(axis=1), axis=0)\n", + "\n", + "# keep only cities that appear in ct_prop, but ordered by global cost rank\n", + "filtered_city_order = [c for c in city_order if c in ct_prop.columns]\n", + "\n", + "ct_prop = ct_prop[filtered_city_order]\n", + "\n", + "# brand colours\n", + "brand_palette = [\"#b22d3e\", \"#026453\", \"#ddac34\", \"#1480d8\"]\n", + "\n", + "sns.set_theme(style=\"whitegrid\") # or other style\n", + "sns.set_palette(brand_palette)\n", + "\n", + "plt.rcParams[\"axes.prop_cycle\"] = plt.cycler(color=brand_palette)\n", + "\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "\n", + "ct_prop.plot(\n", + " kind=\"bar\",\n", + " stacked=True,\n", + " figsize=(12,6),\n", + " width=0.9,\n", + " colormap=\"viridis\" # or your brand colors if mapping manually\n", + ")\n", + "\n", + "plt.xlabel(\"Traveler nationality\")\n", + "plt.ylabel(\"Proportion of trips\")\n", + "plt.title(\"City visit distribution by nationality (sorted by avg cost/day)\")\n", + "\n", + "plt.legend(\n", + " title=\"Destination city (expensive → cheap)\",\n", + " bbox_to_anchor=(1.05, 1),\n", + " loc=\"upper left\"\n", + ")\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "948277f3-d786-4bfb-a966-95ebc58ee30e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ok now let's see average cost per day, for top 5 nationalities\n", + "df = travel_trip_complete_christos_df.copy()\n", + "\n", + "df[\"avg_cost_per_day\"] = (\n", + " (df[\"accommodation_cost\"] + df[\"transportation_cost\"]) / df[\"duration_days\"]\n", + ")\n", + "\n", + "top5_nats = (\n", + " df.groupby(\"traveler_nationality_clean\")\n", + " .size()\n", + " .sort_values(ascending=False)\n", + " .head(5)\n", + " .index\n", + ")\n", + "\n", + "df_top5 = df[df[\"traveler_nationality_clean\"].isin(top5_nats)]\n", + "\n", + "nat_costs = (\n", + " df_top5.groupby(\"traveler_nationality_clean\")[\"avg_cost_per_day\"]\n", + " .mean()\n", + " .sort_values(ascending=False)\n", + ")\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(8,5))\n", + "\n", + "plt.bar(\n", + " nat_costs.index,\n", + " nat_costs.values,\n", + " color=\"#b22d3e\" # brand orange for emphasis\n", + ")\n", + "\n", + "plt.xlabel(\"Traveler nationality\")\n", + "plt.ylabel(\"Average cost per day\")\n", + "plt.title(\"Average cost per day for top 5 traveler nationalities\")\n", + "plt.xticks(rotation=45, ha=\"right\")\n", + "plt.tight_layout()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b50a6cae-3572-4f77-8b51-a6b6da2d501d", + "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/analysis_travel_trip_hoaithuong.ipynb b/notebooks/analysis_travel_trip_hoaithuong.ipynb new file mode 100644 index 00000000..4ac0720b --- /dev/null +++ b/notebooks/analysis_travel_trip_hoaithuong.ipynb @@ -0,0 +1,1467 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d59f052-76ef-41c8-ae22-d76d2e3f7cb1", + "metadata": {}, + "source": [ + "# Calling the cleaned dataframe from the respective notebook" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "f1d024cc-bea2-4cb3-bb4d-fb7fa0c48b1b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_clean
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...1200Flight600.02023520235MayMayUnited States
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...800Flight500.02023620236JuneJuneCanada
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...1000Flight700.02023720237JulyJulySouth Korea
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2000Flight1000.02023820238AugustAugustUnited Kingdom
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...700Train200.02023920239SeptemberSeptemberVietnamese
..................................................................
132Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...2500Private Car2000.02023820238AugustAugustBrazil
133Vancouver, Canada136CanadaVancouver2023-08-012023-08-016.0emma wilson29.0Female...5000Airplane3000.02023820238AugustAugustCanada
134Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...2000Train1000.02023920239SeptemberSeptemberChina
135Barcelona, Spain138SpainBarcelona2023-09-012023-09-017.0sofia rodriguez25.0Female...6000Airplane2500.02023920239SeptemberSeptemberSpain
136Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...7000Train2500.0202310202310OctoberOctoberNew Zealander
\n", + "

137 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "132 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "133 Vancouver, Canada 136 Canada Vancouver \n", + "134 Bangkok, Thailand 137 Thailand Bangkok \n", + "135 Barcelona, Spain 138 Spain Barcelona \n", + "136 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-01 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-01 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-01 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "132 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "133 2023-08-01 2023-08-01 6.0 emma wilson 29.0 \n", + "134 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "135 2023-09-01 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "136 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_cost transportation_type \\\n", + "0 Male ... 1200 Flight \n", + "1 Female ... 800 Flight \n", + "2 Male ... 1000 Flight \n", + "3 Female ... 2000 Flight \n", + "4 Female ... 700 Train \n", + ".. ... ... ... ... \n", + "132 Male ... 2500 Private Car \n", + "133 Female ... 5000 Airplane \n", + "134 Male ... 2000 Train \n", + "135 Female ... 6000 Airplane \n", + "136 Male ... 7000 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600.0 2023 5 2023 5 \n", + "1 500.0 2023 6 2023 6 \n", + "2 700.0 2023 7 2023 7 \n", + "3 1000.0 2023 8 2023 8 \n", + "4 200.0 2023 9 2023 9 \n", + ".. ... ... ... ... ... \n", + "132 2000.0 2023 8 2023 8 \n", + "133 3000.0 2023 8 2023 8 \n", + "134 1000.0 2023 9 2023 9 \n", + "135 2500.0 2023 9 2023 9 \n", + "136 2500.0 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name traveler_nationality_clean \n", + "0 May May United States \n", + "1 June June Canada \n", + "2 July July South Korea \n", + "3 August August United Kingdom \n", + "4 September September Vietnamese \n", + ".. ... ... ... \n", + "132 August August Brazil \n", + "133 August August Canada \n", + "134 September September China \n", + "135 September September Spain \n", + "136 October October New Zealander \n", + "\n", + "[137 rows x 22 columns]" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_complete_hoaithuong_df = pd.read_parquet(\"../data/clean/travel_trip_complete.parquet\", engine=\"fastparquet\")\n", + "travel_trip_complete_hoaithuong_df" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "430b574d-818a-4f4c-a672-189062bbc6bb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['destination', 'trip_id', 'destination_country', 'destination_city',\n", + " 'start_date', 'end_date', 'duration_days', 'traveler_name',\n", + " 'traveler_age', 'traveler_gender', 'traveler_nationality',\n", + " 'accommodation_type', 'accommodation_cost', 'transportation_type',\n", + " 'transportation_cost', 'end_year', 'end_month', 'start_year',\n", + " 'start_month', 'start_month_name', 'end_month_name',\n", + " 'traveler_nationality_clean'],\n", + " dtype='object')" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_hoaithuong_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "0ec9b9e2-f3f1-4f73-a64c-473495fc02b3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_clean
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...1200Flight600.02023520235MayMayUnited States
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...800Flight500.02023620236JuneJuneCanada
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...1000Flight700.02023720237JulyJulySouth Korea
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2000Flight1000.02023820238AugustAugustUnited Kingdom
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...700Train200.02023920239SeptemberSeptemberVietnamese
\n", + "

5 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city start_date \\\n", + "0 London, UK 1 UK London 2023-05-01 \n", + "1 Phuket, Thailand 2 Thailand Phuket 2023-06-01 \n", + "2 Bali, Indonesia 3 Indonesia Bali 2023-07-01 \n", + "3 New York, USA 4 USA New York 2023-08-01 \n", + "4 Tokyo, Japan 5 Japan Tokyo 2023-09-01 \n", + "\n", + " end_date duration_days traveler_name traveler_age traveler_gender ... \\\n", + "0 2023-05-01 7.0 john smith 35.0 Male ... \n", + "1 2023-06-01 5.0 jane doe 28.0 Female ... \n", + "2 2023-07-01 7.0 david lee 45.0 Male ... \n", + "3 2023-08-01 14.0 sarah johnson 29.0 Female ... \n", + "4 2023-09-01 7.0 kim nguyen 26.0 Female ... \n", + "\n", + " accommodation_cost transportation_type transportation_cost end_year \\\n", + "0 1200 Flight 600.0 2023 \n", + "1 800 Flight 500.0 2023 \n", + "2 1000 Flight 700.0 2023 \n", + "3 2000 Flight 1000.0 2023 \n", + "4 700 Train 200.0 2023 \n", + "\n", + " end_month start_year start_month start_month_name end_month_name \\\n", + "0 5 2023 5 May May \n", + "1 6 2023 6 June June \n", + "2 7 2023 7 July July \n", + "3 8 2023 8 August August \n", + "4 9 2023 9 September September \n", + "\n", + " traveler_nationality_clean \n", + "0 United States \n", + "1 Canada \n", + "2 South Korea \n", + "3 United Kingdom \n", + "4 Vietnamese \n", + "\n", + "[5 rows x 22 columns]" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_hoaithuong_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "696693d2-a2d4-4709-af07-687c6308f9a0", + "metadata": {}, + "outputs": [], + "source": [ + "# 3. (count of) Accommodation type per trip duration" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "6b9b7d27-9f9a-4cb6-ad2e-23ba97bd771d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Hotel\n", + "1 Resort\n", + "2 Villa\n", + "3 Hotel\n", + "4 Airbnb\n", + " ... \n", + "132 Hostel\n", + "133 Hotel\n", + "134 Hostel\n", + "135 Airbnb\n", + "136 Hotel\n", + "Name: accommodation_type, Length: 137, dtype: object" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_hoaithuong_df['accommodation_type']" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "d760ce0c-c751-49b9-a5dd-0972a6966c80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Hotel', 'Resort', 'Villa', 'Airbnb', 'Hostel', 'Vacation rental'],\n", + " dtype=object)" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_hoaithuong_df['accommodation_type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "e2840755-2d84-433e-a1ca-65900766a93f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "accommodation_type Airbnb Hostel Hotel Resort Vacation rental Villa\n", + "duration_days \n", + "5.0 1 1 7 1 0 0\n", + "6.0 3 1 10 0 1 1\n", + "7.0 13 5 25 7 1 3\n", + "8.0 4 7 10 3 0 0\n", + "9.0 4 5 4 1 2 0\n", + "10.0 4 3 2 1 0 0\n", + "11.0 1 2 0 1 0 1\n", + "13.0 0 0 1 0 0 0\n", + "14.0 0 0 1 0 0 0\n" + ] + } + ], + "source": [ + "result = travel_trip_complete_hoaithuong_df.groupby(\"duration_days\")[\"accommodation_type\"].value_counts().unstack(fill_value=0)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9e950e9c-165c-46eb-954f-bba3f4b3dac7", + "metadata": {}, + "outputs": [], + "source": [ + "* Trips with a 7-day duration are the most common\n", + "The highest number of travelers appears in the 7.0-day group.\n", + "Many accommodation types are chosen for this duration, especially Hotels and Airbnb.\n", + "\n", + "* Hotels are the most popular accommodation type across almost all trip durations\n", + "Particularly dominant for trips of 5, 6, 7, 8, and 9 days.\n", + "\n", + "* Airbnb is also strong but still less common than Hotels\n", + "Frequently chosen for trips lasting 6 to 9 days.\n", + "\n", + "* Resorts tend to be selected for longer trips\n", + "Most commonly seen in 7-day and 8-day trips.\n", + "\n", + "* Vacation rentals and Villas are less frequently chosen\n", + "They appear only in a few specific trip durations.\n", + "Not a popular choice among travelers.\n", + "\n", + "* Trips lasting 13 and 14 days only chose Hotels\n", + "Travelers on very long trips prefer Hotels due to stability, convenience, and ease of booking." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "8f6577d6-ccca-44de-b15c-055f87c59b94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "df_pivot = result\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "\n", + "df_pivot.plot(kind=\"bar\", stacked=True, figsize=(12, 7))\n", + "\n", + "plt.title(\"Accommodation Type Count per Trip Duration\", fontsize=16)\n", + "plt.xlabel(\"Trip Duration (days)\", fontsize=14)\n", + "plt.ylabel(\"Count of Accommodation Types\", fontsize=14)\n", + "plt.xticks(rotation=0)\n", + "plt.legend(title=\"Accommodation Type\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "347b4724-30ec-456f-8fc3-ac3a7230a48e", + "metadata": {}, + "outputs": [], + "source": [ + "# 4. (count of) transportation_type per traveler_age_segment" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "813f25e9-1a7d-46c5-8771-5ca1494a57c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_age_segment\n", + "26-33 Young Professionals 72\n", + "34-40 Young Parents 25\n", + "41-50 Money & Energy Group 25\n", + "18-25 Students-Early Professionals 13\n", + "51-60 Rich but Tired 2\n", + "61+ Retired-Elderly 0\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "age_bins = [0, 26, 34, 41, 51, 61, 120]\n", + "age_labels = [\n", + " \"18-25 Students-Early Professionals\", # [0,26) -> effectively 18–25 in your data\n", + " \"26-33 Young Professionals\", # [26,34)\n", + " \"34-40 Young Parents\", # [34,41)\n", + " \"41-50 Money & Energy Group\", # [41,51)\n", + " \"51-60 Rich but Tired\", # [51,61)\n", + " \"61+ Retired-Elderly\" # [61,120)\n", + "]\n", + "\n", + "travel_trip_complete_hoaithuong_df[\"traveler_age_segment\"] = pd.cut(\n", + " travel_trip_complete_hoaithuong_df[\"traveler_age\"],\n", + " bins=age_bins,\n", + " labels=age_labels,\n", + " right=False\n", + ")\n", + "\n", + "travel_trip_complete_hoaithuong_df[\"traveler_age_segment\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "6701f148-01ab-440f-8f63-6e3e8f0fe7b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " traveler_age_segment transportation_type\n", + "0 34-40 Young Parents Flight\n", + "1 26-33 Young Professionals Flight\n", + "2 41-50 Money & Energy Group Flight\n", + "3 26-33 Young Professionals Flight\n", + "4 18-25 Students-Early Professionals Train\n", + ".. ... ...\n", + "132 34-40 Young Parents Private Car\n", + "133 26-33 Young Professionals Airplane\n", + "134 26-33 Young Professionals Train\n", + "135 18-25 Students-Early Professionals Airplane\n", + "136 34-40 Young Parents Train\n", + "\n", + "[137 rows x 2 columns]\n" + ] + } + ], + "source": [ + "travel_trip_complete_hoaithuong_df['traveler_age_segment'] = pd.cut(travel_trip_complete_hoaithuong_df['traveler_age'], bins=age_bins, labels=age_labels, right=True)\n", + "\n", + "output = travel_trip_complete_hoaithuong_df[['traveler_age_segment', 'transportation_type']]\n", + "\n", + "print(output)" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "87a3ae3a-4ed2-4da4-9eee-b2258f145727", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_typeAirplaneBusFerryFlightPrivate CarRental CarSubwayTrain
traveler_age_segment
18-25 Students-Early Professionals92020204
26-33 Young Professionals2931619118
34-40 Young Parents1310210011
41-50 Money & Energy Group100031204
51-60 Rich but Tired10000001
\n", + "
" + ], + "text/plain": [ + "transportation_type Airplane Bus Ferry Flight Private Car \\\n", + "traveler_age_segment \n", + "18-25 Students-Early Professionals 9 2 0 2 0 \n", + "26-33 Young Professionals 29 3 1 6 1 \n", + "34-40 Young Parents 13 1 0 2 1 \n", + "41-50 Money & Energy Group 10 0 0 3 1 \n", + "51-60 Rich but Tired 1 0 0 0 0 \n", + "\n", + "transportation_type Rental Car Subway Train \n", + "traveler_age_segment \n", + "18-25 Students-Early Professionals 2 0 4 \n", + "26-33 Young Professionals 9 1 18 \n", + "34-40 Young Parents 0 0 11 \n", + "41-50 Money & Energy Group 2 0 4 \n", + "51-60 Rich but Tired 0 0 1 " + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "transport_count = pd.crosstab(travel_trip_complete_hoaithuong_df['traveler_age_segment'], travel_trip_complete_hoaithuong_df['transportation_type'])\n", + "transport_count" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84322ea7-0942-4004-9bd0-e9ccc790e4de", + "metadata": {}, + "outputs": [], + "source": [ + "#count of transportation_type per traveler_age_segment" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "6e27238e-c9c6-48c1-bb57-02f5ed08c2c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
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accommodation_typecountmeanminmax
0Airbnb307.7666675.011.0
1Hostel248.2916675.011.0
2Hotel607.2166675.014.0
3Resort147.7142865.011.0
4Vacation rental47.7500006.09.0
5Villa57.6000006.011.0
\n", + "
" + ], + "text/plain": [ + " accommodation_type count mean min max\n", + "0 Airbnb 30 7.766667 5.0 11.0\n", + "1 Hostel 24 8.291667 5.0 11.0\n", + "2 Hotel 60 7.216667 5.0 14.0\n", + "3 Resort 14 7.714286 5.0 11.0\n", + "4 Vacation rental 4 7.750000 6.0 9.0\n", + "5 Villa 5 7.600000 6.0 11.0" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "duration_stats = travel_trip_complete_hoaithuong_df.groupby('accommodation_type')['duration_days'].agg(['count', 'mean', 'min', 'max']).reset_index()\n", + "duration_stats" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "1b1949ae-73f9-4831-9542-4d70ef502ce2", + "metadata": {}, + "outputs": [], + "source": [ + "#Airbnb: 30 trips, average duration ~7.8 days, shortest 5 days, longest 11 days.\n", + "#Hostel: 24 trips, average duration ~8.3 days, shortest 5 days, longest 11 days.\n", + "#Hotel: 60 trips, average duration ~7.2 days, shortest 5 days, longest 14 days.\n", + "#Resort: 14 trips, average duration ~7.7 days, shortest 5 days, longest 11 days.\n", + "#Vacation rental: 4 trips, average duration ~7.8 days, shortest 6 days, longest 9 days.\n", + "#Villa: 5 trips, average duration ~7.6 days, shortest 6 days, longest 11 days.\n", + "#Interpretation: \n", + " #Hotels have the highest number of trips and the longest maximum duration.\n", + " #Hostels tend to have slightly longer average trips than other accommodations. \n", + " #Vacation rentals and Villas are less frequent but have moderate durations." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "f311b445-b9f8-4300-9c5b-68e7015df4cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "yerr = [duration_stats[\"mean\"] - duration_stats[\"min\"], duration_stats[\"max\"] - duration_stats[\"mean\"]]\n", + "\n", + "plt.figure(figsize=(10,6))\n", + "plt.bar(duration_stats[\"accommodation_type\"], duration_stats[\"mean\"], yerr=yerr, capsize=5, color='skyblue')\n", + "plt.xlabel(\"Accommodation Type\")\n", + "plt.ylabel(\"Average Trip Duration (days)\")\n", + "plt.title(\"Trip Duration by Accommodation Type\")\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f49e9f16-0ecd-4e4e-91d0-5f3bc0e941c1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "5df391d9-71b0-497e-b7ef-9d45e39b4fed", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_age_segment\n", + "26-33 Young Professionals 72\n", + "34-40 Young Parents 25\n", + "41-50 Money & Energy Group 25\n", + "18-25 Students-Early Professionals 13\n", + "51-60 Rich but Tired 2\n", + "61+ Retired-Elderly 0\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "age_bins = [0, 26, 34, 41, 51, 61, 120]\n", + "age_labels = [\n", + " \"18-25 Students-Early Professionals\", # [0,26) -> effectively 18–25 in your data\n", + " \"26-33 Young Professionals\", # [26,34)\n", + " \"34-40 Young Parents\", # [34,41)\n", + " \"41-50 Money & Energy Group\", # [41,51)\n", + " \"51-60 Rich but Tired\", # [51,61)\n", + " \"61+ Retired-Elderly\" # [61,120)\n", + "]\n", + "\n", + "travel_trip_complete_hoaithuong_df[\"traveler_age_segment\"] = pd.cut(\n", + " travel_trip_complete_hoaithuong_df[\"traveler_age\"],\n", + " bins=age_bins,\n", + " labels=age_labels,\n", + " right=False\n", + ")\n", + "\n", + "travel_trip_complete_hoaithuong_df[\"traveler_age_segment\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "9d2eb19f-40c9-4311-81a3-887e6ef36966", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Age Segment Count Percentage\n", + "0 18-25 Students-Early Professionals 13 9.489051\n", + "1 26-33 Young Professionals 72 52.554745\n", + "2 34-40 Young Parents 25 18.248175\n", + "3 41-50 Money & Energy Group 25 18.248175\n", + "4 51-60 Rich but Tired 2 1.459854\n", + "5 61+ Retired-Elderly 0 0.000000\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "traveler_age_segment = {\n", + " 'Age Segment': [\n", + " '18-25 Students-Early Professionals',\n", + " '26-33 Young Professionals',\n", + " '34-40 Young Parents',\n", + " '41-50 Money & Energy Group',\n", + " '51-60 Rich but Tired',\n", + " '61+ Retired-Elderly'\n", + " ],\n", + " 'Count': [13, 72, 25, 25, 2, 0]\n", + "}\n", + "\n", + "travel_trip_complete_hoaithuong_df = pd.DataFrame(traveler_age_segment)\n", + "\n", + "travel_trip_complete_hoaithuong_df['Percentage'] = travel_trip_complete_hoaithuong_df['Count'] / travel_trip_complete_hoaithuong_df['Count'].sum() * 100\n", + "\n", + "print(travel_trip_complete_hoaithuong_df)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "63e2d310-c28b-4c1f-ac56-c5a979f10f2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8,8))\n", + "plt.pie(travel_trip_complete_hoaithuong_df['Count'], labels=travel_trip_complete_hoaithuong_df['Age Segment'], autopct='%1.1f%%', startangle=140)\n", + "plt.title(\"Trips Distribution by Traveler Age Segment\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "083ce1eb-bbe8-47da-ad57-337e7c300473", + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/clean_databse.ipynb b/notebooks/clean_databse.ipynb new file mode 100644 index 00000000..0bc4c93d --- /dev/null +++ b/notebooks/clean_databse.ipynb @@ -0,0 +1,6031 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "44384538-89c5-467f-93f1-ea71d83d7cba", + "metadata": {}, + "source": [ + "# Creating a clean database" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e77d16e1-f639-40cb-acec-9497635332e9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Trip IDDestinationStart dateEnd dateDuration (days)Traveler nameTraveler ageTraveler genderTraveler nationalityAccommodation typeAccommodation costTransportation typeTransportation cost
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
56Paris, France10/5/202310/10/20235.0Michael Brown42.0MaleAmericanHotel1500Flight800
67Sydney, Australia11/20/202311/30/202310.0Emily Davis33.0FemaleAustralianHostel500Flight1200
78Rio de Janeiro, Brazil1/5/20241/12/20247.0Lucas Santos25.0MaleBrazilianAirbnb900Flight600
89Amsterdam, Netherlands2/14/20242/21/20247.0Laura Janssen31.0FemaleDutchHotel1200Train200
910Dubai, United Arab Emirates3/10/20243/17/20247.0Mohammed Ali39.0MaleEmiratiResort2500Flight800
\n", + "
" + ], + "text/plain": [ + " Trip ID Destination Start date End date \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 \n", + "5 6 Paris, France 10/5/2023 10/10/2023 \n", + "6 7 Sydney, Australia 11/20/2023 11/30/2023 \n", + "7 8 Rio de Janeiro, Brazil 1/5/2024 1/12/2024 \n", + "8 9 Amsterdam, Netherlands 2/14/2024 2/21/2024 \n", + "9 10 Dubai, United Arab Emirates 3/10/2024 3/17/2024 \n", + "\n", + " Duration (days) Traveler name Traveler age Traveler gender \\\n", + "0 7.0 John Smith 35.0 Male \n", + "1 5.0 Jane Doe 28.0 Female \n", + "2 7.0 David Lee 45.0 Male \n", + "3 14.0 Sarah Johnson 29.0 Female \n", + "4 7.0 Kim Nguyen 26.0 Female \n", + "5 5.0 Michael Brown 42.0 Male \n", + "6 10.0 Emily Davis 33.0 Female \n", + "7 7.0 Lucas Santos 25.0 Male \n", + "8 7.0 Laura Janssen 31.0 Female \n", + "9 7.0 Mohammed Ali 39.0 Male \n", + "\n", + " Traveler nationality Accommodation type Accommodation cost \\\n", + "0 American Hotel 1200 \n", + "1 Canadian Resort 800 \n", + "2 Korean Villa 1000 \n", + "3 British Hotel 2000 \n", + "4 Vietnamese Airbnb 700 \n", + "5 American Hotel 1500 \n", + "6 Australian Hostel 500 \n", + "7 Brazilian Airbnb 900 \n", + "8 Dutch Hotel 1200 \n", + "9 Emirati Resort 2500 \n", + "\n", + " Transportation type Transportation cost \n", + "0 Flight 600 \n", + "1 Flight 500 \n", + "2 Flight 700 \n", + "3 Flight 1000 \n", + "4 Train 200 \n", + "5 Flight 800 \n", + "6 Flight 1200 \n", + "7 Flight 600 \n", + "8 Train 200 \n", + "9 Flight 800 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_data = \"../data/raw/Travel details dataset.csv\"\n", + "travel_trip_full_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + "travel_trip_full_df.head(10)" + ] + }, + { + "cell_type": "markdown", + "id": "d008abb1-c592-4c7a-9f0c-4f74a1625544", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## Christos code" + ] + }, + { + "cell_type": "markdown", + "id": "4add126f-85e7-4a60-a842-31b1490b8992", + "metadata": {}, + "source": [ + "### Cleaning column names" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "581a6aa8-363d-46d6-becc-9e2d9b99a766", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['trip_id', 'destination', 'start_date', 'end_date', 'duration_days',\n", + " 'traveler_name', 'traveler_age', 'traveler_gender',\n", + " 'traveler_nationality', 'accommodation_type', 'accommodation_cost',\n", + " 'transportation_type', 'transportation_cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.columns = travel_trip_full_df.columns.str.replace(\"\", \"\", regex=False).str.replace(r\"[()]\", \"\", regex=True).str.strip(\")\").str.replace(\" \",\"_\").str.lower()\n", + "travel_trip_full_df.columns " + ] + }, + { + "cell_type": "markdown", + "id": "3bda05a4-4610-4c4f-89ce-e6ce4526a79f", + "metadata": {}, + "source": [ + "### Cleaning destination" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "93e860c1-f699-4732-9cf7-66ea050c4464", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_costdestination_citydestination_country
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600LondonUK
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500PhuketThailand
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700BaliIndonesia
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000New YorkUSA
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200TokyoJapan
................................................
134135Rio de Janeiro, Brazil8/1/20238/10/20239.0Jose Perez37.0MaleBrazilianHostel2500Car2000Rio de JaneiroBrazil
135136Vancouver, Canada8/15/20238/21/20236.0Emma Wilson29.0FemaleCanadianHotel5000Airplane3000VancouverCanada
136137Bangkok, Thailand9/1/20239/8/20237.0Ryan Chen34.0MaleChineseHostel2000Train1000BangkokThailand
137138Barcelona, Spain9/15/20239/22/20237.0Sofia Rodriguez25.0FemaleSpanishAirbnb6000Airplane2500BarcelonaSpain
138139Auckland, New Zealand10/1/202310/8/20237.0William Brown39.0MaleNew ZealanderHotel7000Train2500AucklandNew Zealand
\n", + "

139 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 7.0 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 5.0 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 7.0 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 14.0 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 7.0 \n", + ".. ... ... ... ... ... \n", + "134 135 Rio de Janeiro, Brazil 8/1/2023 8/10/2023 9.0 \n", + "135 136 Vancouver, Canada 8/15/2023 8/21/2023 6.0 \n", + "136 137 Bangkok, Thailand 9/1/2023 9/8/2023 7.0 \n", + "137 138 Barcelona, Spain 9/15/2023 9/22/2023 7.0 \n", + "138 139 Auckland, New Zealand 10/1/2023 10/8/2023 7.0 \n", + "\n", + " traveler_name traveler_age traveler_gender traveler_nationality \\\n", + "0 John Smith 35.0 Male American \n", + "1 Jane Doe 28.0 Female Canadian \n", + "2 David Lee 45.0 Male Korean \n", + "3 Sarah Johnson 29.0 Female British \n", + "4 Kim Nguyen 26.0 Female Vietnamese \n", + ".. ... ... ... ... \n", + "134 Jose Perez 37.0 Male Brazilian \n", + "135 Emma Wilson 29.0 Female Canadian \n", + "136 Ryan Chen 34.0 Male Chinese \n", + "137 Sofia Rodriguez 25.0 Female Spanish \n", + "138 William Brown 39.0 Male New Zealander \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + ".. ... ... ... \n", + "134 Hostel 2500 Car \n", + "135 Hotel 5000 Airplane \n", + "136 Hostel 2000 Train \n", + "137 Airbnb 6000 Airplane \n", + "138 Hotel 7000 Train \n", + "\n", + " transportation_cost destination_city destination_country \n", + "0 600 London UK \n", + "1 500 Phuket Thailand \n", + "2 700 Bali Indonesia \n", + "3 1000 New York USA \n", + "4 200 Tokyo Japan \n", + ".. ... ... ... \n", + "134 2000 Rio de Janeiro Brazil \n", + "135 3000 Vancouver Canada \n", + "136 1000 Bangkok Thailand \n", + "137 2500 Barcelona Spain \n", + "138 2500 Auckland New Zealand \n", + "\n", + "[139 rows x 15 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I want to take the column destination, and rather split it in two, destination_city and destination_country\n", + "\n", + "# Split on the first comma into two columns\n", + "travel_trip_full_df[[\"destination_city\", \"destination_country\"]] = (\n", + " travel_trip_full_df[\"destination\"]\n", + " .str.split(\",\", n=1, expand=True)\n", + ")\n", + "\n", + "# Clean up whitespace\n", + "travel_trip_full_df[\"destination_city\"] = (\n", + " travel_trip_full_df[\"destination_city\"].str.strip()\n", + ")\n", + "travel_trip_full_df[\"destination_country\"] = (\n", + " travel_trip_full_df[\"destination_country\"].str.strip()\n", + ")\n", + "\n", + "travel_trip_full_df" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db820e15-7113-434d-83aa-dab843a8a26b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
0London, UK1UKLondon5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
1Phuket, Thailand2ThailandPhuket6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
2Bali, Indonesia3IndonesiaBali7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
3New York, USA4USANew York8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
4Tokyo, Japan5JapanTokyo9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro8/1/20238/10/20239.0Jose Perez37.0MaleBrazilianHostel2500Car2000
135Vancouver, Canada136CanadaVancouver8/15/20238/21/20236.0Emma Wilson29.0FemaleCanadianHotel5000Airplane3000
136Bangkok, Thailand137ThailandBangkok9/1/20239/8/20237.0Ryan Chen34.0MaleChineseHostel2000Train1000
137Barcelona, Spain138SpainBarcelona9/15/20239/22/20237.0Sofia Rodriguez25.0FemaleSpanishAirbnb6000Airplane2500
138Auckland, New Zealand139New ZealandAuckland10/1/202310/8/20237.0William Brown39.0MaleNew ZealanderHotel7000Train2500
\n", + "

139 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 5/1/2023 5/8/2023 7.0 John Smith 35.0 \n", + "1 6/15/2023 6/20/2023 5.0 Jane Doe 28.0 \n", + "2 7/1/2023 7/8/2023 7.0 David Lee 45.0 \n", + "3 8/15/2023 8/29/2023 14.0 Sarah Johnson 29.0 \n", + "4 9/10/2023 9/17/2023 7.0 Kim Nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 8/1/2023 8/10/2023 9.0 Jose Perez 37.0 \n", + "135 8/15/2023 8/21/2023 6.0 Emma Wilson 29.0 \n", + "136 9/1/2023 9/8/2023 7.0 Ryan Chen 34.0 \n", + "137 9/15/2023 9/22/2023 7.0 Sofia Rodriguez 25.0 \n", + "138 10/1/2023 10/8/2023 7.0 William Brown 39.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type \\\n", + "0 Male American Hotel \n", + "1 Female Canadian Resort \n", + "2 Male Korean Villa \n", + "3 Female British Hotel \n", + "4 Female Vietnamese Airbnb \n", + ".. ... ... ... \n", + "134 Male Brazilian Hostel \n", + "135 Female Canadian Hotel \n", + "136 Male Chinese Hostel \n", + "137 Female Spanish Airbnb \n", + "138 Male New Zealander Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600 \n", + "1 800 Flight 500 \n", + "2 1000 Flight 700 \n", + "3 2000 Flight 1000 \n", + "4 700 Train 200 \n", + ".. ... ... ... \n", + "134 2500 Car 2000 \n", + "135 5000 Airplane 3000 \n", + "136 2000 Train 1000 \n", + "137 6000 Airplane 2500 \n", + "138 7000 Train 2500 \n", + "\n", + "[139 rows x 15 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# will however first move the two new columns on the right and next to destination\n", + "cols = list(travel_trip_full_df.columns)\n", + "cols.insert(cols.index(\"destination\") + 1, cols.pop(cols.index(\"destination_city\")))\n", + "travel_trip_full_df = travel_trip_full_df.loc[:, cols]\n", + "\n", + "cols = list(travel_trip_full_df.columns)\n", + "cols.insert(cols.index(\"destination_city\") - 1, cols.pop(cols.index(\"destination_country\")))\n", + "travel_trip_full_df = travel_trip_full_df.loc[:, cols]\n", + "\n", + "cols = list(travel_trip_full_df.columns)\n", + "cols.insert(cols.index(\"destination_country\") - 1, cols.pop(cols.index(\"destination\")))\n", + "travel_trip_full_df = travel_trip_full_df.loc[:, cols]\n", + "\n", + "travel_trip_full_df" + ] + }, + { + "cell_type": "markdown", + "id": "f4179c0e-4771-4e20-9be3-183e3386071c", + "metadata": {}, + "source": [ + "### Searching for NaN values" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6653399a-1ec9-4f8c-b39f-0bd093f2a72a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination 2\n", + "trip_id 0\n", + "destination_country 68\n", + "destination_city 2\n", + "start_date 2\n", + "end_date 2\n", + "duration_days 2\n", + "traveler_name 2\n", + "traveler_age 2\n", + "traveler_gender 2\n", + "traveler_nationality 2\n", + "accommodation_type 2\n", + "accommodation_cost 2\n", + "transportation_type 3\n", + "transportation_cost 3\n", + "dtype: int64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "40a6909d-672b-4f4d-bea8-52bef702f595", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['London, UK', 'Phuket, Thailand', 'Bali, Indonesia',\n", + " 'New York, USA', 'Tokyo, Japan', 'Paris, France',\n", + " 'Sydney, Australia', 'Rio de Janeiro, Brazil',\n", + " 'Amsterdam, Netherlands', 'Dubai, United Arab Emirates',\n", + " 'Cancun, Mexico', 'Barcelona, Spain', 'Honolulu, Hawaii',\n", + " 'Berlin, Germany', 'Marrakech, Morocco', 'Edinburgh, Scotland',\n", + " 'Paris', 'Bali', 'London', 'Tokyo', 'New York', 'Sydney', 'Rome',\n", + " 'Bangkok', 'Hawaii', 'Barcelona', 'Japan', 'Thailand', 'France',\n", + " 'Australia', 'Brazil', 'Greece', 'Egypt', 'Mexico', 'Italy',\n", + " 'Spain', 'Canada', 'New York City, USA', 'Bangkok, Thailand',\n", + " 'Vancouver, Canada', 'Sydney, AUS', 'Seoul, South Korea',\n", + " 'Los Angeles, USA', 'Rome, Italy', 'Cape Town', nan,\n", + " 'Cape Town, SA', 'Sydney, Aus', 'Bangkok, Thai', 'Phuket, Thai',\n", + " 'Dubai', 'Seoul', 'Rio de Janeiro', 'Amsterdam', 'Phuket',\n", + " 'Santorini', 'Phnom Penh', 'Athens, Greece',\n", + " 'Cape Town, South Africa', 'Auckland, New Zealand'], dtype=object)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the destinatio column to see what destination_city has so many NaN values. \n", + "# It shows that for many rows we only have information about the city. \n", + "# That's no problem, we can easily extrapolate the country frrom it.\n", + "travel_trip_full_df.destination.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "73b2ed10-040e-4391-bef6-b20b06011d7f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "# we can immediately identify that most columns have 2 NaN values. Will do a test to check whether those values are in the same rows\n", + "\n", + "assumption_columns = [\n", + " \"destination\",\n", + " \"destination_city\",\n", + " \"start_date\",\n", + " \"end_date\",\n", + " \"duration_days\",\n", + " \"traveler_name\",\n", + " \"traveler_age\",\n", + " \"traveler_gender\",\n", + " \"traveler_nationality\",\n", + " \"accommodation_type\",\n", + " \"accommodation_cost\"\n", + "]\n", + "\n", + "all_null_test = travel_trip_full_df[assumption_columns].isna().all(axis=1)\n", + "all_null_count = all_null_test.sum()\n", + "print(all_null_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8d070289-cdfe-40a5-9a71-bc5297aad981", + "metadata": {}, + "outputs": [], + "source": [ + "# our assumption is true, those two rows are fully empty and we can drop them\n", + "travel_trip_full_df = travel_trip_full_df.dropna(thresh=2)" + ] + }, + { + "cell_type": "markdown", + "id": "68754ac4-78c3-45c8-88b7-df363141a9e5", + "metadata": {}, + "source": [ + "### Checking for duplicates" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a897c610-eafd-4189-812a-5f03ed34c5e3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "7c821eec-659f-4324-8905-ea250cb8f601", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(28)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.traveler_name.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f42acbdc-91c1-474f-908f-c8e1b288393f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['John Smith', 'Jane Doe', 'David Lee', 'Sarah Johnson',\n", + " 'Kim Nguyen', 'Michael Brown', 'Emily Davis', 'Lucas Santos',\n", + " 'Laura Janssen', 'Mohammed Ali', 'Ana Hernandez', 'Carlos Garcia',\n", + " 'Lily Wong', 'Hans Mueller', 'Fatima Khouri', 'James MacKenzie',\n", + " 'Michael Chang', 'Olivia Rodriguez', 'Kenji Nakamura', 'Emily Lee',\n", + " 'James Wilson', 'Sofia Russo', 'Raj Patel', 'Lily Nguyen',\n", + " 'David Kim', 'Maria Garcia', 'Alice Smith', 'Bob Johnson',\n", + " 'Charlie Lee', 'Emma Davis', 'Olivia Martin', 'Harry Wilson',\n", + " 'Sophia Lee', 'James Brown', 'Mia Johnson', 'William Davis',\n", + " 'Amelia Brown', 'Adam Lee', 'Sarah Wong', 'Maria Silva',\n", + " 'Peter Brown', 'Emma Garcia', 'Michael Davis', 'Nina Patel',\n", + " 'Kevin Kim', 'Laura van den Berg', 'Jennifer Nguyen', 'Rachel Lee',\n", + " 'Jessica Wong', 'Felipe Almeida', 'Nisa Patel', 'Ben Smith',\n", + " 'Laura Gomez', 'Park Min Woo', 'Michael Chen', 'Sofia Rossi',\n", + " 'Rachel Sanders', 'Emily Watson', 'Ana Rodriguez', 'Tom Wilson',\n", + " 'Olivia Green', 'James Chen', 'Lila Patel', 'Marco Rossi',\n", + " 'Sarah Brown', 'Sarah Lee', 'Alex Kim', 'Maria Hernandez',\n", + " 'Mark Johnson', 'Amanda Chen', 'Nana Kwon', 'Tom Hanks',\n", + " 'Emma Watson', 'James Kim', 'Fatima Ahmed', 'Liam Nguyen',\n", + " 'Giulia Rossi', 'Putra Wijaya', 'Kim Min-ji', 'Emily Johnson',\n", + " 'Michael Wong', 'Jessica Chen', 'Ken Tanaka', 'Rodrigo Oliveira',\n", + " 'Olivia Kim', 'Robert Mueller', 'Lisa Chen', 'Emily Wong',\n", + " 'Mark Tan', 'Emma Lee', 'George Chen', 'Sophia Kim', 'Alex Ng',\n", + " 'Cindy Chen', 'Emily Kim', 'Frank Li', 'Gina Lee', 'Henry Kim',\n", + " 'Isabella Chen', 'Jack Smith', 'Katie Johnson', 'John Doe',\n", + " 'Jane Smith', 'Michael Johnson', 'Jose Perez', 'Emma Wilson',\n", + " 'Ryan Chen', 'Sofia Rodriguez', 'William Brown'], dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.traveler_name.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7b984db1-013f-42a0-b2d3-f0416eee17db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['john smith', 'jane doe', 'david lee', 'sarah johnson',\n", + " 'kim nguyen', 'michael brown', 'emily davis', 'lucas santos',\n", + " 'laura janssen', 'mohammed ali', 'ana hernandez', 'carlos garcia',\n", + " 'lily wong', 'hans mueller', 'fatima khouri', 'james mackenzie',\n", + " 'michael chang', 'olivia rodriguez', 'kenji nakamura', 'emily lee',\n", + " 'james wilson', 'sofia russo', 'raj patel', 'lily nguyen',\n", + " 'david kim', 'maria garcia', 'alice smith', 'bob johnson',\n", + " 'charlie lee', 'emma davis', 'olivia martin', 'harry wilson',\n", + " 'sophia lee', 'james brown', 'mia johnson', 'william davis',\n", + " 'amelia brown', 'adam lee', 'sarah wong', 'maria silva',\n", + " 'peter brown', 'emma garcia', 'michael davis', 'nina patel',\n", + " 'kevin kim', 'laura van den berg', 'jennifer nguyen', 'rachel lee',\n", + " 'jessica wong', 'felipe almeida', 'nisa patel', 'ben smith',\n", + " 'laura gomez', 'park min woo', 'michael chen', 'sofia rossi',\n", + " 'rachel sanders', 'emily watson', 'ana rodriguez', 'tom wilson',\n", + " 'olivia green', 'james chen', 'lila patel', 'marco rossi',\n", + " 'sarah brown', 'sarah lee', 'alex kim', 'maria hernandez',\n", + " 'mark johnson', 'amanda chen', 'nana kwon', 'tom hanks',\n", + " 'emma watson', 'james kim', 'fatima ahmed', 'liam nguyen',\n", + " 'giulia rossi', 'putra wijaya', 'kim min ji', 'emily johnson',\n", + " 'michael wong', 'jessica chen', 'ken tanaka', 'rodrigo oliveira',\n", + " 'olivia kim', 'robert mueller', 'lisa chen', 'emily wong',\n", + " 'mark tan', 'emma lee', 'george chen', 'sophia kim', 'alex ng',\n", + " 'cindy chen', 'emily kim', 'frank li', 'gina lee', 'henry kim',\n", + " 'isabella chen', 'jack smith', 'katie johnson', 'john doe',\n", + " 'jane smith', 'michael johnson', 'jose perez', 'emma wilson',\n", + " 'ryan chen', 'sofia rodriguez', 'william brown'], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I will make sure that the column traveler_name is lowercased and stripped from double spaces or dashes, just in case this might hide more duplicates\n", + "# ideally, when you created travel_trip_christos_df, you already did:\n", + "# travel_trip_christos_df = original_df[...].copy()\n", + "\n", + "travel_trip_full_df.loc[:, \"traveler_name\"] = (\n", + " travel_trip_full_df[\"traveler_name\"]\n", + " .str.strip() # trims whitespace at both ends\n", + " .str.replace(\"-\", \" \", regex=False) # replace hyphen with space\n", + " .str.lower()\n", + ")\n", + "travel_trip_full_df.traveler_name.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "faefb471-4314-4019-a36a-1c0ae5e12e7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(28)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.traveler_name.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0a4df7e5-ef55-4140-86ad-b1e83f4fc0c6", + "metadata": {}, + "outputs": [], + "source": [ + "# we can see that there are no exact full string duplicates of the column traveler_name. \n", + "# The number 28 above probalby comes from partial dupication, like only first name or only last name" + ] + }, + { + "cell_type": "markdown", + "id": "3bea2d28-f076-4627-a75e-96263dfb0e66", + "metadata": {}, + "source": [ + "### More NaN values" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "22588870-2949-4e9d-96a9-27a4cc900653", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination 0\n", + "trip_id 0\n", + "destination_country 66\n", + "destination_city 0\n", + "start_date 0\n", + "end_date 0\n", + "duration_days 0\n", + "traveler_name 0\n", + "traveler_age 0\n", + "traveler_gender 0\n", + "traveler_nationality 0\n", + "accommodation_type 0\n", + "accommodation_cost 0\n", + "transportation_type 1\n", + "transportation_cost 1\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bc76fde3-b159-4020-b8db-a90445d30f42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
82Rome, Italy83ItalyRome4/15/20254/22/20257.0james kim41.0MaleAmericanHotel100NaNNaN
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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city start_date \\\n", + "82 Rome, Italy 83 Italy Rome 4/15/2025 \n", + "\n", + " end_date duration_days traveler_name traveler_age traveler_gender \\\n", + "82 4/22/2025 7.0 james kim 41.0 Male \n", + "\n", + " traveler_nationality accommodation_type accommodation_cost \\\n", + "82 American Hotel 100 \n", + "\n", + " transportation_type transportation_cost \n", + "82 NaN NaN " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's find out the NaN values in \n", + "mask = travel_trip_full_df[\"transportation_type\"].isna() & \\\n", + " travel_trip_full_df[\"transportation_cost\"].isna()\n", + "\n", + "travel_trip_full_df[mask]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "592eb5ac-8ca5-424f-8769-2eea320016d3", + "metadata": {}, + "outputs": [], + "source": [ + "# for transportation type and transportation_cost NaN values, we will group by destination_city and calculate the count of transportation_type and mean transportation_cost, and filter for Rome.\n", + "\n", + "# we will need to cast the data type of the column transportation_cost to float, before we can proceed with grouping.\n", + "\n", + "# for this to happen we will need to clean the column transportation_cost" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "522b02a3-4f4a-44f4-879e-1bd3c6c2ec6d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination object\n", + "trip_id int64\n", + "destination_country object\n", + "destination_city object\n", + "start_date object\n", + "end_date object\n", + "duration_days float64\n", + "traveler_name object\n", + "traveler_age float64\n", + "traveler_gender object\n", + "traveler_nationality object\n", + "accommodation_type object\n", + "accommodation_cost object\n", + "transportation_type object\n", + "transportation_cost object\n", + "dtype: object" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "bbfc9401-9153-403b-8f0f-77b3058e7a6c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['600', '500', '700', '1000', '200', '800', '1200', '100', '400',\n", + " '150', '$400 ', '$700 ', '$150 ', '$800 ', '$100 ', '$600 ',\n", + " '$80 ', '$500 ', '$300 ', '$50 ', '$120 ', '$75 ', '900', '50',\n", + " '$200 ', '$250 ', '$20 ', '300', '800 USD', '200 USD', '500 USD',\n", + " '700 USD', '300 USD', '600 USD', '400 USD', '1000 USD', nan,\n", + " '100 USD', '350 USD', '150 USD', '$1,200 ', '$900 ', '$1,500 ',\n", + " '$1,000 ', '250', '2500', '1500', '2000', '3000'], dtype=object)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.transportation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ffdead4b-a8cf-4bfe-a108-a488628630d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([600.0, 500.0, 700.0, 1000.0, 200.0, 800.0, 1200.0, 100.0, 400.0,\n", + " 150.0, 80.0, 300.0, 50.0, 120.0, 75.0, 900.0, 250.0, 20.0, nan,\n", + " 350.0, 1500.0, 2500.0, 2000.0, 3000.0], dtype=object)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# we will need to remove anything but the numbers\n", + "travel_trip_full_df.loc[:, \"transportation_cost\"] = (\n", + " travel_trip_full_df[\"transportation_cost\"]\n", + " .astype(str) # in case there are numbers/NaN in there\n", + " .str.strip()\n", + " .str.strip(\"$\")\n", + " .str.replace(\",\", \"\", regex=False)\n", + " .str.replace(\" USD\", \"\", regex=False)\n", + " .str.strip()\n", + ")\n", + "\n", + "travel_trip_full_df.loc[:, \"transportation_cost\"] = pd.to_numeric(\n", + " travel_trip_full_df[\"transportation_cost\"],\n", + " errors=\"coerce\"\n", + ")\n", + "\n", + "travel_trip_full_df.transportation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9c22669e-2e77-4ffa-915d-78790f7ddbe3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination object\n", + "trip_id int64\n", + "destination_country object\n", + "destination_city object\n", + "start_date object\n", + "end_date object\n", + "duration_days float64\n", + "traveler_name object\n", + "traveler_age float64\n", + "traveler_gender object\n", + "traveler_nationality object\n", + "accommodation_type object\n", + "accommodation_cost object\n", + "transportation_type object\n", + "transportation_cost object\n", + "dtype: object" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now we cast, in order to be able to group later\n", + "\n", + "travel_trip_full_df.loc[:, \"transportation_cost\"] = (\n", + " travel_trip_full_df[\"transportation_cost\"].astype(\"float64\")\n", + ")\n", + "\n", + "travel_trip_full_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "57a6080b-7d3c-42cd-9088-622054931a31", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_mean
destination_citytransportation_type
RomePlane2625.0
Train6405.0
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" + ], + "text/plain": [ + " transportation_type_count \\\n", + "destination_city transportation_type \n", + "Rome Plane 2 \n", + " Train 6 \n", + "\n", + " transportation_cost_mean \n", + "destination_city transportation_type \n", + "Rome Plane 625.0 \n", + " Train 405.0 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for transportation type and transportation_cost NaN values, we will group by destination_city and calculate the count of transportation_type and mean transportation_cost, and filter for Rome.\n", + "\n", + "grouped_df = (travel_trip_full_df.groupby([\"destination_city\", \"transportation_type\"]).agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_mean = (\"transportation_cost\", \"mean\")))\n", + "filtered_grouped_df = grouped_df.loc[[\"Rome\"]]\n", + "filtered_grouped_df" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8d80602c-b519-4c42-b88e-42baf5603a0e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# therefore, we will fill the empty values transportation_type with \"Train\", and transportation_cost with \"405.0\"\n", + "travel_trip_full_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_full_df[\"transportation_type\"].fillna(\"Train\")\n", + ")\n", + "\n", + "travel_trip_full_df.transportation_type.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "e14c95b1-fcbf-4de4-9525-6a4a9c6701d1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/13/s00136yd6q5bvv6x74_x_m204g4004/T/ipykernel_39449/1427628029.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " travel_trip_full_df[\"transportation_cost\"].fillna(405.0)\n" + ] + }, + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's replace the NaN, with the mean value.\n", + "travel_trip_full_df.loc[:, \"transportation_cost\"] = (\n", + " travel_trip_full_df[\"transportation_cost\"].fillna(405.0)\n", + ")\n", + "\n", + "travel_trip_full_df.transportation_cost.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "54d5abe2-8cea-48f7-baa1-f497b8768ec5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
0London, UK1UKLondon5/1/20235/8/20237.0john smith35.0MaleAmericanHotel1200Flight600.0
1Phuket, Thailand2ThailandPhuket6/15/20236/20/20235.0jane doe28.0FemaleCanadianResort800Flight500.0
2Bali, Indonesia3IndonesiaBali7/1/20237/8/20237.0david lee45.0MaleKoreanVilla1000Flight700.0
3New York, USA4USANew York8/15/20238/29/202314.0sarah johnson29.0FemaleBritishHotel2000Flight1000.0
4Tokyo, Japan5JapanTokyo9/10/20239/17/20237.0kim nguyen26.0FemaleVietnameseAirbnb700Train200.0
................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro8/1/20238/10/20239.0jose perez37.0MaleBrazilianHostel2500Car2000.0
135Vancouver, Canada136CanadaVancouver8/15/20238/21/20236.0emma wilson29.0FemaleCanadianHotel5000Airplane3000.0
136Bangkok, Thailand137ThailandBangkok9/1/20239/8/20237.0ryan chen34.0MaleChineseHostel2000Train1000.0
137Barcelona, Spain138SpainBarcelona9/15/20239/22/20237.0sofia rodriguez25.0FemaleSpanishAirbnb6000Airplane2500.0
138Auckland, New Zealand139New ZealandAuckland10/1/202310/8/20237.0william brown39.0MaleNew ZealanderHotel7000Train2500.0
\n", + "

137 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 5/1/2023 5/8/2023 7.0 john smith 35.0 \n", + "1 6/15/2023 6/20/2023 5.0 jane doe 28.0 \n", + "2 7/1/2023 7/8/2023 7.0 david lee 45.0 \n", + "3 8/15/2023 8/29/2023 14.0 sarah johnson 29.0 \n", + "4 9/10/2023 9/17/2023 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 8/1/2023 8/10/2023 9.0 jose perez 37.0 \n", + "135 8/15/2023 8/21/2023 6.0 emma wilson 29.0 \n", + "136 9/1/2023 9/8/2023 7.0 ryan chen 34.0 \n", + "137 9/15/2023 9/22/2023 7.0 sofia rodriguez 25.0 \n", + "138 10/1/2023 10/8/2023 7.0 william brown 39.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type \\\n", + "0 Male American Hotel \n", + "1 Female Canadian Resort \n", + "2 Male Korean Villa \n", + "3 Female British Hotel \n", + "4 Female Vietnamese Airbnb \n", + ".. ... ... ... \n", + "134 Male Brazilian Hostel \n", + "135 Female Canadian Hotel \n", + "136 Male Chinese Hostel \n", + "137 Female Spanish Airbnb \n", + "138 Male New Zealander Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600.0 \n", + "1 800 Flight 500.0 \n", + "2 1000 Flight 700.0 \n", + "3 2000 Flight 1000.0 \n", + "4 700 Train 200.0 \n", + ".. ... ... ... \n", + "134 2500 Car 2000.0 \n", + "135 5000 Airplane 3000.0 \n", + "136 2000 Train 1000.0 \n", + "137 6000 Airplane 2500.0 \n", + "138 7000 Train 2500.0 \n", + "\n", + "[137 rows x 15 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df" + ] + }, + { + "cell_type": "markdown", + "id": "57314993-3c5a-4f8e-8732-f8e58e416a42", + "metadata": {}, + "source": [ + "### Checking if we need to cluster vlues together" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3033d934-5386-4cdd-a826-34faf4a80c42", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Flight', 'Train', 'Plane', 'Bus', 'Car rental', 'Subway', 'Car',\n", + " 'Ferry', 'Airplane'], dtype=object)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's quickly check the transportation_types\n", + "\n", + "travel_trip_full_df.transportation_type.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "73ac01c2-5e07-4f23-8dcc-cd3ef04cdc1f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_mean
transportation_type
Subway120.0
Train38346.184211
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" + ], + "text/plain": [ + " transportation_type_count transportation_cost_mean\n", + "transportation_type \n", + "Subway 1 20.0\n", + "Train 38 346.184211" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# do we need to also change \"Subway\" to train? Maybe, who travels to a city using a subway, subways are intra city transportation modes.\n", + "subway_df = travel_trip_full_df.groupby(\"transportation_type\").agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_mean = (\"transportation_cost\", \"mean\"))\n", + "filtered_subway_df = subway_df.loc[[\"Subway\", \"Train\"]]\n", + "filtered_subway_df" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "582a8278-2dc1-4d57-9975-1da8396275bc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_min
transportation_type
Subway120.0
Train3880.0
\n", + "
" + ], + "text/plain": [ + " transportation_type_count transportation_cost_min\n", + "transportation_type \n", + "Subway 1 20.0\n", + "Train 38 80.0" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Subway seems to be an outlier data, but let's check at least if the transportation_cost of the \"Subway\" is at least close to the minimum cost for \"Train\"\n", + "subway_df = travel_trip_full_df.groupby(\"transportation_type\").agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_min = (\"transportation_cost\", \"min\"))\n", + "filtered_subway_min_df = subway_df.loc[[\"Subway\", \"Train\"]]\n", + "filtered_subway_min_df" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "078eb0ee-4daf-4279-a31a-379e862d108a", + "metadata": {}, + "outputs": [], + "source": [ + "# this is not the case, therefore, we will exclude it from our calculations of \"Train\"" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "91d2f8c0-d3b2-4082-9ab5-9dce3cd41437", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_mean
transportation_type
Car rental13296.153846
Car31433.333333
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" + ], + "text/plain": [ + " transportation_type_count transportation_cost_mean\n", + "transportation_type \n", + "Car rental 13 296.153846\n", + "Car 3 1433.333333" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# and \"Car\" and \"Car rental\" under \"Car\"?\n", + "car_df = travel_trip_full_df.groupby(\"transportation_type\").agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_mean = (\"transportation_cost\", \"mean\"))\n", + "filtered_car_df = car_df.loc[[\"Car rental\", \"Car\"]]\n", + "filtered_car_df" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "8ddffc45-7e45-4c73-93c9-c9a5b161f4de", + "metadata": {}, + "outputs": [], + "source": [ + "# no, we see that there is a massive difference in the mean cost between the two categories, suggesting that it is indeed two different transportation modes. \n", + "# Car rental suggests that someone rented a car at the destnation, where car probably indicates that someone did a road trip using a car to destination.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f88afc99-4462-4759-b430-15dc97b1c569", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Flight', 'Train', 'Airplane', 'Bus', 'Rental Car', 'Subway',\n", + " 'Private Car', 'Ferry'], dtype=object)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ok, we will merge \"Plane\" and \"Airplane\" under \"Airplane\", and \"Car rental\" to \"Rental Car\", and \"Car\" to \"Private Car\"\n", + "travel_trip_full_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_full_df[\"transportation_type\"]\n", + " .replace({\"Plane\": \"Airplane\"})\n", + ")\n", + "\n", + "travel_trip_full_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_full_df[\"transportation_type\"]\n", + " .replace({\"Car rental\": \"Rental Car\"})\n", + ")\n", + "\n", + "travel_trip_full_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_full_df[\"transportation_type\"]\n", + " .replace({\"Car\": \"Private Car\"})\n", + ")\n", + "\n", + "travel_trip_full_df[\"transportation_type\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "3fd9e24c-9b63-47eb-b555-c60e60b5a55f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
123Tokyo, Japan124JapanTokyo10/5/202210/13/20228.0henry kim24.0MaleKoreanHotel1200Airplane700.0
124Sydney, Aus125AusSydney11/11/202211/21/202210.0isabella chen30.0FemaleChineseAirbnb900Airplane1000.0
125Paris, France126FranceParis12/24/20221/1/20238.0jack smith28.0MaleAmericanHostel400Airplane700.0
126Bali, Indonesia127IndonesiaBali2/10/20232/18/20238.0katie johnson33.0FemaleCanadianHotel800Airplane800.0
128Paris, France129FranceParis5/1/20235/7/20236.0john doe35.0MaleAmericanHotel5000Airplane2500.0
129Tokyo, Japan130JapanTokyo5/15/20235/22/20237.0jane smith28.0FemaleBritishAirbnb7000Train1500.0
130Cape Town, South Africa131South AfricaCape Town6/1/20236/10/20239.0michael johnson45.0MaleSouth AfricanHostel3000Private Car2000.0
131Sydney, Australia132AustraliaSydney6/15/20236/21/20236.0sarah lee31.0FemaleAustralianHotel6000Airplane3000.0
132Rome, Italy133ItalyRome7/1/20237/8/20237.0david kim42.0MaleKoreanAirbnb4000Train1500.0
133New York City, USA134USANew York City7/15/20237/22/20237.0emily davis27.0FemaleAmericanHotel8000Airplane2500.0
134Rio de Janeiro, Brazil135BrazilRio de Janeiro8/1/20238/10/20239.0jose perez37.0MaleBrazilianHostel2500Private Car2000.0
135Vancouver, Canada136CanadaVancouver8/15/20238/21/20236.0emma wilson29.0FemaleCanadianHotel5000Airplane3000.0
136Bangkok, Thailand137ThailandBangkok9/1/20239/8/20237.0ryan chen34.0MaleChineseHostel2000Train1000.0
137Barcelona, Spain138SpainBarcelona9/15/20239/22/20237.0sofia rodriguez25.0FemaleSpanishAirbnb6000Airplane2500.0
138Auckland, New Zealand139New ZealandAuckland10/1/202310/8/20237.0william brown39.0MaleNew ZealanderHotel7000Train2500.0
\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "123 Tokyo, Japan 124 Japan Tokyo \n", + "124 Sydney, Aus 125 Aus Sydney \n", + "125 Paris, France 126 France Paris \n", + "126 Bali, Indonesia 127 Indonesia Bali \n", + "128 Paris, France 129 France Paris \n", + "129 Tokyo, Japan 130 Japan Tokyo \n", + "130 Cape Town, South Africa 131 South Africa Cape Town \n", + "131 Sydney, Australia 132 Australia Sydney \n", + "132 Rome, Italy 133 Italy Rome \n", + "133 New York City, USA 134 USA New York City \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "123 10/5/2022 10/13/2022 8.0 henry kim 24.0 \n", + "124 11/11/2022 11/21/2022 10.0 isabella chen 30.0 \n", + "125 12/24/2022 1/1/2023 8.0 jack smith 28.0 \n", + "126 2/10/2023 2/18/2023 8.0 katie johnson 33.0 \n", + "128 5/1/2023 5/7/2023 6.0 john doe 35.0 \n", + "129 5/15/2023 5/22/2023 7.0 jane smith 28.0 \n", + "130 6/1/2023 6/10/2023 9.0 michael johnson 45.0 \n", + "131 6/15/2023 6/21/2023 6.0 sarah lee 31.0 \n", + "132 7/1/2023 7/8/2023 7.0 david kim 42.0 \n", + "133 7/15/2023 7/22/2023 7.0 emily davis 27.0 \n", + "134 8/1/2023 8/10/2023 9.0 jose perez 37.0 \n", + "135 8/15/2023 8/21/2023 6.0 emma wilson 29.0 \n", + "136 9/1/2023 9/8/2023 7.0 ryan chen 34.0 \n", + "137 9/15/2023 9/22/2023 7.0 sofia rodriguez 25.0 \n", + "138 10/1/2023 10/8/2023 7.0 william brown 39.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type \\\n", + "123 Male Korean Hotel \n", + "124 Female Chinese Airbnb \n", + "125 Male American Hostel \n", + "126 Female Canadian Hotel \n", + "128 Male American Hotel \n", + "129 Female British Airbnb \n", + "130 Male South African Hostel \n", + "131 Female Australian Hotel \n", + "132 Male Korean Airbnb \n", + "133 Female American Hotel \n", + "134 Male Brazilian Hostel \n", + "135 Female Canadian Hotel \n", + "136 Male Chinese Hostel \n", + "137 Female Spanish Airbnb \n", + "138 Male New Zealander Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "123 1200 Airplane 700.0 \n", + "124 900 Airplane 1000.0 \n", + "125 400 Airplane 700.0 \n", + "126 800 Airplane 800.0 \n", + "128 5000 Airplane 2500.0 \n", + "129 7000 Train 1500.0 \n", + "130 3000 Private Car 2000.0 \n", + "131 6000 Airplane 3000.0 \n", + "132 4000 Train 1500.0 \n", + "133 8000 Airplane 2500.0 \n", + "134 2500 Private Car 2000.0 \n", + "135 5000 Airplane 3000.0 \n", + "136 2000 Train 1000.0 \n", + "137 6000 Airplane 2500.0 \n", + "138 7000 Train 2500.0 " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_df.tail(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "7cc68b39-819a-4158-a71e-7a52ae25ba86", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
0London, UK1UKLondon5/1/20235/8/20237.0john smith35.0MaleAmericanHotel1200Flight600.0
1Phuket, Thailand2ThailandPhuket6/15/20236/20/20235.0jane doe28.0FemaleCanadianResort800Flight500.0
2Bali, Indonesia3IndonesiaBali7/1/20237/8/20237.0david lee45.0MaleKoreanVilla1000Flight700.0
3New York, USA4USANew York8/15/20238/29/202314.0sarah johnson29.0FemaleBritishHotel2000Flight1000.0
4Tokyo, Japan5JapanTokyo9/10/20239/17/20237.0kim nguyen26.0FemaleVietnameseAirbnb700Train200.0
................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro8/1/20238/10/20239.0jose perez37.0MaleBrazilianHostel2500Private Car2000.0
135Vancouver, Canada136CanadaVancouver8/15/20238/21/20236.0emma wilson29.0FemaleCanadianHotel5000Airplane3000.0
136Bangkok, Thailand137ThailandBangkok9/1/20239/8/20237.0ryan chen34.0MaleChineseHostel2000Train1000.0
137Barcelona, Spain138SpainBarcelona9/15/20239/22/20237.0sofia rodriguez25.0FemaleSpanishAirbnb6000Airplane2500.0
138Auckland, New Zealand139New ZealandAuckland10/1/202310/8/20237.0william brown39.0MaleNew ZealanderHotel7000Train2500.0
\n", + "

137 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 5/1/2023 5/8/2023 7.0 john smith 35.0 \n", + "1 6/15/2023 6/20/2023 5.0 jane doe 28.0 \n", + "2 7/1/2023 7/8/2023 7.0 david lee 45.0 \n", + "3 8/15/2023 8/29/2023 14.0 sarah johnson 29.0 \n", + "4 9/10/2023 9/17/2023 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 8/1/2023 8/10/2023 9.0 jose perez 37.0 \n", + "135 8/15/2023 8/21/2023 6.0 emma wilson 29.0 \n", + "136 9/1/2023 9/8/2023 7.0 ryan chen 34.0 \n", + "137 9/15/2023 9/22/2023 7.0 sofia rodriguez 25.0 \n", + "138 10/1/2023 10/8/2023 7.0 william brown 39.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type \\\n", + "0 Male American Hotel \n", + "1 Female Canadian Resort \n", + "2 Male Korean Villa \n", + "3 Female British Hotel \n", + "4 Female Vietnamese Airbnb \n", + ".. ... ... ... \n", + "134 Male Brazilian Hostel \n", + "135 Female Canadian Hotel \n", + "136 Male Chinese Hostel \n", + "137 Female Spanish Airbnb \n", + "138 Male New Zealander Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600.0 \n", + "1 800 Flight 500.0 \n", + "2 1000 Flight 700.0 \n", + "3 2000 Flight 1000.0 \n", + "4 700 Train 200.0 \n", + ".. ... ... ... \n", + "134 2500 Private Car 2000.0 \n", + "135 5000 Airplane 3000.0 \n", + "136 2000 Train 1000.0 \n", + "137 6000 Airplane 2500.0 \n", + "138 7000 Train 2500.0 \n", + "\n", + "[137 rows x 15 columns]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_2_df = travel_trip_full_df.copy()\n", + "travel_trip_full_2_df" + ] + }, + { + "cell_type": "markdown", + "id": "e64d0c06-9952-45c9-b068-416cb9903e81", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## Safina code" + ] + }, + { + "cell_type": "markdown", + "id": "37ec4caa-97fb-431b-8752-25933ddad108", + "metadata": {}, + "source": [ + "### Cleaning end dates" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "2a1b4394-8569-4ae3-97ee-9ad35a1b4b9a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All steps completed successfully with the updated file path.\n", + "DataFrame variable name: travel_trip_full_df\n", + "Data type of 'end_date': object\n", + "\n", + "First 5 rows of the converted 'end_date' column:\n", + " end_date\n", + "0 2023-05-08\n", + "1 2023-06-20\n", + "2 2023-07-08\n", + "3 2023-08-29\n", + "4 2023-09-17\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_full_2_df[\"end_date\"] = pd.to_datetime(\n", + " travel_trip_full_2_df[\"end_date\"],\n", + " errors=\"coerce\"\n", + ")\n", + "travel_trip_full_2_df[\"end_date\"] = travel_trip_full_2_df[\"end_date\"].dt.strftime(\"%Y-%m-%d\")\n", + "\n", + "print(\"All steps completed successfully with the updated file path.\")\n", + "print(f\"DataFrame variable name: travel_trip_full_df\")\n", + "print(f\"Data type of 'end_date': {travel_trip_full_2_df['end_date'].dtype}\")\n", + "print(\"\\nFirst 5 rows of the converted 'end_date' column:\")\n", + "print(travel_trip_full_2_df[[\"end_date\"]].head())\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "9536b058-9e4a-4bf2-be90-5f0e1e6362c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2023-05\n", + "1 2023-06\n", + "2 2023-07\n", + "3 2023-08\n", + "4 2023-09\n", + " ... \n", + "134 2023-08\n", + "135 2023-08\n", + "136 2023-09\n", + "137 2023-09\n", + "138 2023-10\n", + "Name: end_date, Length: 137, dtype: object\n" + ] + } + ], + "source": [ + "# Assuming travel_trip_safina_df is loaded and 'end_date' is currently datetime (or has been converted in the steps above).\n", + "\n", + "# Convert the 'end_date' column to datetime first (if not already done)\n", + "travel_trip_full_2_df['end_date'] = pd.to_datetime(\n", + " travel_trip_full_2_df['end_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Use .dt.strftime('%Y-%m') to format the date to Year and Month only.\n", + "travel_trip_full_2_df['end_date'] = travel_trip_full_2_df['end_date'].dt.strftime('%Y-%m')\n", + "\n", + "# Optional: Display the new data type and values\n", + "print(travel_trip_full_2_df['end_date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "366bc3e2-1ebd-46ea-aad6-3fd61099a833", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data loading and feature engineering complete.\n", + "\n", + "First 5 rows of all date columns:\n", + " start_date start_year start_month end_date end_year end_month\n", + "0 2023-05-01 2023 5 2023-05-01 2023 5\n", + "1 2023-06-15 2023 6 2023-06-01 2023 6\n", + "2 2023-07-01 2023 7 2023-07-01 2023 7\n", + "3 2023-08-15 2023 8 2023-08-01 2023 8\n", + "4 2023-09-10 2023 9 2023-09-01 2023 9\n" + ] + } + ], + "source": [ + "import pandas as pd \n", + "\n", + "# 4. CLEAN AND EXTRACT DATE COMPONENTS\n", + "\n", + "# --- Process 'end_date' column ---\n", + "# Convert to a clean datetime object\n", + "travel_trip_full_2_df['end_date'] = pd.to_datetime(\n", + " travel_trip_full_2_df['end_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Create the 'end_year' and 'end_month' integer columns\n", + "travel_trip_full_2_df['end_year'] = travel_trip_full_2_df['end_date'].dt.year.astype('Int64')\n", + "travel_trip_full_2_df['end_month'] = travel_trip_full_2_df['end_date'].dt.month.astype('Int64')\n", + "\n", + "\n", + "# --- Process 'start_date' column ---\n", + "# Convert to a clean datetime object\n", + "travel_trip_full_2_df['start_date'] = pd.to_datetime(\n", + " travel_trip_full_2_df['start_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Create the 'start_year' and 'start_month' integer columns\n", + "travel_trip_full_2_df['start_year'] = travel_trip_full_2_df['start_date'].dt.year.astype('Int64')\n", + "travel_trip_full_2_df['start_month'] = travel_trip_full_2_df['start_date'].dt.month.astype('Int64')\n", + "\n", + "\n", + "# 5. DISPLAY VERIFICATION (Optional)\n", + "print(\"Data loading and feature engineering complete.\")\n", + "print(\"\\nFirst 5 rows of all date columns:\")\n", + "print(travel_trip_full_2_df[['start_date', 'start_year', 'start_month', \n", + " 'end_date', 'end_year', 'end_month']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "6f832cc1-4ff3-473b-b754-64872d2c261e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " start_month start_month_name end_month end_month_name\n", + "0 5 May 5 May\n", + "1 6 June 6 June\n", + "2 7 July 7 July\n", + "3 8 August 8 August\n", + "4 9 September 9 September\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. Define the month mapping dictionary\n", + "month_map = {\n", + " 1: 'January', 2: 'February', 3: 'March', 4: 'April', \n", + " 5: 'May', 6: 'June', 7: 'July', 8: 'August', \n", + " 9: 'September', 10: 'October', 11: 'November', 12: 'December'\n", + "}\n", + "\n", + "# 2. Create 'start_month_name' by mapping the integer month to its name\n", + "travel_trip_full_2_df['start_month_name'] = travel_trip_full_2_df['start_month'].map(month_map)\n", + "\n", + "# 3. Create 'end_month_name' by mapping the integer month to its name\n", + "travel_trip_full_2_df['end_month_name'] = travel_trip_full_2_df['end_month'].map(month_map)\n", + "\n", + "# Optional: Display the first few rows for verification\n", + "print(travel_trip_full_2_df[['start_month', 'start_month_name', 'end_month', 'end_month_name']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "910ebca3-9ace-404c-9682-7c683bb790f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " start_year start_month_name end_year end_month_name\n", + "0 2023 May 2023 May\n", + "1 2023 June 2023 June\n", + "2 2023 July 2023 July\n", + "3 2023 August 2023 August\n", + "4 2023 September 2023 September\n" + ] + } + ], + "source": [ + "print(travel_trip_full_2_df[['start_year', 'start_month_name', 'end_year', 'end_month_name']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "de7b48de-ec77-43b6-9c0f-b5c33dafd29b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_typeaccommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_name
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...Hotel1200Flight600.02023520235MayMay
1Phuket, Thailand2ThailandPhuket2023-06-152023-06-015.0jane doe28.0Female...Resort800Flight500.02023620236JuneJune
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...Villa1000Flight700.02023720237JulyJuly
3New York, USA4USANew York2023-08-152023-08-0114.0sarah johnson29.0Female...Hotel2000Flight1000.02023820238AugustAugust
4Tokyo, Japan5JapanTokyo2023-09-102023-09-017.0kim nguyen26.0Female...Airbnb700Train200.02023920239SeptemberSeptember
..................................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...Hostel2500Private Car2000.02023820238AugustAugust
135Vancouver, Canada136CanadaVancouver2023-08-152023-08-016.0emma wilson29.0Female...Hotel5000Airplane3000.02023820238AugustAugust
136Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...Hostel2000Train1000.02023920239SeptemberSeptember
137Barcelona, Spain138SpainBarcelona2023-09-152023-09-017.0sofia rodriguez25.0Female...Airbnb6000Airplane2500.02023920239SeptemberSeptember
138Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...Hotel7000Train2500.0202310202310OctoberOctober
\n", + "

137 rows × 21 columns

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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-15 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-15 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-10 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "135 2023-08-15 2023-08-01 6.0 emma wilson 29.0 \n", + "136 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "137 2023-09-15 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "138 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_type accommodation_cost \\\n", + "0 Male ... Hotel 1200 \n", + "1 Female ... Resort 800 \n", + "2 Male ... Villa 1000 \n", + "3 Female ... Hotel 2000 \n", + "4 Female ... Airbnb 700 \n", + ".. ... ... ... ... \n", + "134 Male ... Hostel 2500 \n", + "135 Female ... Hotel 5000 \n", + "136 Male ... Hostel 2000 \n", + "137 Female ... Airbnb 6000 \n", + "138 Male ... Hotel 7000 \n", + "\n", + " transportation_type transportation_cost end_year end_month start_year \\\n", + "0 Flight 600.0 2023 5 2023 \n", + "1 Flight 500.0 2023 6 2023 \n", + "2 Flight 700.0 2023 7 2023 \n", + "3 Flight 1000.0 2023 8 2023 \n", + "4 Train 200.0 2023 9 2023 \n", + ".. ... ... ... ... ... \n", + "134 Private Car 2000.0 2023 8 2023 \n", + "135 Airplane 3000.0 2023 8 2023 \n", + "136 Train 1000.0 2023 9 2023 \n", + "137 Airplane 2500.0 2023 9 2023 \n", + "138 Train 2500.0 2023 10 2023 \n", + "\n", + " start_month start_month_name end_month_name \n", + "0 5 May May \n", + "1 6 June June \n", + "2 7 July July \n", + "3 8 August August \n", + "4 9 September September \n", + ".. ... ... ... \n", + "134 8 August August \n", + "135 8 August August \n", + "136 9 September September \n", + "137 9 September September \n", + "138 10 October October \n", + "\n", + "[137 rows x 21 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_full_2_df" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "13a57d3d-5679-40b5-bcde-1bd284b0e262", + "metadata": {}, + "outputs": [], + "source": [ + "travel_trip_dull_3_df = travel_trip_full_2_df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "983bd27a-3888-4608-96f7-d8061d75e7ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_typeaccommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_name
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...Hotel1200Flight600.02023520235MayMay
1Phuket, Thailand2ThailandPhuket2023-06-152023-06-015.0jane doe28.0Female...Resort800Flight500.02023620236JuneJune
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...Villa1000Flight700.02023720237JulyJuly
3New York, USA4USANew York2023-08-152023-08-0114.0sarah johnson29.0Female...Hotel2000Flight1000.02023820238AugustAugust
4Tokyo, Japan5JapanTokyo2023-09-102023-09-017.0kim nguyen26.0Female...Airbnb700Train200.02023920239SeptemberSeptember
..................................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...Hostel2500Private Car2000.02023820238AugustAugust
135Vancouver, Canada136CanadaVancouver2023-08-152023-08-016.0emma wilson29.0Female...Hotel5000Airplane3000.02023820238AugustAugust
136Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...Hostel2000Train1000.02023920239SeptemberSeptember
137Barcelona, Spain138SpainBarcelona2023-09-152023-09-017.0sofia rodriguez25.0Female...Airbnb6000Airplane2500.02023920239SeptemberSeptember
138Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...Hotel7000Train2500.0202310202310OctoberOctober
\n", + "

137 rows × 21 columns

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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-15 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-15 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-10 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "135 2023-08-15 2023-08-01 6.0 emma wilson 29.0 \n", + "136 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "137 2023-09-15 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "138 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_type accommodation_cost \\\n", + "0 Male ... Hotel 1200 \n", + "1 Female ... Resort 800 \n", + "2 Male ... Villa 1000 \n", + "3 Female ... Hotel 2000 \n", + "4 Female ... Airbnb 700 \n", + ".. ... ... ... ... \n", + "134 Male ... Hostel 2500 \n", + "135 Female ... Hotel 5000 \n", + "136 Male ... Hostel 2000 \n", + "137 Female ... Airbnb 6000 \n", + "138 Male ... Hotel 7000 \n", + "\n", + " transportation_type transportation_cost end_year end_month start_year \\\n", + "0 Flight 600.0 2023 5 2023 \n", + "1 Flight 500.0 2023 6 2023 \n", + "2 Flight 700.0 2023 7 2023 \n", + "3 Flight 1000.0 2023 8 2023 \n", + "4 Train 200.0 2023 9 2023 \n", + ".. ... ... ... ... ... \n", + "134 Private Car 2000.0 2023 8 2023 \n", + "135 Airplane 3000.0 2023 8 2023 \n", + "136 Train 1000.0 2023 9 2023 \n", + "137 Airplane 2500.0 2023 9 2023 \n", + "138 Train 2500.0 2023 10 2023 \n", + "\n", + " start_month start_month_name end_month_name \n", + "0 5 May May \n", + "1 6 June June \n", + "2 7 July July \n", + "3 8 August August \n", + "4 9 September September \n", + ".. ... ... ... \n", + "134 8 August August \n", + "135 8 August August \n", + "136 9 September September \n", + "137 9 September September \n", + "138 10 October October \n", + "\n", + "[137 rows x 21 columns]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df" + ] + }, + { + "cell_type": "markdown", + "id": "d269ab47-6761-4245-a4b8-83dc6cb9f8a4", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## Hoaithuong code" + ] + }, + { + "cell_type": "markdown", + "id": "814e1d2c-8869-43b8-af88-72298ae127bb", + "metadata": {}, + "source": [ + "### Cleaning starting date" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "538c4178-b5a4-4d74-82b0-354ebce597c7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "['2023-05-01 00:00:00', '2023-06-15 00:00:00', '2023-07-01 00:00:00',\n", + " '2023-08-15 00:00:00', '2023-09-10 00:00:00', '2023-10-05 00:00:00',\n", + " '2023-11-20 00:00:00', '2024-01-05 00:00:00', '2024-02-14 00:00:00',\n", + " '2024-03-10 00:00:00',\n", + " ...\n", + " '2022-08-08 00:00:00', '2022-09-20 00:00:00', '2022-10-05 00:00:00',\n", + " '2022-11-11 00:00:00', '2022-12-24 00:00:00', '2023-02-10 00:00:00',\n", + " '2023-05-15 00:00:00', '2023-06-01 00:00:00', '2023-07-15 00:00:00',\n", + " '2023-10-01 00:00:00']\n", + "Length: 111, dtype: datetime64[ns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['start_date'].unique()\n", + "#extract the list of unique values" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "628a1d1b-c931-470a-8952-c4e3b69d4f64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_typeaccommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_name
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0 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [destination, trip_id, destination_country, destination_city, start_date, end_date, duration_days, traveler_name, traveler_age, traveler_gender, traveler_nationality, accommodation_type, accommodation_cost, transportation_type, transportation_cost, end_year, end_month, start_year, start_month, start_month_name, end_month_name]\n", + "Index: []\n", + "\n", + "[0 rows x 21 columns]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df[travel_trip_dull_3_df['start_date'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "c9e2afab-2a3a-4fa2-998f-cdf4a69df80c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df[\"start_date\"].isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "780d1f7f-0667-48c4-90ed-4a7ed1853719", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df[\"start_date\"].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "7485f668-0bb0-4a8e-92ce-69a5d90f5553", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2023-05-01\n", + "1 2023-06-15\n", + "2 2023-07-01\n", + "3 2023-08-15\n", + "4 2023-09-10\n", + " ... \n", + "134 2023-08-01\n", + "135 2023-08-15\n", + "136 2023-09-01\n", + "137 2023-09-15\n", + "138 2023-10-01\n", + "Name: start_date, Length: 137, dtype: datetime64[ns]\n" + ] + } + ], + "source": [ + "travel_trip_dull_3_df['start_date'] = pd.to_datetime(travel_trip_dull_3_df['start_date'], format='%m/%d/%Y')\n", + "print(travel_trip_dull_3_df['start_date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "807deb28-e6c6-43d7-b186-56149f7de432", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " start_date\n", + "0 2023-05\n", + "1 2023-06\n", + "2 2023-07\n", + "3 2023-08\n", + "4 2023-09\n" + ] + } + ], + "source": [ + "# Convert the 'end_date' column to datetime first (if not already done)\n", + "travel_trip_dull_3_df['start_date'] = pd.to_datetime(\n", + " travel_trip_dull_3_df['start_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Use .dt.strftime('%Y-%m') to format the date to Year and Month only.\n", + "travel_trip_dull_3_df['start_date'] = travel_trip_dull_3_df['start_date'].dt.strftime('%Y-%m')\n", + "\n", + "# Optional: Display the new data type and values\n", + "print(travel_trip_dull_3_df[['start_date']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "2e47bcfc-add0-493f-9a65-8bf9d8286e05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2023-05-01\n", + "1 2023-06-01\n", + "2 2023-07-01\n", + "3 2023-08-01\n", + "4 2023-09-01\n", + " ... \n", + "134 2023-08-01\n", + "135 2023-08-01\n", + "136 2023-09-01\n", + "137 2023-09-01\n", + "138 2023-10-01\n", + "Name: start_date, Length: 137, dtype: datetime64[ns]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['start_date'] = pd.to_datetime(travel_trip_dull_3_df['start_date'], errors='coerce')\n", + "travel_trip_dull_3_df['start_date']" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "c6b6912b-e1d7-44b8-bfd3-9c1970130824", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 May\n", + "1 June\n", + "2 July\n", + "3 August\n", + "4 September\n", + " ... \n", + "134 August\n", + "135 August\n", + "136 September\n", + "137 September\n", + "138 October\n", + "Name: start_month, Length: 137, dtype: object" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['start_month'] = travel_trip_dull_3_df['start_date'].dt.strftime('%B')\n", + "travel_trip_dull_3_df['start_month']" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "2f617b44-ca2d-4777-86c5-914acc4fcf04", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_typeaccommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_name
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...Hotel1200Flight600.02023520235MayMay
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...Resort800Flight500.02023620236JuneJune
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...Villa1000Flight700.02023720237JulyJuly
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...Hotel2000Flight1000.02023820238AugustAugust
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...Airbnb700Train200.02023920239SeptemberSeptember
\n", + "

5 rows × 21 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city start_date \\\n", + "0 London, UK 1 UK London 2023-05-01 \n", + "1 Phuket, Thailand 2 Thailand Phuket 2023-06-01 \n", + "2 Bali, Indonesia 3 Indonesia Bali 2023-07-01 \n", + "3 New York, USA 4 USA New York 2023-08-01 \n", + "4 Tokyo, Japan 5 Japan Tokyo 2023-09-01 \n", + "\n", + " end_date duration_days traveler_name traveler_age traveler_gender ... \\\n", + "0 2023-05-01 7.0 john smith 35.0 Male ... \n", + "1 2023-06-01 5.0 jane doe 28.0 Female ... \n", + "2 2023-07-01 7.0 david lee 45.0 Male ... \n", + "3 2023-08-01 14.0 sarah johnson 29.0 Female ... \n", + "4 2023-09-01 7.0 kim nguyen 26.0 Female ... \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600.0 2023 5 2023 5 \n", + "1 500.0 2023 6 2023 6 \n", + "2 700.0 2023 7 2023 7 \n", + "3 1000.0 2023 8 2023 8 \n", + "4 200.0 2023 9 2023 9 \n", + "\n", + " start_month_name end_month_name \n", + "0 May May \n", + "1 June June \n", + "2 July July \n", + "3 August August \n", + "4 September September \n", + "\n", + "[5 rows x 21 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df[\"start_date\"] = pd.to_datetime(\n", + " travel_trip_dull_3_df[\"start_date\"], errors=\"coerce\"\n", + ")\n", + "\n", + "travel_trip_dull_3_df[\"start_year\"] = (\n", + " travel_trip_dull_3_df[\"start_date\"].dt.year.astype(\"Int64\")\n", + ")\n", + "travel_trip_dull_3_df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b3767468-98c9-4095-b46c-87215ec65de9", + "metadata": {}, + "source": [ + "### Clean columns: traveler_nationality" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "37f0b6f1-cf41-42de-b213-54ef2386acb8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 American\n", + "1 Canadian\n", + "2 Korean\n", + "3 British\n", + "4 Vietnamese\n", + " ... \n", + "134 Brazilian\n", + "135 Canadian\n", + "136 Chinese\n", + "137 Spanish\n", + "138 New Zealander\n", + "Name: traveler_nationality, Length: 137, dtype: object" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_nationality'].str.strip().str.lower().str.title()" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "8314ad7f-835e-4bd5-8125-df9dc02c706a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_nationality\n", + "American 24\n", + "Korean 13\n", + "British 12\n", + "Canadian 9\n", + "Australian 8\n", + "Chinese 7\n", + "Spanish 7\n", + "Brazilian 4\n", + "Italian 4\n", + "Indian 4\n", + "South Korean 3\n", + "Vietnamese 3\n", + "South Korea 3\n", + "Emirati 2\n", + "Dutch 2\n", + "Japanese 2\n", + "South African 2\n", + "USA 2\n", + "Canada 2\n", + "Taiwan 2\n", + "Mexican 2\n", + "Moroccan 1\n", + "French 1\n", + "German 1\n", + "Scottish 1\n", + "Taiwanese 1\n", + "Indonesian 1\n", + "UK 1\n", + "China 1\n", + "Japan 1\n", + "Spain 1\n", + "Brazil 1\n", + "Germany 1\n", + "Hong Kong 1\n", + "United Kingdom 1\n", + "Singapore 1\n", + "Italy 1\n", + "Greece 1\n", + "United Arab Emirates 1\n", + "Cambodia 1\n", + "New Zealander 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_nationality'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "39ad6a27-1624-49d0-9965-6fec73757d6c", + "metadata": {}, + "outputs": [], + "source": [ + "mapping = {\n", + " 'American': 'United States',\n", + " 'USA': 'United States',\n", + " 'Canada': 'Canada',\n", + " 'Canadian': 'Canada',\n", + " 'UK': 'United Kingdom',\n", + " 'United Kingdom': 'United Kingdom',\n", + " 'British': 'United Kingdom',\n", + " 'Scottish': 'United Kingdom',\n", + " 'Korean': 'South Korea',\n", + " 'South Korea': 'South Korea',\n", + " 'South Korean': 'South Korea',\n", + " 'China': 'China',\n", + " 'Chinese': 'China',\n", + " 'Italy': 'Italy',\n", + " 'Italian': 'Italy',\n", + " 'Brazil': 'Brazil',\n", + " 'Brazilian': 'Brazil',\n", + " 'Emirati': 'United Arab Emirates',\n", + " 'United Arab Emirates': 'United Arab Emirates',\n", + " 'Taiwan': 'Taiwan',\n", + " 'Taiwanese': 'Taiwan',\n", + " 'Germany': 'Germany',\n", + " 'German': 'Germany',\n", + " 'Spain': 'Spain',\n", + " 'Spanish': 'Spain',\n", + " 'Japan': 'Japan',\n", + " 'Japanese': 'Japan'\n", + "}\n", + "\n", + "travel_trip_dull_3_df['traveler_nationality_clean'] = travel_trip_dull_3_df['traveler_nationality'].replace(mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "1d9972ce-e28c-4f56-b0cb-b806594636ba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_nationality_clean\n", + "United States 26\n", + "South Korea 19\n", + "United Kingdom 15\n", + "Canada 11\n", + "Australian 8\n", + "Spain 8\n", + "China 8\n", + "Italy 5\n", + "Brazil 5\n", + "Indian 4\n", + "Japan 3\n", + "Taiwan 3\n", + "United Arab Emirates 3\n", + "Vietnamese 3\n", + "Mexican 2\n", + "Dutch 2\n", + "South African 2\n", + "Germany 2\n", + "French 1\n", + "Moroccan 1\n", + "Indonesian 1\n", + "Hong Kong 1\n", + "Singapore 1\n", + "Greece 1\n", + "Cambodia 1\n", + "New Zealander 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_nationality_clean'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "06e6092e-21a3-45f6-8c00-292bd3b0f352", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('O')" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_nationality'].dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "b782b44a-dbfb-4c5d-8903-ce7ed859a5ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 American\n", + "1 Canadian\n", + "2 Korean\n", + "3 British\n", + "4 Vietnamese\n", + " ... \n", + "134 Brazilian\n", + "135 Canadian\n", + "136 Chinese\n", + "137 Spanish\n", + "138 New Zealander\n", + "Name: traveler_nationality, Length: 137, dtype: string" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_nationality'].astype('string')" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "ebaa4e37-b7d1-4b35-a81f-24cd4956e3dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_nationality'].isna().sum()" + ] + }, + { + "cell_type": "markdown", + "id": "e38d5847-acf6-446e-9aa9-79cd83b4f30a", + "metadata": {}, + "source": [ + "### Cleaning the column traveler_gender" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "2401fcec-ff6e-4460-b1cc-6f9660f23d8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Male', 'Female'], dtype=object)" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_gender'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "59ca96d7-ee1d-44a9-b959-851537d6774b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_gender\n", + "Female 70\n", + "Male 67\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['traveler_gender'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "9f5e91f9-0d63-4fe8-87d3-13bdfca06760", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_gender\n", + "Female 70\n", + "Male 67\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df[\"traveler_gender\"] = (\n", + " travel_trip_dull_3_df[\"traveler_gender\"].fillna(\"Unknown\")\n", + ")\n", + "travel_trip_dull_3_df['traveler_gender'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "16ddaf3b-97b7-481a-b588-e06e7b21d5ab", + "metadata": {}, + "source": [ + "### Clean columns accommodation_type" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "3c509d63-cfc7-441f-b40e-583f4b589d10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Hotel', 'Resort', 'Villa', 'Airbnb', 'Hostel', 'Riad',\n", + " 'Vacation rental', 'Guesthouse'], dtype=object)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['accommodation_type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "1bb25acf-4e48-4abf-b820-842d3ab42f33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "accommodation_type\n", + "Hotel 60\n", + "Airbnb 30\n", + "Hostel 24\n", + "Resort 14\n", + "Villa 4\n", + "Vacation rental 3\n", + "Riad 1\n", + "Guesthouse 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['accommodation_type'] = travel_trip_dull_3_df['accommodation_type'].fillna('Unknown')\n", + "travel_trip_dull_3_df['accommodation_type'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "7390ae0e-c80d-409e-a707-66f88c843ebe", + "metadata": {}, + "outputs": [], + "source": [ + "mapping = {\n", + " 'Riad': 'Villa',\n", + " 'Guesthouse': 'Vacation rental'\n", + "}\n", + "travel_trip_dull_3_df['accommodation_type'] = travel_trip_dull_3_df['accommodation_type'].replace(mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "67a3793c-298f-43bf-aa48-5f80f46a1e74", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "accommodation_type\n", + "Hotel 60\n", + "Airbnb 30\n", + "Hostel 24\n", + "Resort 14\n", + "Villa 5\n", + "Vacation rental 4\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['accommodation_type'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "id": "326e2bdb-7a35-4d9a-b740-ba3bfc58789e", + "metadata": {}, + "source": [ + "### Clean columns accommodation_cost" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "06c73f46-ab11-47c5-9443-22b4f8cf1df1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 137\n", + "unique 53\n", + "top 1200\n", + "freq 7\n", + "Name: accommodation_cost, dtype: object" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df['accommodation_cost'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "06e5d1f5-22da-4ade-bbb7-e28d3dd70c2b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1200, 800, 1000, 2000, 700, 1500, 500, 900, 2500, 3000, 1400, 600,\n", + " 400, 1100, 200, 150, 180, 350, 2200, 300, 1300, 1800, 100, 5000,\n", + " 7000, 6000, 4000, 8000], dtype=object)" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df.loc[:, \"accommodation_cost\"] = (\n", + " travel_trip_dull_3_df[\"accommodation_cost\"]\n", + " .astype(str) # in case there are numbers/NaN in there\n", + " .str.strip()\n", + " .str.strip(\"$\")\n", + " .str.replace(\",\", \"\", regex=False)\n", + " .str.replace(\" USD\", \"\", regex=False)\n", + " .str.strip()\n", + ")\n", + "\n", + "travel_trip_dull_3_df.loc[:, \"accommodation_cost\"] = pd.to_numeric(\n", + " travel_trip_dull_3_df[\"accommodation_cost\"],\n", + " errors=\"coerce\"\n", + ")\n", + "\n", + "travel_trip_dull_3_df.accommodation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "7ce1be0c-db2f-4369-b653-3ed57b04584a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/13/s00136yd6q5bvv6x74_x_m204g4004/T/ipykernel_39449/2161018080.py:2: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " travel_trip_dull_3_df['accommodation_cost'] = travel_trip_dull_3_df['accommodation_cost'].fillna(median_cost)\n" + ] + }, + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_clean
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...1200Flight600.02023520235MayMayUnited States
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...800Flight500.02023620236JuneJuneCanada
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...1000Flight700.02023720237JulyJulySouth Korea
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2000Flight1000.02023820238AugustAugustUnited Kingdom
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...700Train200.02023920239SeptemberSeptemberVietnamese
..................................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...2500Private Car2000.02023820238AugustAugustBrazil
135Vancouver, Canada136CanadaVancouver2023-08-012023-08-016.0emma wilson29.0Female...5000Airplane3000.02023820238AugustAugustCanada
136Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...2000Train1000.02023920239SeptemberSeptemberChina
137Barcelona, Spain138SpainBarcelona2023-09-012023-09-017.0sofia rodriguez25.0Female...6000Airplane2500.02023920239SeptemberSeptemberSpain
138Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...7000Train2500.0202310202310OctoberOctoberNew Zealander
\n", + "

137 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-01 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-01 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-01 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "135 2023-08-01 2023-08-01 6.0 emma wilson 29.0 \n", + "136 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "137 2023-09-01 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "138 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_cost transportation_type \\\n", + "0 Male ... 1200 Flight \n", + "1 Female ... 800 Flight \n", + "2 Male ... 1000 Flight \n", + "3 Female ... 2000 Flight \n", + "4 Female ... 700 Train \n", + ".. ... ... ... ... \n", + "134 Male ... 2500 Private Car \n", + "135 Female ... 5000 Airplane \n", + "136 Male ... 2000 Train \n", + "137 Female ... 6000 Airplane \n", + "138 Male ... 7000 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600.0 2023 5 2023 5 \n", + "1 500.0 2023 6 2023 6 \n", + "2 700.0 2023 7 2023 7 \n", + "3 1000.0 2023 8 2023 8 \n", + "4 200.0 2023 9 2023 9 \n", + ".. ... ... ... ... ... \n", + "134 2000.0 2023 8 2023 8 \n", + "135 3000.0 2023 8 2023 8 \n", + "136 1000.0 2023 9 2023 9 \n", + "137 2500.0 2023 9 2023 9 \n", + "138 2500.0 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name traveler_nationality_clean \n", + "0 May May United States \n", + "1 June June Canada \n", + "2 July July South Korea \n", + "3 August August United Kingdom \n", + "4 September September Vietnamese \n", + ".. ... ... ... \n", + "134 August August Brazil \n", + "135 August August Canada \n", + "136 September September China \n", + "137 September September Spain \n", + "138 October October New Zealander \n", + "\n", + "[137 rows x 22 columns]" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_cost = travel_trip_dull_3_df['accommodation_cost'].median()\n", + "travel_trip_dull_3_df['accommodation_cost'] = travel_trip_dull_3_df['accommodation_cost'].fillna(median_cost)\n", + "travel_trip_dull_3_df['accommodation_cost']\n", + "travel_trip_dull_3_df" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "4fb753d3-8756-40e0-b949-5b79d29add1a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_dull_3_df.accommodation_cost.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "de1e173b-d8af-4e1d-8c5c-58061c7815e9", + "metadata": {}, + "outputs": [], + "source": [ + "travel_trip_complete_df = travel_trip_dull_3_df.copy()" + ] + }, + { + "cell_type": "markdown", + "id": "dfda13ba-a898-439d-b090-642873e95d1d", + "metadata": {}, + "source": [ + "# Our clean dataframe to do the analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "6c4e6f07-5486-41e5-be50-c9121c6de262", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gender...accommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_nametraveler_nationality_clean
0London, UK1UKLondon2023-05-012023-05-017.0john smith35.0Male...1200Flight600.02023520235MayMayUnited States
1Phuket, Thailand2ThailandPhuket2023-06-012023-06-015.0jane doe28.0Female...800Flight500.02023620236JuneJuneCanada
2Bali, Indonesia3IndonesiaBali2023-07-012023-07-017.0david lee45.0Male...1000Flight700.02023720237JulyJulySouth Korea
3New York, USA4USANew York2023-08-012023-08-0114.0sarah johnson29.0Female...2000Flight1000.02023820238AugustAugustUnited Kingdom
4Tokyo, Japan5JapanTokyo2023-09-012023-09-017.0kim nguyen26.0Female...700Train200.02023920239SeptemberSeptemberVietnamese
..................................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro2023-08-012023-08-019.0jose perez37.0Male...2500Private Car2000.02023820238AugustAugustBrazil
135Vancouver, Canada136CanadaVancouver2023-08-012023-08-016.0emma wilson29.0Female...5000Airplane3000.02023820238AugustAugustCanada
136Bangkok, Thailand137ThailandBangkok2023-09-012023-09-017.0ryan chen34.0Male...2000Train1000.02023920239SeptemberSeptemberChina
137Barcelona, Spain138SpainBarcelona2023-09-012023-09-017.0sofia rodriguez25.0Female...6000Airplane2500.02023920239SeptemberSeptemberSpain
138Auckland, New Zealand139New ZealandAuckland2023-10-012023-10-017.0william brown39.0Male...7000Train2500.0202310202310OctoberOctoberNew Zealander
\n", + "

137 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 2023-05-01 2023-05-01 7.0 john smith 35.0 \n", + "1 2023-06-01 2023-06-01 5.0 jane doe 28.0 \n", + "2 2023-07-01 2023-07-01 7.0 david lee 45.0 \n", + "3 2023-08-01 2023-08-01 14.0 sarah johnson 29.0 \n", + "4 2023-09-01 2023-09-01 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 2023-08-01 2023-08-01 9.0 jose perez 37.0 \n", + "135 2023-08-01 2023-08-01 6.0 emma wilson 29.0 \n", + "136 2023-09-01 2023-09-01 7.0 ryan chen 34.0 \n", + "137 2023-09-01 2023-09-01 7.0 sofia rodriguez 25.0 \n", + "138 2023-10-01 2023-10-01 7.0 william brown 39.0 \n", + "\n", + " traveler_gender ... accommodation_cost transportation_type \\\n", + "0 Male ... 1200 Flight \n", + "1 Female ... 800 Flight \n", + "2 Male ... 1000 Flight \n", + "3 Female ... 2000 Flight \n", + "4 Female ... 700 Train \n", + ".. ... ... ... ... \n", + "134 Male ... 2500 Private Car \n", + "135 Female ... 5000 Airplane \n", + "136 Male ... 2000 Train \n", + "137 Female ... 6000 Airplane \n", + "138 Male ... 7000 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600.0 2023 5 2023 5 \n", + "1 500.0 2023 6 2023 6 \n", + "2 700.0 2023 7 2023 7 \n", + "3 1000.0 2023 8 2023 8 \n", + "4 200.0 2023 9 2023 9 \n", + ".. ... ... ... ... ... \n", + "134 2000.0 2023 8 2023 8 \n", + "135 3000.0 2023 8 2023 8 \n", + "136 1000.0 2023 9 2023 9 \n", + "137 2500.0 2023 9 2023 9 \n", + "138 2500.0 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name traveler_nationality_clean \n", + "0 May May United States \n", + "1 June June Canada \n", + "2 July July South Korea \n", + "3 August August United Kingdom \n", + "4 September September Vietnamese \n", + ".. ... ... ... \n", + "134 August August Brazil \n", + "135 August August Canada \n", + "136 September September China \n", + "137 September September Spain \n", + "138 October October New Zealander \n", + "\n", + "[137 rows x 22 columns]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_complete_df" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "830f82b4-2841-46e9-87a7-f8e1a236cc42", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.makedirs(\"../data/clean\", exist_ok=True)\n", + "\n", + "# 1) Human / sharing version\n", + "travel_trip_complete_df.to_csv(\"../data/clean/travel_trip_complete.csv\", index=False)\n", + "\n", + "# 2) Analysis version (keeps dtypes)\n", + "travel_trip_complete_df.to_parquet(\"../data/clean/travel_trip_complete.parquet\", index=False)\n", + "# or:\n", + "# travel_trip_complete_df.to_pickle(\"../data/processed/travel_trip_complete.pkl\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "654aa65c-fa28-445b-bf20-8fb8f550ce11", + "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/explore_clean_data_username.ipynb b/notebooks/explore_clean_data_username.ipynb deleted file mode 100644 index 792d6005..00000000 --- a/notebooks/explore_clean_data_username.ipynb +++ /dev/null @@ -1 +0,0 @@ -# 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/seven_day_trip_details_with_nationality.csv b/notebooks/seven_day_trip_details_with_nationality.csv new file mode 100644 index 00000000..abe8df7e --- /dev/null +++ b/notebooks/seven_day_trip_details_with_nationality.csv @@ -0,0 +1,55 @@ +traveler_name,traveler_nationality,destination,duration_(days) +John Smith,American,"London, UK",7 +David Lee,Korean,"Bali, Indonesia",7 +Kim Nguyen,Vietnamese,"Tokyo, Japan",7 +Lucas Santos,Brazilian,"Rio de Janeiro, Brazil",7 +Laura Janssen,Dutch,"Amsterdam, Netherlands",7 +Mohammed Ali,Emirati,"Dubai, United Arab Emirates",7 +Ana Hernandez,Mexican,"Cancun, Mexico",7 +Carlos Garcia,Spanish,"Barcelona, Spain",7 +Fatima Khouri,Moroccan,"Marrakech, Morocco",7 +James MacKenzie,Scottish,"Edinburgh, Scotland",7 +James Wilson,Australian,Sydney,7 +Sofia Russo,Italian,Rome,7 +Bob Johnson,Canadian,Thailand,7 +Sophia Lee,Canadian,Egypt,7 +Amelia Brown,Australian,Canada,7 +Mia Johnson,American,"Paris, France",7 +Adam Lee,Canadian,"Sydney, Australia",7 +John Smith,American,"Cancun, Mexico",7 +Maria Silva,Brazilian,"Rio de Janeiro, Brazil",7 +Peter Brown,British,"London, UK",7 +Emma Garcia,Spanish,"Barcelona, Spain",7 +Michael Davis,American,"New York City, USA",7 +Kevin Kim,Korean,"Vancouver, Canada",7 +Jennifer Nguyen,Canadian,"Paris, France",7 +Rachel Lee,South Korean,"Sydney, AUS",7 +Jessica Wong,Canadian,"New York, USA",7 +Michael Chen,Chinese,"Los Angeles, USA",7 +Kenji Nakamura,Japanese,Tokyo,7 +Ana Rodriguez,Spanish,Barcelona,7 +Tom Wilson,American,Bali,7 +Sarah Lee,South Korean,"Bali, Indonesia",7 +Maria Hernandez,Mexican,"Cancun, Mexico",7 +John Smith,British,"Paris, France",7 +Amanda Chen,Taiwanese,"Bali, Indonesia",7 +David Lee,Australian,"Sydney, Aus",7 +Nana Kwon,Korean,"Bangkok, Thai",7 +Tom Hanks,American,"New York, USA",7 +Emma Watson,British,"Phuket, Thai",7 +James Kim,American,"Rome, Italy",7 +Liam Nguyen,Vietnamese,Bangkok,7 +Jessica Chen,Taiwan,Rome,7 +Rodrigo Oliveira,Brazil,Rio de Janeiro,7 +Robert Mueller,Germany,Amsterdam,7 +Mark Tan,Singapore,Phuket,7 +Emma Lee,Italy,Rome,7 +George Chen,Greece,Santorini,7 +Bob Johnson,Canadian,"Paris, France",7 +David Lee,Korean,"Rome, Italy",7 +Jane Smith,British,"Tokyo, Japan",7 +David Kim,Korean,"Rome, Italy",7 +Emily Davis,American,"New York City, USA",7 +Ryan Chen,Chinese,"Bangkok, Thailand",7 +Sofia Rodriguez,Spanish,"Barcelona, Spain",7 +William Brown,New Zealander,"Auckland, New Zealand",7 diff --git a/notebooks/travel_trip_data_christos.ipynb b/notebooks/travel_trip_data_christos.ipynb new file mode 100644 index 00000000..52a28fab --- /dev/null +++ b/notebooks/travel_trip_data_christos.ipynb @@ -0,0 +1,2957 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "09d348ff-c86d-47fe-a0a5-88cb5f3a451c", + "metadata": {}, + "source": [ + "# Travel trip data project" + ] + }, + { + "cell_type": "markdown", + "id": "956ed231-de07-4ebf-ba4c-8de9400b8a3b", + "metadata": {}, + "source": [ + "## Loading the CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "b6ca0042-45bd-4e21-ac98-14bfa57c2f32", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Trip IDDestinationStart dateEnd dateDuration (days)Traveler nameTraveler ageTraveler genderTraveler nationalityAccommodation typeAccommodation costTransportation typeTransportation cost
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
56Paris, France10/5/202310/10/20235.0Michael Brown42.0MaleAmericanHotel1500Flight800
67Sydney, Australia11/20/202311/30/202310.0Emily Davis33.0FemaleAustralianHostel500Flight1200
78Rio de Janeiro, Brazil1/5/20241/12/20247.0Lucas Santos25.0MaleBrazilianAirbnb900Flight600
89Amsterdam, Netherlands2/14/20242/21/20247.0Laura Janssen31.0FemaleDutchHotel1200Train200
910Dubai, United Arab Emirates3/10/20243/17/20247.0Mohammed Ali39.0MaleEmiratiResort2500Flight800
\n", + "
" + ], + "text/plain": [ + " Trip ID Destination Start date End date \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 \n", + "5 6 Paris, France 10/5/2023 10/10/2023 \n", + "6 7 Sydney, Australia 11/20/2023 11/30/2023 \n", + "7 8 Rio de Janeiro, Brazil 1/5/2024 1/12/2024 \n", + "8 9 Amsterdam, Netherlands 2/14/2024 2/21/2024 \n", + "9 10 Dubai, United Arab Emirates 3/10/2024 3/17/2024 \n", + "\n", + " Duration (days) Traveler name Traveler age Traveler gender \\\n", + "0 7.0 John Smith 35.0 Male \n", + "1 5.0 Jane Doe 28.0 Female \n", + "2 7.0 David Lee 45.0 Male \n", + "3 14.0 Sarah Johnson 29.0 Female \n", + "4 7.0 Kim Nguyen 26.0 Female \n", + "5 5.0 Michael Brown 42.0 Male \n", + "6 10.0 Emily Davis 33.0 Female \n", + "7 7.0 Lucas Santos 25.0 Male \n", + "8 7.0 Laura Janssen 31.0 Female \n", + "9 7.0 Mohammed Ali 39.0 Male \n", + "\n", + " Traveler nationality Accommodation type Accommodation cost \\\n", + "0 American Hotel 1200 \n", + "1 Canadian Resort 800 \n", + "2 Korean Villa 1000 \n", + "3 British Hotel 2000 \n", + "4 Vietnamese Airbnb 700 \n", + "5 American Hotel 1500 \n", + "6 Australian Hostel 500 \n", + "7 Brazilian Airbnb 900 \n", + "8 Dutch Hotel 1200 \n", + "9 Emirati Resort 2500 \n", + "\n", + " Transportation type Transportation cost \n", + "0 Flight 600 \n", + "1 Flight 500 \n", + "2 Flight 700 \n", + "3 Flight 1000 \n", + "4 Train 200 \n", + "5 Flight 800 \n", + "6 Flight 1200 \n", + "7 Flight 600 \n", + "8 Train 200 \n", + "9 Flight 800 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_data = \"../data/raw/Travel details dataset.csv\"\n", + "travel_trip_christos_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + "travel_trip_christos_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "fab15d20-64fb-4aa3-ba38-53abaa1b6c9d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Trip ID', 'Destination', 'Start date', 'End date',\n", + " 'Duration (days)', 'Traveler name', 'Traveler age', 'Traveler gender',\n", + " 'Traveler nationality', 'Accommodation type', 'Accommodation cost',\n", + " 'Transportation type', 'Transportation cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "1d77a53a-7fa4-4426-9b43-5eb7c171b8ee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['trip_id', 'destination', 'start_date', 'end_date', 'duration_days',\n", + " 'traveler_name', 'traveler_age', 'traveler_gender',\n", + " 'traveler_nationality', 'accommodation_type', 'accommodation_cost',\n", + " 'transportation_type', 'transportation_cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.columns = travel_trip_christos_df.columns.str.replace(\"\", \"\", regex=False).str.replace(r\"[()]\", \"\", regex=True).str.strip(\")\").str.replace(\" \",\"_\").str.lower()\n", + "travel_trip_christos_df.columns " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "abfddadd-f65d-4e66-9ed8-a703bbf1dd65", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
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" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days traveler_name \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 7.0 John Smith \n", + "\n", + " traveler_age traveler_gender traveler_nationality accommodation_type \\\n", + "0 35.0 Male American Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600 " + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.head(1)" + ] + }, + { + "cell_type": "markdown", + "id": "1727b20c-d279-4e6a-b0a7-2aa6a3c6a753", + "metadata": {}, + "source": [ + "## Cleaning dataframe" + ] + }, + { + "cell_type": "markdown", + "id": "10bb170e-88ee-4070-b0c4-090eeb61e0fa", + "metadata": {}, + "source": [ + "### Starting with \"destination\"" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "aaeec699-0bfc-4b9a-9d9f-ced868221f38", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_costdestination_citydestination_country
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600LondonUK
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500PhuketThailand
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700BaliIndonesia
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000New YorkUSA
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200TokyoJapan
................................................
134135Rio de Janeiro, Brazil8/1/20238/10/20239.0Jose Perez37.0MaleBrazilianHostel2500Car2000Rio de JaneiroBrazil
135136Vancouver, Canada8/15/20238/21/20236.0Emma Wilson29.0FemaleCanadianHotel5000Airplane3000VancouverCanada
136137Bangkok, Thailand9/1/20239/8/20237.0Ryan Chen34.0MaleChineseHostel2000Train1000BangkokThailand
137138Barcelona, Spain9/15/20239/22/20237.0Sofia Rodriguez25.0FemaleSpanishAirbnb6000Airplane2500BarcelonaSpain
138139Auckland, New Zealand10/1/202310/8/20237.0William Brown39.0MaleNew ZealanderHotel7000Train2500AucklandNew Zealand
\n", + "

139 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 7.0 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 5.0 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 7.0 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 14.0 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 7.0 \n", + ".. ... ... ... ... ... \n", + "134 135 Rio de Janeiro, Brazil 8/1/2023 8/10/2023 9.0 \n", + "135 136 Vancouver, Canada 8/15/2023 8/21/2023 6.0 \n", + "136 137 Bangkok, Thailand 9/1/2023 9/8/2023 7.0 \n", + "137 138 Barcelona, Spain 9/15/2023 9/22/2023 7.0 \n", + "138 139 Auckland, New Zealand 10/1/2023 10/8/2023 7.0 \n", + "\n", + " traveler_name traveler_age traveler_gender traveler_nationality \\\n", + "0 John Smith 35.0 Male American \n", + "1 Jane Doe 28.0 Female Canadian \n", + "2 David Lee 45.0 Male Korean \n", + "3 Sarah Johnson 29.0 Female British \n", + "4 Kim Nguyen 26.0 Female Vietnamese \n", + ".. ... ... ... ... \n", + "134 Jose Perez 37.0 Male Brazilian \n", + "135 Emma Wilson 29.0 Female Canadian \n", + "136 Ryan Chen 34.0 Male Chinese \n", + "137 Sofia Rodriguez 25.0 Female Spanish \n", + "138 William Brown 39.0 Male New Zealander \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + ".. ... ... ... \n", + "134 Hostel 2500 Car \n", + "135 Hotel 5000 Airplane \n", + "136 Hostel 2000 Train \n", + "137 Airbnb 6000 Airplane \n", + "138 Hotel 7000 Train \n", + "\n", + " transportation_cost destination_city destination_country \n", + "0 600 London UK \n", + "1 500 Phuket Thailand \n", + "2 700 Bali Indonesia \n", + "3 1000 New York USA \n", + "4 200 Tokyo Japan \n", + ".. ... ... ... \n", + "134 2000 Rio de Janeiro Brazil \n", + "135 3000 Vancouver Canada \n", + "136 1000 Bangkok Thailand \n", + "137 2500 Barcelona Spain \n", + "138 2500 Auckland New Zealand \n", + "\n", + "[139 rows x 15 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I want to take the column destination, and rather split it in two, destination_city and destination_country\n", + "\n", + "# Split on the first comma into two columns\n", + "travel_trip_christos_df[[\"destination_city\", \"destination_country\"]] = (\n", + " travel_trip_christos_df[\"destination\"]\n", + " .str.split(\",\", n=1, expand=True)\n", + ")\n", + "\n", + "# Clean up whitespace\n", + "travel_trip_christos_df[\"destination_city\"] = (\n", + " travel_trip_christos_df[\"destination_city\"].str.strip()\n", + ")\n", + "travel_trip_christos_df[\"destination_country\"] = (\n", + " travel_trip_christos_df[\"destination_country\"].str.strip()\n", + ")\n", + "\n", + "travel_trip_christos_df" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "6c149138-e377-472c-8f1e-b35e91841b1d", + "metadata": {}, + "outputs": [], + "source": [ + "# I will drop the column \"destination\" after merging my table with the cleaned tables with my colleagues working in parallel." + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "61783158-1b4c-4c32-b279-722dccb72cfb", + "metadata": {}, + "outputs": [], + "source": [ + "# will however first move the two new columns on the right and next to destination" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "64fde922-91a8-4501-89af-21b879597429", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
0London, UK1UKLondon5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
1Phuket, Thailand2ThailandPhuket6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
2Bali, Indonesia3IndonesiaBali7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
3New York, USA4USANew York8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
4Tokyo, Japan5JapanTokyo9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro8/1/20238/10/20239.0Jose Perez37.0MaleBrazilianHostel2500Car2000
135Vancouver, Canada136CanadaVancouver8/15/20238/21/20236.0Emma Wilson29.0FemaleCanadianHotel5000Airplane3000
136Bangkok, Thailand137ThailandBangkok9/1/20239/8/20237.0Ryan Chen34.0MaleChineseHostel2000Train1000
137Barcelona, Spain138SpainBarcelona9/15/20239/22/20237.0Sofia Rodriguez25.0FemaleSpanishAirbnb6000Airplane2500
138Auckland, New Zealand139New ZealandAuckland10/1/202310/8/20237.0William Brown39.0MaleNew ZealanderHotel7000Train2500
\n", + "

139 rows × 15 columns

\n", + "
" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 5/1/2023 5/8/2023 7.0 John Smith 35.0 \n", + "1 6/15/2023 6/20/2023 5.0 Jane Doe 28.0 \n", + "2 7/1/2023 7/8/2023 7.0 David Lee 45.0 \n", + "3 8/15/2023 8/29/2023 14.0 Sarah Johnson 29.0 \n", + "4 9/10/2023 9/17/2023 7.0 Kim Nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 8/1/2023 8/10/2023 9.0 Jose Perez 37.0 \n", + "135 8/15/2023 8/21/2023 6.0 Emma Wilson 29.0 \n", + "136 9/1/2023 9/8/2023 7.0 Ryan Chen 34.0 \n", + "137 9/15/2023 9/22/2023 7.0 Sofia Rodriguez 25.0 \n", + "138 10/1/2023 10/8/2023 7.0 William Brown 39.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type \\\n", + "0 Male American Hotel \n", + "1 Female Canadian Resort \n", + "2 Male Korean Villa \n", + "3 Female British Hotel \n", + "4 Female Vietnamese Airbnb \n", + ".. ... ... ... \n", + "134 Male Brazilian Hostel \n", + "135 Female Canadian Hotel \n", + "136 Male Chinese Hostel \n", + "137 Female Spanish Airbnb \n", + "138 Male New Zealander Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600 \n", + "1 800 Flight 500 \n", + "2 1000 Flight 700 \n", + "3 2000 Flight 1000 \n", + "4 700 Train 200 \n", + ".. ... ... ... \n", + "134 2500 Car 2000 \n", + "135 5000 Airplane 3000 \n", + "136 2000 Train 1000 \n", + "137 6000 Airplane 2500 \n", + "138 7000 Train 2500 \n", + "\n", + "[139 rows x 15 columns]" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cols = list(travel_trip_christos_df.columns)\n", + "cols.insert(cols.index(\"destination\") + 1, cols.pop(cols.index(\"destination_city\")))\n", + "travel_trip_christos_df = travel_trip_christos_df.loc[:, cols]\n", + "\n", + "cols = list(travel_trip_christos_df.columns)\n", + "cols.insert(cols.index(\"destination_city\") - 1, cols.pop(cols.index(\"destination_country\")))\n", + "travel_trip_christos_df = travel_trip_christos_df.loc[:, cols]\n", + "\n", + "cols = list(travel_trip_christos_df.columns)\n", + "cols.insert(cols.index(\"destination_country\") - 1, cols.pop(cols.index(\"destination\")))\n", + "travel_trip_christos_df = travel_trip_christos_df.loc[:, cols]\n", + "\n", + "travel_trip_christos_df" + ] + }, + { + "cell_type": "markdown", + "id": "faece5e5-8983-47fc-9694-2a9e9ff937f6", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "### Searching for NaN values" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "27f20889-fafe-4d54-8fdd-a1cbb348949c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination 2\n", + "trip_id 0\n", + "destination_country 68\n", + "destination_city 2\n", + "start_date 2\n", + "end_date 2\n", + "duration_days 2\n", + "traveler_name 2\n", + "traveler_age 2\n", + "traveler_gender 2\n", + "traveler_nationality 2\n", + "accommodation_type 2\n", + "accommodation_cost 2\n", + "transportation_type 3\n", + "transportation_cost 3\n", + "dtype: int64" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "b11c4625-e73d-4fff-9244-6b50f3ef64cf", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['London, UK', 'Phuket, Thailand', 'Bali, Indonesia',\n", + " 'New York, USA', 'Tokyo, Japan', 'Paris, France',\n", + " 'Sydney, Australia', 'Rio de Janeiro, Brazil',\n", + " 'Amsterdam, Netherlands', 'Dubai, United Arab Emirates',\n", + " 'Cancun, Mexico', 'Barcelona, Spain', 'Honolulu, Hawaii',\n", + " 'Berlin, Germany', 'Marrakech, Morocco', 'Edinburgh, Scotland',\n", + " 'Paris', 'Bali', 'London', 'Tokyo', 'New York', 'Sydney', 'Rome',\n", + " 'Bangkok', 'Hawaii', 'Barcelona', 'Japan', 'Thailand', 'France',\n", + " 'Australia', 'Brazil', 'Greece', 'Egypt', 'Mexico', 'Italy',\n", + " 'Spain', 'Canada', 'New York City, USA', 'Bangkok, Thailand',\n", + " 'Vancouver, Canada', 'Sydney, AUS', 'Seoul, South Korea',\n", + " 'Los Angeles, USA', 'Rome, Italy', 'Cape Town', nan,\n", + " 'Cape Town, SA', 'Sydney, Aus', 'Bangkok, Thai', 'Phuket, Thai',\n", + " 'Dubai', 'Seoul', 'Rio de Janeiro', 'Amsterdam', 'Phuket',\n", + " 'Santorini', 'Phnom Penh', 'Athens, Greece',\n", + " 'Cape Town, South Africa', 'Auckland, New Zealand'], dtype=object)" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# checking the destinatio column to see what destination_city has so many NaN values. \n", + "# It shows that for many rows we only have information about the city. \n", + "# That's no problem, we can easily extrapolate the country frrom it.\n", + "travel_trip_christos_df.destination.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "a873cdec-c049-4dff-a77c-fa1deab3b8f6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "# we can immediately identify that most columns have 2 NaN values. Will do a test to check whether those values are in the same rows\n", + "\n", + "assumption_columns = [\n", + " \"destination\",\n", + " \"destination_city\",\n", + " \"start_date\",\n", + " \"end_date\",\n", + " \"duration_days\",\n", + " \"traveler_name\",\n", + " \"traveler_age\",\n", + " \"traveler_gender\",\n", + " \"traveler_nationality\",\n", + " \"accommodation_type\",\n", + " \"accommodation_cost\"\n", + "]\n", + "\n", + "all_null_test = travel_trip_christos_df[assumption_columns].isna().all(axis=1)\n", + "all_null_count = all_null_test.sum()\n", + "print(all_null_count)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "caafe407-12f0-4c5c-a634-b60ebfa47211", + "metadata": {}, + "outputs": [], + "source": [ + "# our assumption is true, those two rows are fully empty and we can drop them" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "13c1c566-2d51-4926-865c-ee58cb1db1eb", + "metadata": {}, + "outputs": [], + "source": [ + "travel_trip_christos_df = travel_trip_christos_df.dropna(thresh=2)" + ] + }, + { + "cell_type": "markdown", + "id": "cd0f77de-fee7-410a-a798-5da331383694", + "metadata": {}, + "source": [ + "### Checking for duplicates" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "dc4b639d-8eb6-4b95-9c65-a902de787eea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "64966465-7f5d-4a19-bf2d-259418620964", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(28)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.traveler_name.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "d554abd2-34aa-4e27-be66-47c840f0e7ab", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "109" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.traveler_name.nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "743571d4-a2f9-489c-88e4-445300dfbf3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['John Smith', 'Jane Doe', 'David Lee', 'Sarah Johnson',\n", + " 'Kim Nguyen', 'Michael Brown', 'Emily Davis', 'Lucas Santos',\n", + " 'Laura Janssen', 'Mohammed Ali', 'Ana Hernandez', 'Carlos Garcia',\n", + " 'Lily Wong', 'Hans Mueller', 'Fatima Khouri', 'James MacKenzie',\n", + " 'Michael Chang', 'Olivia Rodriguez', 'Kenji Nakamura', 'Emily Lee',\n", + " 'James Wilson', 'Sofia Russo', 'Raj Patel', 'Lily Nguyen',\n", + " 'David Kim', 'Maria Garcia', 'Alice Smith', 'Bob Johnson',\n", + " 'Charlie Lee', 'Emma Davis', 'Olivia Martin', 'Harry Wilson',\n", + " 'Sophia Lee', 'James Brown', 'Mia Johnson', 'William Davis',\n", + " 'Amelia Brown', 'Adam Lee', 'Sarah Wong', 'Maria Silva',\n", + " 'Peter Brown', 'Emma Garcia', 'Michael Davis', 'Nina Patel',\n", + " 'Kevin Kim', 'Laura van den Berg', 'Jennifer Nguyen', 'Rachel Lee',\n", + " 'Jessica Wong', 'Felipe Almeida', 'Nisa Patel', 'Ben Smith',\n", + " 'Laura Gomez', 'Park Min Woo', 'Michael Chen', 'Sofia Rossi',\n", + " 'Rachel Sanders', 'Emily Watson', 'Ana Rodriguez', 'Tom Wilson',\n", + " 'Olivia Green', 'James Chen', 'Lila Patel', 'Marco Rossi',\n", + " 'Sarah Brown', 'Sarah Lee', 'Alex Kim', 'Maria Hernandez',\n", + " 'Mark Johnson', 'Amanda Chen', 'Nana Kwon', 'Tom Hanks',\n", + " 'Emma Watson', 'James Kim', 'Fatima Ahmed', 'Liam Nguyen',\n", + " 'Giulia Rossi', 'Putra Wijaya', 'Kim Min-ji', 'Emily Johnson',\n", + " 'Michael Wong', 'Jessica Chen', 'Ken Tanaka', 'Rodrigo Oliveira',\n", + " 'Olivia Kim', 'Robert Mueller', 'Lisa Chen', 'Emily Wong',\n", + " 'Mark Tan', 'Emma Lee', 'George Chen', 'Sophia Kim', 'Alex Ng',\n", + " 'Cindy Chen', 'Emily Kim', 'Frank Li', 'Gina Lee', 'Henry Kim',\n", + " 'Isabella Chen', 'Jack Smith', 'Katie Johnson', 'John Doe',\n", + " 'Jane Smith', 'Michael Johnson', 'Jose Perez', 'Emma Wilson',\n", + " 'Ryan Chen', 'Sofia Rodriguez', 'William Brown'], dtype=object)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.traveler_name.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "07f19434-f40b-4e91-bacf-97534a93d48d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['john smith', 'jane doe', 'david lee', 'sarah johnson',\n", + " 'kim nguyen', 'michael brown', 'emily davis', 'lucas santos',\n", + " 'laura janssen', 'mohammed ali', 'ana hernandez', 'carlos garcia',\n", + " 'lily wong', 'hans mueller', 'fatima khouri', 'james mackenzie',\n", + " 'michael chang', 'olivia rodriguez', 'kenji nakamura', 'emily lee',\n", + " 'james wilson', 'sofia russo', 'raj patel', 'lily nguyen',\n", + " 'david kim', 'maria garcia', 'alice smith', 'bob johnson',\n", + " 'charlie lee', 'emma davis', 'olivia martin', 'harry wilson',\n", + " 'sophia lee', 'james brown', 'mia johnson', 'william davis',\n", + " 'amelia brown', 'adam lee', 'sarah wong', 'maria silva',\n", + " 'peter brown', 'emma garcia', 'michael davis', 'nina patel',\n", + " 'kevin kim', 'laura van den berg', 'jennifer nguyen', 'rachel lee',\n", + " 'jessica wong', 'felipe almeida', 'nisa patel', 'ben smith',\n", + " 'laura gomez', 'park min woo', 'michael chen', 'sofia rossi',\n", + " 'rachel sanders', 'emily watson', 'ana rodriguez', 'tom wilson',\n", + " 'olivia green', 'james chen', 'lila patel', 'marco rossi',\n", + " 'sarah brown', 'sarah lee', 'alex kim', 'maria hernandez',\n", + " 'mark johnson', 'amanda chen', 'nana kwon', 'tom hanks',\n", + " 'emma watson', 'james kim', 'fatima ahmed', 'liam nguyen',\n", + " 'giulia rossi', 'putra wijaya', 'kim min ji', 'emily johnson',\n", + " 'michael wong', 'jessica chen', 'ken tanaka', 'rodrigo oliveira',\n", + " 'olivia kim', 'robert mueller', 'lisa chen', 'emily wong',\n", + " 'mark tan', 'emma lee', 'george chen', 'sophia kim', 'alex ng',\n", + " 'cindy chen', 'emily kim', 'frank li', 'gina lee', 'henry kim',\n", + " 'isabella chen', 'jack smith', 'katie johnson', 'john doe',\n", + " 'jane smith', 'michael johnson', 'jose perez', 'emma wilson',\n", + " 'ryan chen', 'sofia rodriguez', 'william brown'], dtype=object)" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# I will make sure that the column traveler_name is lowercased and stripped from double spaces or dashes, just in case this might hide more duplicates\n", + "# ideally, when you created travel_trip_christos_df, you already did:\n", + "# travel_trip_christos_df = original_df[...].copy()\n", + "\n", + "travel_trip_christos_df.loc[:, \"traveler_name\"] = (\n", + " travel_trip_christos_df[\"traveler_name\"]\n", + " .str.strip() # trims whitespace at both ends\n", + " .str.replace(\"-\", \" \", regex=False) # replace hyphen with space\n", + " .str.lower()\n", + ")\n", + "travel_trip_christos_df.traveler_name.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0212cf0f-6f93-4e04-807a-47520e38914b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(28)" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.traveler_name.duplicated().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "f2fa8293-8ecb-416d-b7b4-d7181942951d", + "metadata": {}, + "outputs": [], + "source": [ + "# we can see that there are no exact full string duplicates of the column traveler_name. \n", + "# The number 29 above probalby comes from partial dupication, like only first name or only last name" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "83c5031e-29d0-493f-adb3-08236d85504d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination 0\n", + "trip_id 0\n", + "destination_country 66\n", + "destination_city 0\n", + "start_date 0\n", + "end_date 0\n", + "duration_days 0\n", + "traveler_name 0\n", + "traveler_age 0\n", + "traveler_gender 0\n", + "traveler_nationality 0\n", + "accommodation_type 0\n", + "accommodation_cost 0\n", + "transportation_type 1\n", + "transportation_cost 1\n", + "dtype: int64" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "6a636a58-87df-4df2-8edd-2056952b9d05", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
82Rome, Italy83ItalyRome4/15/20254/22/20257.0james kim41.0MaleAmericanHotel100NaNNaN
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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city start_date \\\n", + "82 Rome, Italy 83 Italy Rome 4/15/2025 \n", + "\n", + " end_date duration_days traveler_name traveler_age traveler_gender \\\n", + "82 4/22/2025 7.0 james kim 41.0 Male \n", + "\n", + " traveler_nationality accommodation_type accommodation_cost \\\n", + "82 American Hotel 100 \n", + "\n", + " transportation_type transportation_cost \n", + "82 NaN NaN " + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's find out the NaN values in \n", + "mask = travel_trip_christos_df[\"transportation_type\"].isna() & \\\n", + " travel_trip_christos_df[\"transportation_cost\"].isna()\n", + "\n", + "travel_trip_christos_df[mask]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "77be8df9-eef7-4374-8477-0d2b55e6901b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination object\n", + "trip_id int64\n", + "destination_country object\n", + "destination_city object\n", + "start_date object\n", + "end_date object\n", + "duration_days float64\n", + "traveler_name object\n", + "traveler_age float64\n", + "traveler_gender object\n", + "traveler_nationality object\n", + "accommodation_type object\n", + "accommodation_cost object\n", + "transportation_type object\n", + "transportation_cost object\n", + "dtype: object" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "de17fc4b-030b-47ab-9fbd-e2701d23568b", + "metadata": {}, + "outputs": [], + "source": [ + "# for transportation type and transportation_cost NaN values, we will group by destination_city and calculate the count of transportation_type and mean transportation_cost, and filter for Rome.\n", + "\n", + "# we will need to cast the data type of the column transportation_cost to float, before we can proceed with grouping.\n", + "\n", + "# for this to happen we will need to clean the column transportation_cost" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "6913f7b3-51ea-485d-bc35-04026cff7b18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['600', '500', '700', '1000', '200', '800', '1200', '100', '400',\n", + " '150', '$400 ', '$700 ', '$150 ', '$800 ', '$100 ', '$600 ',\n", + " '$80 ', '$500 ', '$300 ', '$50 ', '$120 ', '$75 ', '900', '50',\n", + " '$200 ', '$250 ', '$20 ', '300', '800 USD', '200 USD', '500 USD',\n", + " '700 USD', '300 USD', '600 USD', '400 USD', '1000 USD', nan,\n", + " '100 USD', '350 USD', '150 USD', '$1,200 ', '$900 ', '$1,500 ',\n", + " '$1,000 ', '250', '2500', '1500', '2000', '3000'], dtype=object)" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.transportation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "a5896df5-f63f-4efc-8bea-9dc1a7f35eb7", + "metadata": {}, + "outputs": [], + "source": [ + "# we will need to remove anything but the numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "e28ce23d-fe62-4efe-aa17-435e908887f3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([600.0, 500.0, 700.0, 1000.0, 200.0, 800.0, 1200.0, 100.0, 400.0,\n", + " 150.0, 80.0, 300.0, 50.0, 120.0, 75.0, 900.0, 250.0, 20.0, nan,\n", + " 350.0, 1500.0, 2500.0, 2000.0, 3000.0], dtype=object)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.loc[:, \"transportation_cost\"] = (\n", + " travel_trip_christos_df[\"transportation_cost\"]\n", + " .astype(str) # in case there are numbers/NaN in there\n", + " .str.strip()\n", + " .str.strip(\"$\")\n", + " .str.replace(\",\", \"\", regex=False)\n", + " .str.replace(\" USD\", \"\", regex=False)\n", + " .str.strip()\n", + ")\n", + "\n", + "travel_trip_christos_df.loc[:, \"transportation_cost\"] = pd.to_numeric(\n", + " travel_trip_christos_df[\"transportation_cost\"],\n", + " errors=\"coerce\"\n", + ")\n", + "\n", + "travel_trip_christos_df.transportation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "3bdbfd84-e3c0-43b7-ad54-95494d446afb", + "metadata": {}, + "outputs": [], + "source": [ + "# assuming travel_trip_christos_df was created with .copy() earlier\n", + "#travel_trip_christos_df.loc[:, \"transportation_cost\"] = (\n", + "# travel_trip_christos_df[\"transportation_cost\"]\n", + "# .fillna(\"0\")\n", + "# )\n", + "\n", + "# travel_trip_christos_df.transportation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "58380656-bd6b-45f2-8a5d-b319b1f6ba9a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "destination object\n", + "trip_id int64\n", + "destination_country object\n", + "destination_city object\n", + "start_date object\n", + "end_date object\n", + "duration_days float64\n", + "traveler_name object\n", + "traveler_age float64\n", + "traveler_gender object\n", + "traveler_nationality object\n", + "accommodation_type object\n", + "accommodation_cost object\n", + "transportation_type object\n", + "transportation_cost object\n", + "dtype: object" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now we cast, in order to be able to group later\n", + "\n", + "travel_trip_christos_df.loc[:, \"transportation_cost\"] = (\n", + " travel_trip_christos_df[\"transportation_cost\"].astype(\"Int64\")\n", + ")\n", + "\n", + "travel_trip_christos_df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "9628c441-a06e-4d19-a130-269c65090c8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_mean
destination_citytransportation_type
RomePlane2625.0
Train6405.0
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" + ], + "text/plain": [ + " transportation_type_count \\\n", + "destination_city transportation_type \n", + "Rome Plane 2 \n", + " Train 6 \n", + "\n", + " transportation_cost_mean \n", + "destination_city transportation_type \n", + "Rome Plane 625.0 \n", + " Train 405.0 " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# for transportation type and transportation_cost NaN values, we will group by destination_city and calculate the count of transportation_type and mean transportation_cost, and filter for Rome.\n", + "\n", + "grouped_df = (travel_trip_christos_df.groupby([\"destination_city\", \"transportation_type\"]).agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_mean = (\"transportation_cost\", \"mean\")))\n", + "filtered_grouped_df = grouped_df.loc[[\"Rome\"]]\n", + "filtered_grouped_df" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "1f658817-fc59-4144-8b15-4489e2a101b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# therefore, we will fill the empty values transportation_type with \"Train\", and transportation_cost with \"405.0\"\n", + "travel_trip_christos_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_christos_df[\"transportation_type\"].fillna(\"Train\")\n", + ")\n", + "\n", + "travel_trip_christos_df.transportation_type.isnull().sum()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "96e60c97-5c0e-478f-b67a-ad331219e1c3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/13/s00136yd6q5bvv6x74_x_m204g4004/T/ipykernel_33666/4279038090.py:3: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " travel_trip_christos_df[\"transportation_cost\"].fillna(405.0)\n" + ] + }, + { + "data": { + "text/plain": [ + "np.int64(0)" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# now let's replace the 0 we placed before in place of NaN, with the mean value.\n", + "travel_trip_christos_df.loc[:, \"transportation_cost\"] = (\n", + " travel_trip_christos_df[\"transportation_cost\"].fillna(405.0)\n", + ")\n", + "\n", + "travel_trip_christos_df.transportation_cost.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "6e0c78f9-b504-491d-be01-4cab9cf833a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
0London, UK1UKLondon5/1/20235/8/20237.0john smith35.0MaleAmericanHotel1200Flight600.0
1Phuket, Thailand2ThailandPhuket6/15/20236/20/20235.0jane doe28.0FemaleCanadianResort800Flight500.0
2Bali, Indonesia3IndonesiaBali7/1/20237/8/20237.0david lee45.0MaleKoreanVilla1000Flight700.0
3New York, USA4USANew York8/15/20238/29/202314.0sarah johnson29.0FemaleBritishHotel2000Flight1000.0
4Tokyo, Japan5JapanTokyo9/10/20239/17/20237.0kim nguyen26.0FemaleVietnameseAirbnb700Train200.0
................................................
134Rio de Janeiro, Brazil135BrazilRio de Janeiro8/1/20238/10/20239.0jose perez37.0MaleBrazilianHostel2500Car2000.0
135Vancouver, Canada136CanadaVancouver8/15/20238/21/20236.0emma wilson29.0FemaleCanadianHotel5000Airplane3000.0
136Bangkok, Thailand137ThailandBangkok9/1/20239/8/20237.0ryan chen34.0MaleChineseHostel2000Train1000.0
137Barcelona, Spain138SpainBarcelona9/15/20239/22/20237.0sofia rodriguez25.0FemaleSpanishAirbnb6000Airplane2500.0
138Auckland, New Zealand139New ZealandAuckland10/1/202310/8/20237.0william brown39.0MaleNew ZealanderHotel7000Train2500.0
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137 rows × 15 columns

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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "0 London, UK 1 UK London \n", + "1 Phuket, Thailand 2 Thailand Phuket \n", + "2 Bali, Indonesia 3 Indonesia Bali \n", + "3 New York, USA 4 USA New York \n", + "4 Tokyo, Japan 5 Japan Tokyo \n", + ".. ... ... ... ... \n", + "134 Rio de Janeiro, Brazil 135 Brazil Rio de Janeiro \n", + "135 Vancouver, Canada 136 Canada Vancouver \n", + "136 Bangkok, Thailand 137 Thailand Bangkok \n", + "137 Barcelona, Spain 138 Spain Barcelona \n", + "138 Auckland, New Zealand 139 New Zealand Auckland \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "0 5/1/2023 5/8/2023 7.0 john smith 35.0 \n", + "1 6/15/2023 6/20/2023 5.0 jane doe 28.0 \n", + "2 7/1/2023 7/8/2023 7.0 david lee 45.0 \n", + "3 8/15/2023 8/29/2023 14.0 sarah johnson 29.0 \n", + "4 9/10/2023 9/17/2023 7.0 kim nguyen 26.0 \n", + ".. ... ... ... ... ... \n", + "134 8/1/2023 8/10/2023 9.0 jose perez 37.0 \n", + "135 8/15/2023 8/21/2023 6.0 emma wilson 29.0 \n", + "136 9/1/2023 9/8/2023 7.0 ryan chen 34.0 \n", + "137 9/15/2023 9/22/2023 7.0 sofia rodriguez 25.0 \n", + "138 10/1/2023 10/8/2023 7.0 william brown 39.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type \\\n", + "0 Male American Hotel \n", + "1 Female Canadian Resort \n", + "2 Male Korean Villa \n", + "3 Female British Hotel \n", + "4 Female Vietnamese Airbnb \n", + ".. ... ... ... \n", + "134 Male Brazilian Hostel \n", + "135 Female Canadian Hotel \n", + "136 Male Chinese Hostel \n", + "137 Female Spanish Airbnb \n", + "138 Male New Zealander Hotel \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600.0 \n", + "1 800 Flight 500.0 \n", + "2 1000 Flight 700.0 \n", + "3 2000 Flight 1000.0 \n", + "4 700 Train 200.0 \n", + ".. ... ... ... \n", + "134 2500 Car 2000.0 \n", + "135 5000 Airplane 3000.0 \n", + "136 2000 Train 1000.0 \n", + "137 6000 Airplane 2500.0 \n", + "138 7000 Train 2500.0 \n", + "\n", + "[137 rows x 15 columns]" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "a926c2de-009a-42e8-894a-9da707627077", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'FlightTrainPlaneBusCar rentalSubwayCarFerryAirplane'" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# let's quickly check the transportation_types\n", + "\n", + "travel_trip_christos_df.transportation_type.unique().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "2529893b-1e8b-4828-9c9b-c6d918413bce", + "metadata": {}, + "outputs": [], + "source": [ + "# do we need to also change \"Subway\" to train? Maybe, who travels to a city using a subway, subways are intra city transportation modes." + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "f78c1e93-d5ac-4d48-ba8a-db6af09fce44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_mean
transportation_type
Subway120.0
Train38346.184211
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" + ], + "text/plain": [ + " transportation_type_count transportation_cost_mean\n", + "transportation_type \n", + "Subway 1 20.0\n", + "Train 38 346.184211" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subway_df = travel_trip_christos_df.groupby(\"transportation_type\").agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_mean = (\"transportation_cost\", \"mean\"))\n", + "filtered_subway_df = subway_df.loc[[\"Subway\", \"Train\"]]\n", + "filtered_subway_df" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "0c50f0b0-ba4d-47db-8f81-5555bb6d786b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
57Seoul, South Korea58South KoreaSeoul5/10/20245/18/20248.0park min woo27.0MaleSouth KoreanHostel$500Subway20.0
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" + ], + "text/plain": [ + " destination trip_id destination_country destination_city \\\n", + "57 Seoul, South Korea 58 South Korea Seoul \n", + "\n", + " start_date end_date duration_days traveler_name traveler_age \\\n", + "57 5/10/2024 5/18/2024 8.0 park min woo 27.0 \n", + "\n", + " traveler_gender traveler_nationality accommodation_type accommodation_cost \\\n", + "57 Male South Korean Hostel $500 \n", + "\n", + " transportation_type transportation_cost \n", + "57 Subway 20.0 " + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df[travel_trip_christos_df[\"transportation_type\"] == \"Subway\"]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "0b6bebb9-c8ac-495b-b76f-cb10630b7c55", + "metadata": {}, + "outputs": [], + "source": [ + "# Subway seems to be an outlier data, but let's check at least if the transportation_cost of the \"Subway\" is at least close to the minimum cost for \"Train\"" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "a9be2ef9-9390-4c8e-a160-b701e5cd9815", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_min
transportation_type
Subway120.0
Train3880.0
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" + ], + "text/plain": [ + " transportation_type_count transportation_cost_min\n", + "transportation_type \n", + "Subway 1 20.0\n", + "Train 38 80.0" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "subway_df = travel_trip_christos_df.groupby(\"transportation_type\").agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_min = (\"transportation_cost\", \"min\"))\n", + "filtered_subway_df = subway_df.loc[[\"Subway\", \"Train\"]]\n", + "filtered_subway_df" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "f74e1dcc-3c6c-41fa-a168-45e24076e656", + "metadata": {}, + "outputs": [], + "source": [ + "# this is not the case, therefore, we will exclude it from our calculations of \"Train\"" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "a1d31b3c-fe13-4f3d-a4c3-924af8a012cd", + "metadata": {}, + "outputs": [], + "source": [ + "# and \"Car\" and \"Car rental\" under \"Car\"?" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "5801b326-635a-4be9-b759-3f3a672e681a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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transportation_type_counttransportation_cost_min
transportation_type
Car rental13200.0
Car3300.0
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" + ], + "text/plain": [ + " transportation_type_count transportation_cost_min\n", + "transportation_type \n", + "Car rental 13 200.0\n", + "Car 3 300.0" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "car_df = travel_trip_christos_df.groupby(\"transportation_type\").agg(transportation_type_count = (\"transportation_type\", \"count\"), transportation_cost_mean = (\"transportation_cost\", \"mean\"))\n", + "filtered_car_df = subway_df.loc[[\"Car rental\", \"Car\"]]\n", + "filtered_car_df" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "8919054b-82f5-478c-a799-4da6ef741cca", + "metadata": {}, + "outputs": [], + "source": [ + "# no, we see that there is a massive difference in the mean cost between the two categories, suggesting that it is indeed two different transportation modes. \n", + "# Car rental suggests that someone rented a car at the destnation, where car probably indicates that someone did a road trip using a car to destination.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d7b7ff99-5f4d-4031-9a75-14aab6ed07a1", + "metadata": {}, + "source": [ + "### Merging equivalent transportations types" + ] + }, + { + "cell_type": "code", + "execution_count": 99, + "id": "b3a35a1c-7124-45b7-8c8f-b745c5a5450d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Flight', 'Train', 'Airplane', 'Bus', 'Rental Car', 'Subway',\n", + " 'Private Car', 'Ferry'], dtype=object)" + ] + }, + "execution_count": 99, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ok, we will merge \"Plane\" and \"Airplane\" under \"Airplane\", and \"Car rental\" to \"Rental Car\", and \"Car\" to \"Private Car\"\n", + "travel_trip_christos_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_christos_df[\"transportation_type\"]\n", + " .replace({\"Plane\": \"Airplane\"})\n", + ")\n", + "\n", + "travel_trip_christos_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_christos_df[\"transportation_type\"]\n", + " .replace({\"Car rental\": \"Rental Car\"})\n", + ")\n", + "\n", + "travel_trip_christos_df.loc[:, \"transportation_type\"] = (\n", + " travel_trip_christos_df[\"transportation_type\"]\n", + " .replace({\"Car\": \"Private Car\"})\n", + ")\n", + "\n", + "travel_trip_christos_df[\"transportation_type\"].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "bd76ad1c-08b2-4712-9143-c93320760674", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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destinationtrip_iddestination_countrydestination_citystart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
0London, UK1UKLondon5/1/20235/8/20237.0john smith35.0MaleAmericanHotel1200Flight600.0
1Phuket, Thailand2ThailandPhuket6/15/20236/20/20235.0jane doe28.0FemaleCanadianResort800Flight500.0
2Bali, Indonesia3IndonesiaBali7/1/20237/8/20237.0david lee45.0MaleKoreanVilla1000Flight700.0
3New York, USA4USANew York8/15/20238/29/202314.0sarah johnson29.0FemaleBritishHotel2000Flight1000.0
4Tokyo, Japan5JapanTokyo9/10/20239/17/20237.0kim nguyen26.0FemaleVietnameseAirbnb700Train200.0
5Paris, France6FranceParis10/5/202310/10/20235.0michael brown42.0MaleAmericanHotel1500Flight800.0
6Sydney, Australia7AustraliaSydney11/20/202311/30/202310.0emily davis33.0FemaleAustralianHostel500Flight1200.0
7Rio de Janeiro, Brazil8BrazilRio de Janeiro1/5/20241/12/20247.0lucas santos25.0MaleBrazilianAirbnb900Flight600.0
8Amsterdam, Netherlands9NetherlandsAmsterdam2/14/20242/21/20247.0laura janssen31.0FemaleDutchHotel1200Train200.0
9Dubai, United Arab Emirates10United Arab EmiratesDubai3/10/20243/17/20247.0mohammed ali39.0MaleEmiratiResort2500Flight800.0
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" + ], + "text/plain": [ + " destination trip_id destination_country \\\n", + "0 London, UK 1 UK \n", + "1 Phuket, Thailand 2 Thailand \n", + "2 Bali, Indonesia 3 Indonesia \n", + "3 New York, USA 4 USA \n", + "4 Tokyo, Japan 5 Japan \n", + "5 Paris, France 6 France \n", + "6 Sydney, Australia 7 Australia \n", + "7 Rio de Janeiro, Brazil 8 Brazil \n", + "8 Amsterdam, Netherlands 9 Netherlands \n", + "9 Dubai, United Arab Emirates 10 United Arab Emirates \n", + "\n", + " destination_city start_date end_date duration_days traveler_name \\\n", + "0 London 5/1/2023 5/8/2023 7.0 john smith \n", + "1 Phuket 6/15/2023 6/20/2023 5.0 jane doe \n", + "2 Bali 7/1/2023 7/8/2023 7.0 david lee \n", + "3 New York 8/15/2023 8/29/2023 14.0 sarah johnson \n", + "4 Tokyo 9/10/2023 9/17/2023 7.0 kim nguyen \n", + "5 Paris 10/5/2023 10/10/2023 5.0 michael brown \n", + "6 Sydney 11/20/2023 11/30/2023 10.0 emily davis \n", + "7 Rio de Janeiro 1/5/2024 1/12/2024 7.0 lucas santos \n", + "8 Amsterdam 2/14/2024 2/21/2024 7.0 laura janssen \n", + "9 Dubai 3/10/2024 3/17/2024 7.0 mohammed ali \n", + "\n", + " traveler_age traveler_gender traveler_nationality accommodation_type \\\n", + "0 35.0 Male American Hotel \n", + "1 28.0 Female Canadian Resort \n", + "2 45.0 Male Korean Villa \n", + "3 29.0 Female British Hotel \n", + "4 26.0 Female Vietnamese Airbnb \n", + "5 42.0 Male American Hotel \n", + "6 33.0 Female Australian Hostel \n", + "7 25.0 Male Brazilian Airbnb \n", + "8 31.0 Female Dutch Hotel \n", + "9 39.0 Male Emirati Resort \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "0 1200 Flight 600.0 \n", + "1 800 Flight 500.0 \n", + "2 1000 Flight 700.0 \n", + "3 2000 Flight 1000.0 \n", + "4 700 Train 200.0 \n", + "5 1500 Flight 800.0 \n", + "6 500 Flight 1200.0 \n", + "7 900 Flight 600.0 \n", + "8 1200 Train 200.0 \n", + "9 2500 Flight 800.0 " + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_christos_df.head(10)" + ] + } + ], + "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/travel_trip_data_safina.ipynb b/notebooks/travel_trip_data_safina.ipynb new file mode 100644 index 00000000..8c1f2211 --- /dev/null +++ b/notebooks/travel_trip_data_safina.ipynb @@ -0,0 +1,1852 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "id": "c1e3f75d-b81e-42fe-bf48-cfe7e84ad8c9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Trip IDDestinationStart dateEnd dateDuration (days)Traveler nameTraveler ageTraveler genderTraveler nationalityAccommodation typeAccommodation costTransportation typeTransportation cost
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
56Paris, France10/5/202310/10/20235.0Michael Brown42.0MaleAmericanHotel1500Flight800
67Sydney, Australia11/20/202311/30/202310.0Emily Davis33.0FemaleAustralianHostel500Flight1200
78Rio de Janeiro, Brazil1/5/20241/12/20247.0Lucas Santos25.0MaleBrazilianAirbnb900Flight600
89Amsterdam, Netherlands2/14/20242/21/20247.0Laura Janssen31.0FemaleDutchHotel1200Train200
910Dubai, United Arab Emirates3/10/20243/17/20247.0Mohammed Ali39.0MaleEmiratiResort2500Flight800
\n", + "
" + ], + "text/plain": [ + " Trip ID Destination Start date End date \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 \n", + "5 6 Paris, France 10/5/2023 10/10/2023 \n", + "6 7 Sydney, Australia 11/20/2023 11/30/2023 \n", + "7 8 Rio de Janeiro, Brazil 1/5/2024 1/12/2024 \n", + "8 9 Amsterdam, Netherlands 2/14/2024 2/21/2024 \n", + "9 10 Dubai, United Arab Emirates 3/10/2024 3/17/2024 \n", + "\n", + " Duration (days) Traveler name Traveler age Traveler gender \\\n", + "0 7.0 John Smith 35.0 Male \n", + "1 5.0 Jane Doe 28.0 Female \n", + "2 7.0 David Lee 45.0 Male \n", + "3 14.0 Sarah Johnson 29.0 Female \n", + "4 7.0 Kim Nguyen 26.0 Female \n", + "5 5.0 Michael Brown 42.0 Male \n", + "6 10.0 Emily Davis 33.0 Female \n", + "7 7.0 Lucas Santos 25.0 Male \n", + "8 7.0 Laura Janssen 31.0 Female \n", + "9 7.0 Mohammed Ali 39.0 Male \n", + "\n", + " Traveler nationality Accommodation type Accommodation cost \\\n", + "0 American Hotel 1200 \n", + "1 Canadian Resort 800 \n", + "2 Korean Villa 1000 \n", + "3 British Hotel 2000 \n", + "4 Vietnamese Airbnb 700 \n", + "5 American Hotel 1500 \n", + "6 Australian Hostel 500 \n", + "7 Brazilian Airbnb 900 \n", + "8 Dutch Hotel 1200 \n", + "9 Emirati Resort 2500 \n", + "\n", + " Transportation type Transportation cost \n", + "0 Flight 600 \n", + "1 Flight 500 \n", + "2 Flight 700 \n", + "3 Flight 1000 \n", + "4 Train 200 \n", + "5 Flight 800 \n", + "6 Flight 1200 \n", + "7 Flight 600 \n", + "8 Train 200 \n", + "9 Flight 800 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_data = \"../data/raw/Travel details dataset.csv\"\n", + "travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + "travel_trip_safina_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c0ab5b41-86e2-43e4-bc90-5c21be0a6b4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Trip ID', 'Destination', 'Start date', 'End date', 'Duration (days)',\n", + " 'Traveler name', 'Traveler age', 'Traveler gender',\n", + " 'Traveler nationality', 'Accommodation type', 'Accommodation cost',\n", + " 'Transportation type', 'Transportation cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_safina_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "25725ab6-a864-4979-a60d-fa7e135b5424", + "metadata": {}, + "outputs": [], + "source": [ + "travel_trip_safina_df.columns = travel_trip_safina_df.columns.str.replace(\"\", \"\", regex=False).str.replace(r\"[()]\", \"\", regex=True).str.strip(\")\").str.replace(\" \",\"_\").str.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "270c5dec-c800-49fb-aec4-2d736b966d92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['trip_id', 'destination', 'start_date', 'end_date', 'duration_days',\n", + " 'traveler_name', 'traveler_age', 'traveler_gender',\n", + " 'traveler_nationality', 'accommodation_type', 'accommodation_cost',\n", + " 'transportation_type', 'transportation_cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_safina_df.columns = travel_trip_safina_df.columns.str.replace(\"\", \"\", regex=False).str.replace(r\"[()]\", \"\", regex=True).str.strip(\")\").str.replace(\" \",\"_\").str.lower()\n", + "travel_trip_safina_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f055d801-5c7a-487a-92ee-c5e86fd25571", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All steps completed successfully with the updated file path.\n", + "DataFrame variable name: travel_trip_safina_df\n", + "Data type of 'end_date': object\n", + "\n", + "First 5 rows of the converted 'end_date' column:\n", + " end_date\n", + "0 2023-05-08\n", + "1 2023-06-20\n", + "2 2023-07-08\n", + "3 2023-08-29\n", + "4 2023-09-17\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. DEFINE THE CORRECT ABSOLUTE PATH (using r-string for Windows backslashes)\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# 2. DEFINE COLUMN CLEANING FUNCTION\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names by lowercasing, stripping whitespace, \n", + " replacing spaces with underscores, and removing the BOM character.\"\"\"\n", + " name = name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + " return name\n", + "\n", + "# 3. LOAD DATA\n", + "# Load the data using your variable name, the correct path, and encoding.\n", + "try:\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + "except FileNotFoundError:\n", + " print(f\"\\nERROR: File not found. Please verify this path exists on your system: {travel_trip_data}\")\n", + " raise # Reraise the error so you can see it clearly\n", + "\n", + "# 4. APPLY COLUMN CLEANING\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# 5. CLEAN AND CONVERT 'end_date' TO STRING\n", + "# Convert to datetime first (essential cleaning to handle errors/format)\n", + "travel_trip_safina_df['end_date'] = pd.to_datetime(\n", + " travel_trip_safina_df['end_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Convert the datetime object to the desired string format (YYYY-MM-DD)\n", + "travel_trip_safina_df['end_date'] = travel_trip_safina_df['end_date'].dt.strftime('%Y-%m-%d')\n", + "\n", + "\n", + "# 6. DISPLAY RESULTS\n", + "print(\"All steps completed successfully with the updated file path.\")\n", + "print(f\"DataFrame variable name: travel_trip_safina_df\")\n", + "print(f\"Data type of 'end_date': {travel_trip_safina_df['end_date'].dtype}\")\n", + "print(\"\\nFirst 5 rows of the converted 'end_date' column:\")\n", + "print(travel_trip_safina_df[['end_date']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e327943d-a88c-4615-8059-aa9b98a9dff7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2023-05\n", + "1 2023-06\n", + "2 2023-07\n", + "3 2023-08\n", + "4 2023-09\n", + " ... \n", + "134 2023-08\n", + "135 2023-08\n", + "136 2023-09\n", + "137 2023-09\n", + "138 2023-10\n", + "Name: end_date, Length: 139, dtype: object\n" + ] + } + ], + "source": [ + "# Assuming travel_trip_safina_df is loaded and 'end_date' is currently datetime (or has been converted in the steps above).\n", + "\n", + "# Convert the 'end_date' column to datetime first (if not already done)\n", + "travel_trip_safina_df['end_date'] = pd.to_datetime(\n", + " travel_trip_safina_df['end_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Use .dt.strftime('%Y-%m') to format the date to Year and Month only.\n", + "travel_trip_safina_df['end_date'] = travel_trip_safina_df['end_date'].dt.strftime('%Y-%m')\n", + "\n", + "# Optional: Display the new data type and values\n", + "print(travel_trip_safina_df['end_date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "2fd68450-aad7-460b-b06a-46a4fbae5824", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data loading and feature engineering complete.\n", + "\n", + "First 5 rows of all date columns:\n", + " start_date start_year start_month end_date end_year end_month\n", + "0 2023-05-01 2023 5 2023-05-08 2023 5\n", + "1 2023-06-15 2023 6 2023-06-20 2023 6\n", + "2 2023-07-01 2023 7 2023-07-08 2023 7\n", + "3 2023-08-15 2023 8 2023-08-29 2023 8\n", + "4 2023-09-10 2023 9 2023-09-17 2023 9\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# Ensure this path is correct for your local machine:\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# 2. DEFINE COLUMN CLEANING FUNCTION\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names by lowercasing, stripping whitespace, \n", + " replacing spaces with underscores, and removing the BOM character.\"\"\"\n", + " name = name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + " return name\n", + "\n", + "# 3. LOAD DATA AND APPLY COLUMN CLEANING\n", + "try:\n", + " # Load the data using the corrected path and encoding\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + "except FileNotFoundError:\n", + " print(f\"Error: File not found at {travel_trip_data}. Please verify the path and file name.\")\n", + " raise\n", + "\n", + "# Apply the cleaning function to all column names\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# 4. CLEAN AND EXTRACT DATE COMPONENTS\n", + "\n", + "# --- Process 'end_date' column ---\n", + "# Convert to a clean datetime object\n", + "travel_trip_safina_df['end_date'] = pd.to_datetime(\n", + " travel_trip_safina_df['end_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Create the 'end_year' and 'end_month' integer columns\n", + "travel_trip_safina_df['end_year'] = travel_trip_safina_df['end_date'].dt.year.astype('Int64')\n", + "travel_trip_safina_df['end_month'] = travel_trip_safina_df['end_date'].dt.month.astype('Int64')\n", + "\n", + "\n", + "# --- Process 'start_date' column ---\n", + "# Convert to a clean datetime object\n", + "travel_trip_safina_df['start_date'] = pd.to_datetime(\n", + " travel_trip_safina_df['start_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Create the 'start_year' and 'start_month' integer columns\n", + "travel_trip_safina_df['start_year'] = travel_trip_safina_df['start_date'].dt.year.astype('Int64')\n", + "travel_trip_safina_df['start_month'] = travel_trip_safina_df['start_date'].dt.month.astype('Int64')\n", + "\n", + "\n", + "# 5. DISPLAY VERIFICATION (Optional)\n", + "print(\"Data loading and feature engineering complete.\")\n", + "print(\"\\nFirst 5 rows of all date columns:\")\n", + "print(travel_trip_safina_df[['start_date', 'start_year', 'start_month', \n", + " 'end_date', 'end_year', 'end_month']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "5634ba43-ec7b-45fe-ad10-4e3ec83105cc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " start_month start_month_name end_month end_month_name\n", + "0 5 May 5 May\n", + "1 6 June 6 June\n", + "2 7 July 7 July\n", + "3 8 August 8 August\n", + "4 9 September 9 September\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. Define the month mapping dictionary\n", + "month_map = {\n", + " 1: 'January', 2: 'February', 3: 'March', 4: 'April', \n", + " 5: 'May', 6: 'June', 7: 'July', 8: 'August', \n", + " 9: 'September', 10: 'October', 11: 'November', 12: 'December'\n", + "}\n", + "\n", + "# 2. Create 'start_month_name' by mapping the integer month to its name\n", + "travel_trip_safina_df['start_month_name'] = travel_trip_safina_df['start_month'].map(month_map)\n", + "\n", + "# 3. Create 'end_month_name' by mapping the integer month to its name\n", + "travel_trip_safina_df['end_month_name'] = travel_trip_safina_df['end_month'].map(month_map)\n", + "\n", + "# Optional: Display the first few rows for verification\n", + "print(travel_trip_safina_df[['start_month', 'start_month_name', 'end_month', 'end_month_name']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2494e574-beba-42ab-8d53-ebea32cc91e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " start_year start_month_name end_year end_month_name\n", + "0 2023 May 2023 May\n", + "1 2023 June 2023 June\n", + "2 2023 July 2023 July\n", + "3 2023 August 2023 August\n", + "4 2023 September 2023 September\n" + ] + } + ], + "source": [ + "print(travel_trip_safina_df[['start_year', 'start_month_name', 'end_year', 'end_month_name']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "f3e86d88-31cf-4892-822a-5792c04fdbe8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_(days)traveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_costend_yearend_monthstart_yearstart_monthstart_month_nameend_month_name
01London, UK2023-05-012023-05-087.0John Smith35.0MaleAmericanHotel1200Flight6002023520235MayMay
12Phuket, Thailand2023-06-152023-06-205.0Jane Doe28.0FemaleCanadianResort800Flight5002023620236JuneJune
23Bali, Indonesia2023-07-012023-07-087.0David Lee45.0MaleKoreanVilla1000Flight7002023720237JulyJuly
34New York, USA2023-08-152023-08-2914.0Sarah Johnson29.0FemaleBritishHotel2000Flight10002023820238AugustAugust
45Tokyo, Japan2023-09-102023-09-177.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train2002023920239SeptemberSeptember
............................................................
134135Rio de Janeiro, Brazil2023-08-012023-08-109.0Jose Perez37.0MaleBrazilianHostel2500Car20002023820238AugustAugust
135136Vancouver, Canada2023-08-152023-08-216.0Emma Wilson29.0FemaleCanadianHotel5000Airplane30002023820238AugustAugust
136137Bangkok, Thailand2023-09-012023-09-087.0Ryan Chen34.0MaleChineseHostel2000Train10002023920239SeptemberSeptember
137138Barcelona, Spain2023-09-152023-09-227.0Sofia Rodriguez25.0FemaleSpanishAirbnb6000Airplane25002023920239SeptemberSeptember
138139Auckland, New Zealand2023-10-012023-10-087.0William Brown39.0MaleNew ZealanderHotel7000Train2500202310202310OctoberOctober
\n", + "

139 rows × 19 columns

\n", + "
" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_(days) \\\n", + "0 1 London, UK 2023-05-01 2023-05-08 7.0 \n", + "1 2 Phuket, Thailand 2023-06-15 2023-06-20 5.0 \n", + "2 3 Bali, Indonesia 2023-07-01 2023-07-08 7.0 \n", + "3 4 New York, USA 2023-08-15 2023-08-29 14.0 \n", + "4 5 Tokyo, Japan 2023-09-10 2023-09-17 7.0 \n", + ".. ... ... ... ... ... \n", + "134 135 Rio de Janeiro, Brazil 2023-08-01 2023-08-10 9.0 \n", + "135 136 Vancouver, Canada 2023-08-15 2023-08-21 6.0 \n", + "136 137 Bangkok, Thailand 2023-09-01 2023-09-08 7.0 \n", + "137 138 Barcelona, Spain 2023-09-15 2023-09-22 7.0 \n", + "138 139 Auckland, New Zealand 2023-10-01 2023-10-08 7.0 \n", + "\n", + " traveler_name traveler_age traveler_gender traveler_nationality \\\n", + "0 John Smith 35.0 Male American \n", + "1 Jane Doe 28.0 Female Canadian \n", + "2 David Lee 45.0 Male Korean \n", + "3 Sarah Johnson 29.0 Female British \n", + "4 Kim Nguyen 26.0 Female Vietnamese \n", + ".. ... ... ... ... \n", + "134 Jose Perez 37.0 Male Brazilian \n", + "135 Emma Wilson 29.0 Female Canadian \n", + "136 Ryan Chen 34.0 Male Chinese \n", + "137 Sofia Rodriguez 25.0 Female Spanish \n", + "138 William Brown 39.0 Male New Zealander \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + ".. ... ... ... \n", + "134 Hostel 2500 Car \n", + "135 Hotel 5000 Airplane \n", + "136 Hostel 2000 Train \n", + "137 Airbnb 6000 Airplane \n", + "138 Hotel 7000 Train \n", + "\n", + " transportation_cost end_year end_month start_year start_month \\\n", + "0 600 2023 5 2023 5 \n", + "1 500 2023 6 2023 6 \n", + "2 700 2023 7 2023 7 \n", + "3 1000 2023 8 2023 8 \n", + "4 200 2023 9 2023 9 \n", + ".. ... ... ... ... ... \n", + "134 2000 2023 8 2023 8 \n", + "135 3000 2023 8 2023 8 \n", + "136 1000 2023 9 2023 9 \n", + "137 2500 2023 9 2023 9 \n", + "138 2500 2023 10 2023 10 \n", + "\n", + " start_month_name end_month_name \n", + "0 May May \n", + "1 June June \n", + "2 July July \n", + "3 August August \n", + "4 September September \n", + ".. ... ... \n", + "134 August August \n", + "135 August August \n", + "136 September September \n", + "137 September September \n", + "138 October October \n", + "\n", + "[139 rows x 19 columns]" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_safina_df" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "38d552e9-f5b1-443a-a4b9-ea1854ec554b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "--- Most Frequent Travel Durations (Top 10) ---\n", + "Duration (Days)\n", + "7 54\n", + "8 24\n", + "9 16\n", + "6 16\n", + "5 10\n", + "10 10\n", + "11 5\n", + "14 1\n", + "13 1\n", + "Name: Number of Trips, dtype: Int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH (REQUIRED TO FIX THE ERROR)\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function (Must be defined first) ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # Use the absolute path variable to load the file\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"Error: The file was still not found at the specified path.\")\n", + " print(f\"Please double-check that the file exists here: {travel_trip_data}\")\n", + " # Re-raising the error to stop execution gracefully\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 2. Prepare 'duration_(days)' Column ---\n", + "travel_trip_safina_df['duration_(days)'] = pd.to_numeric(\n", + " travel_trip_safina_df['duration_(days)'], \n", + " errors='coerce'\n", + ").astype('Int64')\n", + "\n", + "# --- 3. Analyze Duration Frequencies ---\n", + "duration_counts = travel_trip_safina_df['duration_(days)'].value_counts().sort_values(ascending=False)\n", + "duration_counts = duration_counts.rename_axis('Duration (Days)').rename('Number of Trips')\n", + "\n", + "# --- 4. Display the top 10 most frequent durations ---\n", + "print(\"\\n--- Most Frequent Travel Durations (Top 10) ---\")\n", + "print(duration_counts.head(10))\n", + "\n", + "# --- 5. Generate Plot (Optional) ---\n", + "top_10_durations = duration_counts.head(10)\n", + "plt.figure(figsize=(10, 6))\n", + "top_10_durations.plot(kind='bar', color='darkorange')\n", + "plt.title('Top 10 Most Frequent Travel Durations')\n", + "plt.xlabel('Duration (Days)')\n", + "plt.ylabel('Number of Trips')\n", + "plt.xticks(rotation=0)\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "990c780f-e60e-4875-a82f-7073d8cf9915", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "--- Month Travel Frequency (Sorted) ---\n", + "The month with the highest number of trips is at the top.\n", + "End Month\n", + "September 18\n", + "August 16\n", + "July 16\n", + "June 15\n", + "May 15\n", + "November 10\n", + "January 10\n", + "February 10\n", + "October 9\n", + "April 7\n", + "March 6\n", + "December 5\n", + "Name: Number of Trips, dtype: Int64\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT ***\n", + "# You must change the path below to match the exact location of the CSV file on your computer.\n", + "# Based on our previous conversation, this should be:\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function (Must be defined first) ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # Use the absolute path variable to load the file\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found at the specified path.\")\n", + " print(f\"Please check and correct the path variable: {travel_trip_data}\")\n", + " # Stopping execution if file is not found\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 2. Prepare 'end_month' Column ---\n", + "# Convert 'end_date' to datetime\n", + "travel_trip_safina_df['end_date'] = pd.to_datetime(travel_trip_safina_df['end_date'], errors='coerce')\n", + "\n", + "# Extract 'end_month' as a clean integer (1=Jan, 12=Dec)\n", + "travel_trip_safina_df['end_month'] = travel_trip_safina_df['end_date'].dt.month.astype('Int64')\n", + "\n", + "# --- 3. Analyze Month Frequencies ---\n", + "# Count the number of trips ending in each month and sort descending\n", + "month_counts = travel_trip_safina_df['end_month'].value_counts().sort_values(ascending=False)\n", + "\n", + "# --- 4. Map Month Numbers to Names for Readability ---\n", + "month_map = {\n", + " 1: 'January', 2: 'February', 3: 'March', 4: 'April', \n", + " 5: 'May', 6: 'June', 7: 'July', 8: 'August', \n", + " 9: 'September', 10: 'October', 11: 'November', 12: 'December'\n", + "}\n", + "\n", + "# Apply the mapping\n", + "month_counts.index = month_counts.index.map(month_map)\n", + "\n", + "# Clean up (remove 'NaN' index if present) and rename for display\n", + "if 'nan' in month_counts.index:\n", + " month_counts = month_counts.drop('nan')\n", + "month_counts = month_counts.rename_axis('End Month').rename('Number of Trips')\n", + "\n", + "# --- 5. Display the results ---\n", + "print(\"\\n--- Month Travel Frequency (Sorted) ---\")\n", + "print(\"The month with the highest number of trips is at the top.\")\n", + "print(month_counts)\n", + "\n", + "# --- 6. Generate Plot (Optional) ---\n", + "plt.figure(figsize=(10, 6))\n", + "month_counts.plot(kind='bar', color='teal')\n", + "plt.title('Travel Frequency by End Month (Sorted)')\n", + "plt.xlabel('Month')\n", + "plt.ylabel('Number of Trips')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.savefig('travel_frequency_by_month_final.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "36f7845a-027d-4d60-8578-88682c1018e3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "--- Most Frequent Travel Destinations (Top 10) ---\n", + " country city Number of Trips\n", + "5 Bali Bali 7\n", + "26 Japan Tokyo 7\n", + "16 France Paris 7\n", + "34 Paris Paris 7\n", + "22 Indonesia Bali 5\n", + "32 New York New York 5\n", + "38 Rome Rome 5\n", + "47 Sydney Sydney 5\n", + "53 Tokyo Tokyo 5\n", + "6 Bangkok Bangkok 4\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Please ensure this path is correct for your local machine.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # Use the absolute path variable to load the file\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found. Please check and correct the path variable: {travel_trip_data}\")\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 2. Split Destination into City and Country ---\n", + "travel_trip_safina_df['destination'] = travel_trip_safina_df['destination'].str.strip()\n", + "destination_split = travel_trip_safina_df['destination'].str.split(',\\s*', expand=True)\n", + "\n", + "travel_trip_safina_df['city'] = destination_split[0].str.strip()\n", + "travel_trip_safina_df['country'] = destination_split[1].fillna(destination_split[0]).str.strip() \n", + "\n", + "# --- 3. Analyze Destination Frequencies ---\n", + "# Group by both country and city to count the number of trips\n", + "destination_counts = travel_trip_safina_df.groupby(['country', 'city']).size().reset_index(name='Number of Trips')\n", + "\n", + "# **This is the critical line that completes the statement and fixes the SyntaxError**\n", + "destination_counts = destination_counts.sort_values(by='Number of Trips', ascending=False)\n", + "\n", + "# --- 4. Display the top 10 destinations ---\n", + "print(\"\\n--- Most Frequent Travel Destinations (Top 10) ---\")\n", + "print(destination_counts.head(10))\n", + "\n", + "# --- 5. Generate Plot (Optional) ---\n", + "top_10_destinations = destination_counts.head(10).copy()\n", + "top_10_destinations['Destination'] = top_10_destinations['city'] + ', ' + top_10_destinations['country']\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "plt.bar(top_10_destinations['Destination'], top_10_destinations['Number of Trips'], color='indigo')\n", + "plt.title('Top 10 Most Frequent Travel Destinations (City, Country)')\n", + "plt.xlabel('Destination')\n", + "plt.ylabel('Number of Trips')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a485c7ea-8895-48c9-8dce-c536a7e3c409", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "--- Top Travel Destinations (City, Country) for September ---\n", + " country city Number of Trips\n", + "12 Tokyo Tokyo 3\n", + "0 Bangkok Bangkok 2\n", + "2 Greece Athens 1\n", + "3 Japan Tokyo 1\n", + "4 Paris Paris 1\n", + "1 Brazil Brazil 1\n", + "5 Phnom Penh Phnom Penh 1\n", + "6 Phuket Phuket 1\n", + "8 Scotland Edinburgh 1\n", + "7 SA Cape Town 1\n", + "9 Spain Barcelona 1\n", + "10 Sydney Sydney 1\n", + "11 Thailand Bangkok 1\n", + "13 USA New York City 1\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Please ensure this path is correct for your local machine.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # Use the absolute path variable to load the file\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found. Please check and correct the path variable: {travel_trip_data}\")\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 2. Prepare Date Columns and Filter for September ---\n", + "# Convert 'start_date' to datetime\n", + "travel_trip_safina_df['start_date'] = pd.to_datetime(travel_trip_safina_df['start_date'], errors='coerce')\n", + "\n", + "# Extract 'start_month'\n", + "travel_trip_safina_df['start_month'] = travel_trip_safina_df['start_date'].dt.month.astype('Int64')\n", + "\n", + "# Filter for trips starting in September (Month 9)\n", + "september_trips_df = travel_trip_safina_df[travel_trip_safina_df['start_month'] == 9].copy()\n", + "\n", + "# --- 3. Clean and Split Destination ---\n", + "september_trips_df['destination'] = september_trips_df['destination'].str.strip()\n", + "destination_split = september_trips_df['destination'].str.split(',\\s*', expand=True)\n", + "\n", + "# Assign the split parts to new city and country columns\n", + "september_trips_df['city'] = destination_split[0].str.strip()\n", + "september_trips_df['country'] = destination_split[1].fillna(destination_split[0]).str.strip()\n", + "\n", + "# --- 4. Analyze Destination Frequencies for September (CREATES THE VARIABLE) ---\n", + "# Group by both country and city to count the number of trips\n", + "september_destination_counts = september_trips_df.groupby(['country', 'city']).size().reset_index(name='Number of Trips')\n", + "\n", + "# Sort the results in descending order\n", + "september_destination_counts = september_destination_counts.sort_values(by='Number of Trips', ascending=False)\n", + "\n", + "# --- 5. Display the results ---\n", + "print(\"\\n--- Top Travel Destinations (City, Country) for September ---\")\n", + "print(september_destination_counts)\n", + "\n", + "# --- 6. Generate Plot ---\n", + "# Preparing data for plotting\n", + "september_destination_counts['Destination'] = september_destination_counts['city'] + ', ' + september_destination_counts['country']\n", + "\n", + "plt.figure(figsize=(12, 7))\n", + "plt.bar(september_destination_counts['Destination'], september_destination_counts['Number of Trips'], color='forestgreen')\n", + "plt.title('Travel Destinations in September')\n", + "plt.xlabel('Destination')\n", + "plt.ylabel('Number of Trips')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "ab6a8aa1-fa8c-4e1a-8705-2c1fa2534860", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "Full details, including nationality, have been saved to 'seven_day_trip_details_with_nationality.csv'.\n", + "\n", + "--- Nationality Breakdown for 7-Day Trips ---\n", + "Traveler Nationality\n", + "American 8\n", + "Canadian 6\n", + "Korean 5\n", + "British 4\n", + "Spanish 4\n", + "Australian 3\n", + "Mexican 2\n", + "Brazilian 2\n", + "Vietnamese 2\n", + "Chinese 2\n", + "South Korean 2\n", + "Dutch 1\n", + "Emirati 1\n", + "Moroccan 1\n", + "Scottish 1\n", + "Italian 1\n", + "Japanese 1\n", + "Taiwanese 1\n", + "Taiwan 1\n", + "Brazil 1\n", + "Germany 1\n", + "Singapore 1\n", + "Italy 1\n", + "Greece 1\n", + "New Zealander 1\n", + "Name: Number of 7-Day Trips, dtype: int64\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Please ensure this path is correct for your local machine.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # Use the absolute path variable to load the file\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found. Please check and correct the path variable: {travel_trip_data}\")\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 2. Prepare 'duration_(days)' Column and Filter ---\n", + "# Convert duration to a nullable integer type\n", + "travel_trip_safina_df['duration_(days)'] = pd.to_numeric(\n", + " travel_trip_safina_df['duration_(days)'], \n", + " errors='coerce'\n", + ").astype('Int64')\n", + "\n", + "# Filter for all trips that lasted exactly 7 days\n", + "seven_day_trips = travel_trip_safina_df[\n", + " travel_trip_safina_df['duration_(days)'] == 7\n", + "].copy()\n", + "\n", + "# --- 3. Analyze Nationality Frequency ---\n", + "# Count the number of trips for each nationality\n", + "nationality_counts = seven_day_trips['traveler_nationality'].value_counts().sort_values(ascending=False)\n", + "\n", + "# Rename the series for clear display\n", + "nationality_counts = nationality_counts.rename_axis('Traveler Nationality').rename('Number of 7-Day Trips')\n", + "\n", + "# --- 4. Prepare and Save Full Details to CSV ---\n", + "details_df = seven_day_trips[['traveler_name', 'traveler_nationality', 'destination', 'duration_(days)']]\n", + "\n", + "output_file_full = 'seven_day_trip_details_with_nationality.csv'\n", + "details_df.to_csv(output_file_full, index=False)\n", + "print(f\"\\nFull details, including nationality, have been saved to '{output_file_full}'.\")\n", + "\n", + "# --- 5. Display the Breakdown ---\n", + "print(\"\\n--- Nationality Breakdown for 7-Day Trips ---\")\n", + "print(nationality_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "ae1ff22c-540b-4e8e-b0b0-403f3599501a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "--- 1. Most Frequent Traveling Nationality ---\n", + "The nationality that takes the most trips is: American (Total Trips: 24)\n", + "\n", + "--- 2. Top Destinations for American Travelers (Top 5) ---\n", + " country city Number of Trips\n", + " France Paris 4\n", + "New York New York 3\n", + " Paris Paris 3\n", + " Japan Tokyo 3\n", + " Mexico Cancun 2\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Please ensure this path is correct for your local machine.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # Use the absolute path variable to load the file\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found. Please check and correct the path variable: {travel_trip_data}\")\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- PART 1: Find the Most Frequent Traveling Nationality ---\n", + "# Calculate total trip counts by nationality\n", + "nationality_counts = travel_trip_safina_df['traveler_nationality'].value_counts()\n", + "top_nationality_name = nationality_counts.index[0]\n", + "top_nationality_trips = nationality_counts.iloc[0]\n", + "\n", + "# --- PART 2: Find Favorite Destinations for the Top Nationality ---\n", + "\n", + "# 1. Filter the DataFrame for the top nationality\n", + "top_nationality_df = travel_trip_safina_df[\n", + " travel_trip_safina_df['traveler_nationality'] == top_nationality_name\n", + "].copy()\n", + "\n", + "# 2. Clean and Split Destination for the filtered data\n", + "top_nationality_df['destination'] = top_nationality_df['destination'].str.strip()\n", + "destination_split = top_nationality_df['destination'].str.split(',\\s*', expand=True)\n", + "\n", + "top_nationality_df['city'] = destination_split[0].str.strip()\n", + "top_nationality_df['country'] = destination_split[1].fillna(destination_split[0]).str.strip() \n", + "\n", + "# 3. Analyze Destination Frequencies\n", + "destination_counts = top_nationality_df.groupby(['country', 'city']).size().reset_index(name='Number of Trips')\n", + "destination_counts = destination_counts.sort_values(by='Number of Trips', ascending=False)\n", + "\n", + "# --- 4. Display Results ---\n", + "print(f\"\\n--- 1. Most Frequent Traveling Nationality ---\")\n", + "print(f\"The nationality that takes the most trips is: {top_nationality_name} (Total Trips: {top_nationality_trips})\")\n", + "\n", + "print(f\"\\n--- 2. Top Destinations for {top_nationality_name} Travelers (Top 5) ---\")\n", + "print(destination_counts.head(5).to_string(index=False))\n", + "\n", + "# --- 5. Generate Plot (Optional) ---\n", + "top_5_destinations = destination_counts.head(5).copy()\n", + "top_5_destinations['Destination'] = top_5_destinations['city'] + ', ' + top_5_destinations['country']\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.bar(top_5_destinations['Destination'], top_5_destinations['Number of Trips'], color='darkcyan')\n", + "plt.title(f'Top 5 Destinations for {top_nationality_name} Travelers')\n", + "plt.xlabel('Destination')\n", + "plt.ylabel('Number of Trips')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.grid(axis='y', linestyle='--', alpha=0.7)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "6f7b9a3e-f748-4b98-a18a-b27d9fb7fd37", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "Unique transportation costs after cleaning:\n", + "['600' '500' '700' '1000' '200' '800' '1200' '100' '400' '150' '$400 '\n", + " '$700 ' '$150 ' '$800 ' '$100 ' '$600 ' '$80 ' '$500 ' '$300 ' '$50 '\n", + " '$120 ' '$75 ' '900' '50' '$200 ' '$250 ' '$20 ' '300' nan '800 USD'\n", + " '200 USD' '500 USD' '700 USD' '300 USD' '600 USD' '400 USD' '1000 USD'\n", + " '100 USD' '350 USD' '150 USD' '$1,200 ' '$900 ' '$1,500 ' '$1,000 ' '250'\n", + " '2500' '1500' '2000' '3000']\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Ensure this path is correct for your local machine.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function (Re-defined for completeness) ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- 2. Load Data and Define DataFrame (FIXES THE NAMEERROR) ---\n", + "try:\n", + " # This line defines the DataFrame and resolves the 'NameError'\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found. Please check and correct the path variable: {travel_trip_data}\")\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# -------------------------------------------------------------------\n", + "# --- 3. Accommodation Cost Cleaning (Your requested code) ---\n", + "# -------------------------------------------------------------------\n", + "\n", + "travel_trip_safina_df.loc[:, \"accommodation_cost\"] = (\n", + " travel_trip_safina_df[\"accommodation_cost\"]\n", + " .astype(str)\n", + " .str.strip()\n", + " .str.strip(\"$\")\n", + " .str.replace(\",\", \"\", regex=False)\n", + " .str.replace(\" USD\", \"\", regex=False)\n", + " .str.strip()\n", + ")\n", + " \n", + "travel_trip_safina_df.loc[:, \"accommodation_cost\"] = pd.to_numeric(\n", + " travel_trip_safina_df[\"accommodation_cost\"],\n", + " errors=\"coerce\"\n", + ")\n", + "\n", + "# Display unique transportation costs (which were previously cleaned)\n", + "print(\"\\nUnique transportation costs after cleaning:\")\n", + "print(travel_trip_safina_df[\"transportation_cost\"].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f5910f17-ac6a-404a-8444-6e48e0eeef94", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File loaded successfully.\n", + "\n", + "First 5 rows of cleaned columns (Dollar sign removed and converted to integer):\n", + " transportation_cost accommodation_cost\n", + "0 600 1200\n", + "1 500 800\n", + "2 700 1000\n", + "3 1000 2000\n", + "4 200 700\n", + "\n", + "Data Types after Integer Conversion:\n", + "\n", + "RangeIndex: 139 entries, 0 to 138\n", + "Data columns (total 2 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 transportation_cost 115 non-null object\n", + " 1 accommodation_cost 116 non-null object\n", + "dtypes: object(2)\n", + "memory usage: 2.3+ KB\n", + "None\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Please ensure this path is correct for your local machine.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- 2. Load Data and Clean Columns ---\n", + "try:\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found. Please check and correct the path variable: {travel_trip_data}\")\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 3. Cleaning Currency Columns (Removing $ and Converting to Int64) ---\n", + "\n", + "columns_to_clean = [\"transportation_cost\", \"accommodation_cost\"]\n", + "\n", + "for col in columns_to_clean:\n", + " # 3a. Clean currency symbols ($, comma, USD) and convert to numeric (float)\n", + " travel_trip_safina_df.loc[:, col] = (\n", + " travel_trip_safina_df[col]\n", + " .astype(str)\n", + " .str.strip()\n", + " .str.strip(\"$\") # <-- REMOVES THE DOLLAR SIGN\n", + " .str.replace(\",\", \"\", regex=False)\n", + " .str.replace(\" usd\", \"\", regex=False)\n", + " .str.strip()\n", + " )\n", + " travel_trip_safina_df.loc[:, col] = pd.to_numeric(\n", + " travel_trip_safina_df[col],\n", + " errors=\"coerce\"\n", + " )\n", + " \n", + " # 3b. Convert to Nullable Integer (Int64)\n", + " travel_trip_safina_df.loc[:, col] = travel_trip_safina_df[col].astype('Int64')\n", + "\n", + "# --- 4. Verify the Result ---\n", + "print(\"\\nFirst 5 rows of cleaned columns (Dollar sign removed and converted to integer):\")\n", + "print(travel_trip_safina_df[columns_to_clean].head())\n", + "print(\"\\nData Types after Integer Conversion:\")\n", + "print(travel_trip_safina_df[columns_to_clean].info())" + ] + }, + { + "cell_type": "code", +<<<<<<< HEAD + "execution_count": 2, + "id": "a5eb7f52-14ba-4944-bf74-f9e24b7373b4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ERROR: File not found at the specified path: D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'D:\\\\Ironhack\\\\Week4\\\\first_project\\\\data\\\\raw\\\\Travel details dataset.csv'", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 16\u001b[39m\n\u001b[32m 13\u001b[39m \u001b[38;5;66;03m# --- Load Data and Clean Columns ---\u001b[39;00m\n\u001b[32m 14\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 15\u001b[39m \u001b[38;5;66;03m# 2. FILE LOADING: This line must execute successfully!\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m travel_trip_safina_df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtravel_trip_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mISO-8859-1\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 17\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mFile loaded successfully.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 19\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m:\n", + "\u001b[36mFile \u001b[39m\u001b[32mD:\\Ironhack\\Week4\\first_project\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m 1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m 1014\u001b[39m dialect,\n\u001b[32m 1015\u001b[39m delimiter,\n\u001b[32m (...)\u001b[39m\u001b[32m 1022\u001b[39m dtype_backend=dtype_backend,\n\u001b[32m 1023\u001b[39m )\n\u001b[32m 1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mD:\\Ironhack\\Week4\\first_project\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m 617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m 619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m 623\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m parser\n", + "\u001b[36mFile \u001b[39m\u001b[32mD:\\Ironhack\\Week4\\first_project\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m 1617\u001b[39m \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m 1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mFile \u001b[39m\u001b[32mD:\\Ironhack\\Week4\\first_project\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m 1878\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m 1879\u001b[39m mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1881\u001b[39m \u001b[43m \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1882\u001b[39m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1883\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1884\u001b[39m \u001b[43m \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1885\u001b[39m \u001b[43m \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1886\u001b[39m \u001b[43m \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1887\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1888\u001b[39m \u001b[43m \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m 1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n", + "\u001b[36mFile \u001b[39m\u001b[32mD:\\Ironhack\\Week4\\first_project\\.venv\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m 868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m 869\u001b[39m \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m 870\u001b[39m \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m 871\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m 872\u001b[39m \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m 874\u001b[39m \u001b[43m \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 875\u001b[39m \u001b[43m \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 876\u001b[39m \u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 877\u001b[39m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 878\u001b[39m \u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m 879\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 880\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 881\u001b[39m \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m 882\u001b[39m handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n", + "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'D:\\\\Ironhack\\\\Week4\\\\first_project\\\\data\\\\raw\\\\Travel details dataset.csv'" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# 1. DEFINE THE ABSOLUTE FILE PATH\n", + "# *** IMPORTANT *** Verify this path on your file system.\n", + "travel_trip_data = r\"D:\\Ironhack\\Week4\\first_project\\data\\raw\\Travel details dataset.csv\"\n", + "\n", + "# --- Column Cleaning Function (from previous steps) ---\n", + "def clean_column(name):\n", + " \"\"\"Cleans column names.\"\"\"\n", + " return name.lower().strip().replace(\" \", \"_\").replace('', '') \n", + "\n", + "# --- Load Data and Clean Columns ---\n", + "try:\n", + " # 2. FILE LOADING: This line must execute successfully!\n", + " travel_trip_safina_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + " print(\"File loaded successfully.\")\n", + "\n", + "except FileNotFoundError:\n", + " print(f\"ERROR: File not found at the specified path: {travel_trip_data}\")\n", + " # Raise the error again to stop execution if the file still isn't found\n", + " raise\n", + "\n", + "travel_trip_safina_df.columns = [clean_column(col) for col in travel_trip_safina_df.columns] \n", + "\n", + "# --- 3. Destination Split (Fixing SyntaxWarning with r-string) ---\n", + "travel_trip_safina_df['destination'] = travel_trip_safina_df['destination'].str.strip()\n", + "\n", + "# NOTE: Using r',\\s*' fixes the SyntaxWarning\n", + "destination_split = travel_trip_safina_df['destination'].str.split(r',\\s*', expand=True) \n", + "\n", + "# Assign the split parts to new columns, cleaning whitespace\n", + "travel_trip_safina_df['city'] = destination_split[0].str.strip()\n", + "travel_trip_safina_df['country'] = destination_split[1].fillna(destination_split[0]).str.strip() \n", + "\n", + "print(\"\\nDestination columns successfully created.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9412447-b186-4842-911b-b182b4745d77", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1506fa9a-3f23-47f5-99bd-d9ade378d646", + "metadata": {}, +======= + "execution_count": null, + "id": "a5eb7f52-14ba-4944-bf74-f9e24b7373b4", + "metadata": {}, +>>>>>>> 707607303036efff73de59de812b0742c74b331f + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { +<<<<<<< HEAD + "display_name": "venv", + "language": "python", + "name": "venv" +======= + "display_name": "Python [conda env:base] *", + "language": "python", + "name": "conda-base-py" +>>>>>>> 707607303036efff73de59de812b0742c74b331f + }, + "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/traveler_trip_dataset_hoaithuong.ipynb b/notebooks/traveler_trip_dataset_hoaithuong.ipynb new file mode 100644 index 00000000..5c15042e --- /dev/null +++ b/notebooks/traveler_trip_dataset_hoaithuong.ipynb @@ -0,0 +1,2237 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 142, + "id": "eaa9e1ba-9051-4a18-8857-97b56f4b8140", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Trip IDDestinationStart dateEnd dateDuration (days)Traveler nameTraveler ageTraveler genderTraveler nationalityAccommodation typeAccommodation costTransportation typeTransportation cost
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
56Paris, France10/5/202310/10/20235.0Michael Brown42.0MaleAmericanHotel1500Flight800
67Sydney, Australia11/20/202311/30/202310.0Emily Davis33.0FemaleAustralianHostel500Flight1200
78Rio de Janeiro, Brazil1/5/20241/12/20247.0Lucas Santos25.0MaleBrazilianAirbnb900Flight600
89Amsterdam, Netherlands2/14/20242/21/20247.0Laura Janssen31.0FemaleDutchHotel1200Train200
910Dubai, United Arab Emirates3/10/20243/17/20247.0Mohammed Ali39.0MaleEmiratiResort2500Flight800
\n", + "
" + ], + "text/plain": [ + " Trip ID Destination Start date End date \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 \n", + "5 6 Paris, France 10/5/2023 10/10/2023 \n", + "6 7 Sydney, Australia 11/20/2023 11/30/2023 \n", + "7 8 Rio de Janeiro, Brazil 1/5/2024 1/12/2024 \n", + "8 9 Amsterdam, Netherlands 2/14/2024 2/21/2024 \n", + "9 10 Dubai, United Arab Emirates 3/10/2024 3/17/2024 \n", + "\n", + " Duration (days) Traveler name Traveler age Traveler gender \\\n", + "0 7.0 John Smith 35.0 Male \n", + "1 5.0 Jane Doe 28.0 Female \n", + "2 7.0 David Lee 45.0 Male \n", + "3 14.0 Sarah Johnson 29.0 Female \n", + "4 7.0 Kim Nguyen 26.0 Female \n", + "5 5.0 Michael Brown 42.0 Male \n", + "6 10.0 Emily Davis 33.0 Female \n", + "7 7.0 Lucas Santos 25.0 Male \n", + "8 7.0 Laura Janssen 31.0 Female \n", + "9 7.0 Mohammed Ali 39.0 Male \n", + "\n", + " Traveler nationality Accommodation type Accommodation cost \\\n", + "0 American Hotel 1200 \n", + "1 Canadian Resort 800 \n", + "2 Korean Villa 1000 \n", + "3 British Hotel 2000 \n", + "4 Vietnamese Airbnb 700 \n", + "5 American Hotel 1500 \n", + "6 Australian Hostel 500 \n", + "7 Brazilian Airbnb 900 \n", + "8 Dutch Hotel 1200 \n", + "9 Emirati Resort 2500 \n", + "\n", + " Transportation type Transportation cost \n", + "0 Flight 600 \n", + "1 Flight 500 \n", + "2 Flight 700 \n", + "3 Flight 1000 \n", + "4 Train 200 \n", + "5 Flight 800 \n", + "6 Flight 1200 \n", + "7 Flight 600 \n", + "8 Train 200 \n", + "9 Flight 800 " + ] + }, + "execution_count": 142, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "travel_trip_data = \"../data/raw/Travel details dataset.csv\"\n", + "travel_trip_hoaithuong_df = pd.read_csv(travel_trip_data, encoding='ISO-8859-1')\n", + "travel_trip_hoaithuong_df.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "id": "5215913a-b54e-4a6d-a07e-5e702c75ede4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(139, 13)" + ] + }, + "execution_count": 143, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "id": "d0ae93e3-d769-4e0b-b4b1-9a8f7dee6b9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Trip ID', 'Destination', 'Start date', 'End date',\n", + " 'Duration (days)', 'Traveler name', 'Traveler age', 'Traveler gender',\n", + " 'Traveler nationality', 'Accommodation type', 'Accommodation cost',\n", + " 'Transportation type', 'Transportation cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "f6b0a039-7cdc-461c-958f-4ee1634601be", + "metadata": {}, + "outputs": [], + "source": [ + "travel_trip_hoaithuong_df.columns = travel_trip_hoaithuong_df.columns.str.replace(\"\", \"\", regex=False).str.replace(r\"[()]\", \"\", regex=True).str.strip(\")\").str.replace(\" \",\"_\").str.lower()" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "6785b96f-411c-4f9e-82a6-d00dbc033aaf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['trip_id', 'destination', 'start_date', 'end_date', 'duration_days',\n", + " 'traveler_name', 'traveler_age', 'traveler_gender',\n", + " 'traveler_nationality', 'accommodation_type', 'accommodation_cost',\n", + " 'transportation_type', 'transportation_cost'],\n", + " dtype='object')" + ] + }, + "execution_count": 146, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "4835cbff-9092-476f-bd78-1ba752ecec33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
01London, UK5/1/20235/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
12Phuket, Thailand6/15/20236/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
23Bali, Indonesia7/1/20237/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
34New York, USA8/15/20238/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
45Tokyo, Japan9/10/20239/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
\n", + "
" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days \\\n", + "0 1 London, UK 5/1/2023 5/8/2023 7.0 \n", + "1 2 Phuket, Thailand 6/15/2023 6/20/2023 5.0 \n", + "2 3 Bali, Indonesia 7/1/2023 7/8/2023 7.0 \n", + "3 4 New York, USA 8/15/2023 8/29/2023 14.0 \n", + "4 5 Tokyo, Japan 9/10/2023 9/17/2023 7.0 \n", + "\n", + " traveler_name traveler_age traveler_gender traveler_nationality \\\n", + "0 John Smith 35.0 Male American \n", + "1 Jane Doe 28.0 Female Canadian \n", + "2 David Lee 45.0 Male Korean \n", + "3 Sarah Johnson 29.0 Female British \n", + "4 Kim Nguyen 26.0 Female Vietnamese \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + "\n", + " transportation_cost \n", + "0 600 \n", + "1 500 \n", + "2 700 \n", + "3 1000 \n", + "4 200 " + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "eacb80fb-b3a0-49a8-81b9-259bcb54911b", + "metadata": {}, + "outputs": [], + "source": [ + "# Clean columns start_date" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "d02c1da1-0f38-4260-92be-bb53a6a8791a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['5/1/2023', '6/15/2023', '7/1/2023', '8/15/2023', '9/10/2023',\n", + " '10/5/2023', '11/20/2023', '1/5/2024', '2/14/2024', '3/10/2024',\n", + " '4/1/2024', '5/15/2024', '6/10/2024', '7/1/2024', '8/20/2024',\n", + " '9/5/2024', '9/1/2023', '7/22/2023', '12/5/2023', '11/1/2023',\n", + " '9/15/2023', '12/22/2023', '8/1/2023', '10/20/2023', '5/10/2022',\n", + " '6/15/2022', '7/2/2022', '8/20/2022', '9/5/2022', '10/12/2022',\n", + " '11/8/2022', '1/5/2023', '2/14/2023', '3/23/2023', '4/19/2023',\n", + " '6/12/2022', '1/2/2023', '12/10/2022', '11/20/2022', '3/5/2023',\n", + " '8/18/2023', '9/15/2022', '7/10/2022', '6/20/2023', '10/10/2023',\n", + " '11/5/2023', '12/24/2023', '1/15/2024', '2/1/2024', '3/15/2024',\n", + " '4/5/2024', '5/10/2024', '6/20/2024', '7/15/2024', '7/12/2022',\n", + " '9/3/2022', '1/7/2023', '6/23/2023', '5/6/2024', '7/20/2024',\n", + " '9/8/2024', '2/14/2025', '5/21/2025', nan, '8/5/2022', '1/1/2023',\n", + " '4/15/2023', '6/7/2023', '11/12/2023', '2/5/2024', '1/1/2025',\n", + " '4/15/2025', '6/15/2021', '7/1/2021', '8/10/2021', '9/1/2021',\n", + " '10/15/2021', '11/20/2021', '1/1/2022', '2/14/2022', '3/10/2022',\n", + " '4/15/2022', '5/1/2022', '9/1/2022', '11/23/2022', '5/8/2023',\n", + " '8/20/2023', '1/6/2024', '4/3/2024', '7/22/2024', '10/10/2024',\n", + " '5/15/2022', '6/20/2022', '8/12/2022', '7/1/2022', '6/10/2022',\n", + " '7/15/2022', '8/25/2022', '9/10/2022', '2/5/2022', '3/15/2022',\n", + " '7/20/2022', '8/8/2022', '9/20/2022', '10/5/2022', '11/11/2022',\n", + " '12/24/2022', '2/10/2023', '5/15/2023', '6/1/2023', '7/15/2023',\n", + " '10/1/2023'], dtype=object)" + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['start_date'].unique()\n", + "#extract the list of unique values" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "ce61b023-32f0-4052-b222-3e0dce231312", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('O')" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['start_date'].dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "146c5905-e3ef-435a-a341-603a057d1b58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days traveler_name \\\n", + "71 72 NaN NaN NaN NaN NaN \n", + "127 128 NaN NaN NaN NaN NaN \n", + "\n", + " traveler_age traveler_gender traveler_nationality accommodation_type \\\n", + "71 NaN NaN NaN NaN \n", + "127 NaN NaN NaN NaN \n", + "\n", + " accommodation_cost transportation_type transportation_cost \n", + "71 NaN NaN NaN \n", + "127 NaN NaN NaN " + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df[travel_trip_hoaithuong_df['start_date'].isna()]" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "caf55e0d-37c1-4211-8978-43f323f88eb7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(2)" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df[\"start_date\"].isna().sum()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "14139d28-be33-449f-9b2b-6c1266dad526", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "111" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df[\"start_date\"].nunique()" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "622ecee8-8cc1-495e-a9d2-4e3c2fb6b05d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 2023-05-01\n", + "1 2023-06-15\n", + "2 2023-07-01\n", + "3 2023-08-15\n", + "4 2023-09-10\n", + " ... \n", + "134 2023-08-01\n", + "135 2023-08-15\n", + "136 2023-09-01\n", + "137 2023-09-15\n", + "138 2023-10-01\n", + "Name: start_date, Length: 139, dtype: datetime64[ns]\n" + ] + } + ], + "source": [ + "travel_trip_hoaithuong_df['start_date'] = pd.to_datetime(travel_trip_hoaithuong_df['start_date'], format='%m/%d/%Y')\n", + "print(travel_trip_hoaithuong_df['start_date'])" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "9517424f-c607-4eca-a11b-18de715df887", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " start_date\n", + "0 2023-05\n", + "1 2023-06\n", + "2 2023-07\n", + "3 2023-08\n", + "4 2023-09\n" + ] + } + ], + "source": [ + "# Convert the 'end_date' column to datetime first (if not already done)\n", + "travel_trip_hoaithuong_df['start_date'] = pd.to_datetime(\n", + " travel_trip_hoaithuong_df['start_date'], \n", + " errors='coerce'\n", + ")\n", + "\n", + "# Use .dt.strftime('%Y-%m') to format the date to Year and Month only.\n", + "travel_trip_hoaithuong_df['start_date'] = travel_trip_hoaithuong_df['start_date'].dt.strftime('%Y-%m')\n", + "\n", + "# Optional: Display the new data type and values\n", + "print(travel_trip_hoaithuong_df[['start_date']].head())" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "id": "6c20800d-1a44-4695-a500-70bbaf5f1399", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_cost
01London, UK2023-055/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600
12Phuket, Thailand2023-066/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500
23Bali, Indonesia2023-077/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700
34New York, USA2023-088/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000
45Tokyo, Japan2023-099/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200
..........................................
134135Rio de Janeiro, Brazil2023-088/10/20239.0Jose Perez37.0MaleBrazilianHostel2500Car2000
135136Vancouver, Canada2023-088/21/20236.0Emma Wilson29.0FemaleCanadianHotel5000Airplane3000
136137Bangkok, Thailand2023-099/8/20237.0Ryan Chen34.0MaleChineseHostel2000Train1000
137138Barcelona, Spain2023-099/22/20237.0Sofia Rodriguez25.0FemaleSpanishAirbnb6000Airplane2500
138139Auckland, New Zealand2023-1010/8/20237.0William Brown39.0MaleNew ZealanderHotel7000Train2500
\n", + "

139 rows × 13 columns

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" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days \\\n", + "0 1 London, UK 2023-05 5/8/2023 7.0 \n", + "1 2 Phuket, Thailand 2023-06 6/20/2023 5.0 \n", + "2 3 Bali, Indonesia 2023-07 7/8/2023 7.0 \n", + "3 4 New York, USA 2023-08 8/29/2023 14.0 \n", + "4 5 Tokyo, Japan 2023-09 9/17/2023 7.0 \n", + ".. ... ... ... ... ... \n", + "134 135 Rio de Janeiro, Brazil 2023-08 8/10/2023 9.0 \n", + "135 136 Vancouver, Canada 2023-08 8/21/2023 6.0 \n", + "136 137 Bangkok, Thailand 2023-09 9/8/2023 7.0 \n", + "137 138 Barcelona, Spain 2023-09 9/22/2023 7.0 \n", + "138 139 Auckland, New Zealand 2023-10 10/8/2023 7.0 \n", + "\n", + " traveler_name traveler_age traveler_gender traveler_nationality \\\n", + "0 John Smith 35.0 Male American \n", + "1 Jane Doe 28.0 Female Canadian \n", + "2 David Lee 45.0 Male Korean \n", + "3 Sarah Johnson 29.0 Female British \n", + "4 Kim Nguyen 26.0 Female Vietnamese \n", + ".. ... ... ... ... \n", + "134 Jose Perez 37.0 Male Brazilian \n", + "135 Emma Wilson 29.0 Female Canadian \n", + "136 Ryan Chen 34.0 Male Chinese \n", + "137 Sofia Rodriguez 25.0 Female Spanish \n", + "138 William Brown 39.0 Male New Zealander \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + ".. ... ... ... \n", + "134 Hostel 2500 Car \n", + "135 Hotel 5000 Airplane \n", + "136 Hostel 2000 Train \n", + "137 Airbnb 6000 Airplane \n", + "138 Hotel 7000 Train \n", + "\n", + " transportation_cost \n", + "0 600 \n", + "1 500 \n", + "2 700 \n", + "3 1000 \n", + "4 200 \n", + ".. ... \n", + "134 2000 \n", + "135 3000 \n", + "136 1000 \n", + "137 2500 \n", + "138 2500 \n", + "\n", + "[139 rows x 13 columns]" + ] + }, + "execution_count": 156, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " travel_trip_hoaithuong_df" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "9b426ca7-b105-4e6c-a965-a3d2f9e1794d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 2023-05-01\n", + "1 2023-06-01\n", + "2 2023-07-01\n", + "3 2023-08-01\n", + "4 2023-09-01\n", + " ... \n", + "134 2023-08-01\n", + "135 2023-08-01\n", + "136 2023-09-01\n", + "137 2023-09-01\n", + "138 2023-10-01\n", + "Name: start_date, Length: 139, dtype: datetime64[ns]" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['start_date'] = pd.to_datetime(travel_trip_hoaithuong_df['start_date'], errors='coerce')\n", + "travel_trip_hoaithuong_df['start_date']" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "8ed625ad-7d44-4c07-a604-8d2e7f84843c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 May\n", + "1 June\n", + "2 July\n", + "3 August\n", + "4 September\n", + " ... \n", + "134 August\n", + "135 August\n", + "136 September\n", + "137 September\n", + "138 October\n", + "Name: start_month, Length: 139, dtype: object" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['start_month'] = travel_trip_hoaithuong_df['start_date'].dt.strftime('%B')\n", + "travel_trip_hoaithuong_df['start_month']" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "f450c9cf-f145-4683-be6d-966e7b034201", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_coststart_month
01London, UK2023-05-015/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600May
12Phuket, Thailand2023-06-016/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500June
23Bali, Indonesia2023-07-017/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700July
34New York, USA2023-08-018/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000August
45Tokyo, Japan2023-09-019/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200September
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trip_iddestinationstart_dateend_dateduration_daystraveler_nametraveler_agetraveler_gendertraveler_nationalityaccommodation_typeaccommodation_costtransportation_typetransportation_coststart_monthstart_year
01London, UK2023-05-015/8/20237.0John Smith35.0MaleAmericanHotel1200Flight600May2023
12Phuket, Thailand2023-06-016/20/20235.0Jane Doe28.0FemaleCanadianResort800Flight500June2023
23Bali, Indonesia2023-07-017/8/20237.0David Lee45.0MaleKoreanVilla1000Flight700July2023
34New York, USA2023-08-018/29/202314.0Sarah Johnson29.0FemaleBritishHotel2000Flight1000August2023
45Tokyo, Japan2023-09-019/17/20237.0Kim Nguyen26.0FemaleVietnameseAirbnb700Train200September2023
\n", + "
" + ], + "text/plain": [ + " trip_id destination start_date end_date duration_days \\\n", + "0 1 London, UK 2023-05-01 5/8/2023 7.0 \n", + "1 2 Phuket, Thailand 2023-06-01 6/20/2023 5.0 \n", + "2 3 Bali, Indonesia 2023-07-01 7/8/2023 7.0 \n", + "3 4 New York, USA 2023-08-01 8/29/2023 14.0 \n", + "4 5 Tokyo, Japan 2023-09-01 9/17/2023 7.0 \n", + "\n", + " traveler_name traveler_age traveler_gender traveler_nationality \\\n", + "0 John Smith 35.0 Male American \n", + "1 Jane Doe 28.0 Female Canadian \n", + "2 David Lee 45.0 Male Korean \n", + "3 Sarah Johnson 29.0 Female British \n", + "4 Kim Nguyen 26.0 Female Vietnamese \n", + "\n", + " accommodation_type accommodation_cost transportation_type \\\n", + "0 Hotel 1200 Flight \n", + "1 Resort 800 Flight \n", + "2 Villa 1000 Flight \n", + "3 Hotel 2000 Flight \n", + "4 Airbnb 700 Train \n", + "\n", + " transportation_cost start_month start_year \n", + "0 600 May 2023 \n", + "1 500 June 2023 \n", + "2 700 July 2023 \n", + "3 1000 August 2023 \n", + "4 200 September 2023 " + ] + }, + "execution_count": 160, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df[\"start_date\"] = pd.to_datetime(\n", + " travel_trip_hoaithuong_df[\"start_date\"], errors=\"coerce\"\n", + ")\n", + "\n", + "travel_trip_hoaithuong_df[\"start_year\"] = (\n", + " travel_trip_hoaithuong_df[\"start_date\"].dt.year.astype(\"Int64\")\n", + ")\n", + "travel_trip_hoaithuong_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "id": "2cd814fb-da83-4583-9f34-97372e41ee74", + "metadata": {}, + "outputs": [], + "source": [ + "#Clean columns: traveler_nationality" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "id": "d36774ee-16ad-4b7c-8b92-5b4ac81c589f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['American', 'Canadian', 'Korean', 'British', 'Vietnamese',\n", + " 'Australian', 'Brazilian', 'Dutch', 'Emirati', 'Mexican',\n", + " 'Spanish', 'Chinese', 'German', 'Moroccan', 'Scottish', 'Japanese',\n", + " 'Italian', 'Indian', 'South Korean', 'French', nan,\n", + " 'South African', 'Taiwanese', 'Indonesian', 'USA', 'Canada',\n", + " 'South Korea', 'UK', 'China', 'Taiwan', 'Japan', 'Spain', 'Brazil',\n", + " 'Germany', 'Hong Kong', 'United Kingdom', 'Singapore', 'Italy',\n", + " 'Greece', 'United Arab Emirates', 'Cambodia', 'New Zealander'],\n", + " dtype=object)" + ] + }, + "execution_count": 162, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " travel_trip_hoaithuong_df['traveler_nationality'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "id": "2e165506-1ee0-44c9-91a5-907d9e6884bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 American\n", + "1 Canadian\n", + "2 Korean\n", + "3 British\n", + "4 Vietnamese\n", + " ... \n", + "134 Brazilian\n", + "135 Canadian\n", + "136 Chinese\n", + "137 Spanish\n", + "138 New Zealander\n", + "Name: traveler_nationality, Length: 139, dtype: object" + ] + }, + "execution_count": 163, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_nationality'].str.strip().str.lower().str.title()" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "2ea7355a-8df1-4bc6-b29b-e893e1221394", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_nationality\n", + "American 24\n", + "Korean 13\n", + "British 12\n", + "Canadian 9\n", + "Australian 8\n", + "Spanish 7\n", + "Chinese 7\n", + "Italian 4\n", + "Brazilian 4\n", + "Indian 4\n", + "Vietnamese 3\n", + "South Korea 3\n", + "South Korean 3\n", + "Taiwan 2\n", + "Canada 2\n", + "USA 2\n", + "South African 2\n", + "Japanese 2\n", + "Mexican 2\n", + "Emirati 2\n", + "Dutch 2\n", + "Brazil 1\n", + "Cambodia 1\n", + "United Arab Emirates 1\n", + "Greece 1\n", + "Italy 1\n", + "Singapore 1\n", + "United Kingdom 1\n", + "Hong Kong 1\n", + "Germany 1\n", + "Japan 1\n", + "Spain 1\n", + "Scottish 1\n", + "China 1\n", + "UK 1\n", + "German 1\n", + "Indonesian 1\n", + "Taiwanese 1\n", + "Moroccan 1\n", + "French 1\n", + "New Zealander 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 164, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_nationality'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "id": "b4b7ab06-6da3-40c9-9bbf-62a0d321cd76", + "metadata": {}, + "outputs": [], + "source": [ + "mapping = {\n", + " 'American': 'United States',\n", + " 'USA': 'United States',\n", + " 'Canada': 'Canada',\n", + " 'Canadian': 'Canada',\n", + " 'UK': 'United Kingdom',\n", + " 'United Kingdom': 'United Kingdom',\n", + " 'British': 'United Kingdom',\n", + " 'Scottish': 'United Kingdom',\n", + " 'Korean': 'South Korea',\n", + " 'South Korea': 'South Korea',\n", + " 'South Korean': 'South Korea',\n", + " 'China': 'China',\n", + " 'Chinese': 'China',\n", + " 'Italy': 'Italy',\n", + " 'Italian': 'Italy',\n", + " 'Brazil': 'Brazil',\n", + " 'Brazilian': 'Brazil',\n", + " 'Emirati': 'United Arab Emirates',\n", + " 'United Arab Emirates': 'United Arab Emirates',\n", + " 'Taiwan': 'Taiwan',\n", + " 'Taiwanese': 'Taiwan',\n", + " 'Germany': 'Germany',\n", + " 'German': 'Germany',\n", + " 'Spain': 'Spain',\n", + " 'Spanish': 'Spain',\n", + " 'Japan': 'Japan',\n", + " 'Japanese': 'Japan'\n", + "}\n", + "\n", + "travel_trip_hoaithuong_df['traveler_nationality_clean'] = travel_trip_hoaithuong_df['traveler_nationality'].replace(mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "id": "6044065c-4763-4f33-8e14-4d404c9de96c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_nationality_clean\n", + "United States 26\n", + "South Korea 19\n", + "United Kingdom 15\n", + "Canada 11\n", + "China 8\n", + "Spain 8\n", + "Australian 8\n", + "Brazil 5\n", + "Italy 5\n", + "Indian 4\n", + "United Arab Emirates 3\n", + "Vietnamese 3\n", + "Japan 3\n", + "Taiwan 3\n", + "Dutch 2\n", + "Mexican 2\n", + "Germany 2\n", + "South African 2\n", + "Singapore 1\n", + "Cambodia 1\n", + "Greece 1\n", + "Moroccan 1\n", + "Hong Kong 1\n", + "Indonesian 1\n", + "French 1\n", + "New Zealander 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 166, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_nationality_clean'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "id": "f473b554-a78f-4431-8c5e-06980976e343", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('O')" + ] + }, + "execution_count": 167, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_nationality'].dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "5bfed24f-5b63-4330-9e2d-7d6edfce67a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 American\n", + "1 Canadian\n", + "2 Korean\n", + "3 British\n", + "4 Vietnamese\n", + " ... \n", + "134 Brazilian\n", + "135 Canadian\n", + "136 Chinese\n", + "137 Spanish\n", + "138 New Zealander\n", + "Name: traveler_nationality, Length: 139, dtype: string" + ] + }, + "execution_count": 168, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_nationality'].astype('string')" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "9c363536-ca86-417a-aa9a-59b37db0119c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(2)" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_nationality'].isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "id": "85f9faa5-c85d-48b3-b0b0-3dcea796fdaf", + "metadata": {}, + "outputs": [], + "source": [ + "# Clean columns traveler_gender" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "id": "68ceab2f-3ae5-425d-ba28-704a44289be7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Male', 'Female', nan], dtype=object)" + ] + }, + "execution_count": 171, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_gender'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "id": "0e794a05-5879-4857-84f9-1674d4212a1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_gender\n", + "Female 70\n", + "Male 67\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 172, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_gender'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 173, + "id": "e87e63d4-c180-4263-820b-abf80afe31e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.int64(2)" + ] + }, + "execution_count": 173, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_gender'].isna().sum() " + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "id": "8a073849-ae87-44aa-bd84-3916171634f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 Male\n", + "1 Female\n", + "2 Male\n", + "3 Female\n", + "4 Female\n", + " ... \n", + "134 Male\n", + "135 Female\n", + "136 Male\n", + "137 Female\n", + "138 Male\n", + "Name: traveler_gender, Length: 139, dtype: object" + ] + }, + "execution_count": 174, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['traveler_gender'].str.strip().str.title()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "id": "7a432295-fa6c-474e-b3ad-98835f8dc5db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "traveler_gender\n", + "Female 70\n", + "Male 67\n", + "Unknown 2\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 175, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df[\"traveler_gender\"] = (\n", + " travel_trip_hoaithuong_df[\"traveler_gender\"].fillna(\"Unknown\")\n", + ")\n", + "travel_trip_hoaithuong_df['traveler_gender'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb6b32cd-c090-4b88-97a6-f9c8d647e7b7", + "metadata": {}, + "outputs": [], + "source": [ + "# Clean columns accommodation_type\t" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "id": "d5a09621-5c8a-4ce7-8226-4551ff7c1d63", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Hotel', 'Resort', 'Villa', 'Airbnb', 'Hostel', 'Riad',\n", + " 'Vacation rental', nan, 'Guesthouse'], dtype=object)" + ] + }, + "execution_count": 176, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['accommodation_type'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "id": "0254376d-d53b-4c6a-9af3-ca28eb0e9362", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "accommodation_type\n", + "Hotel 60\n", + "Airbnb 30\n", + "Hostel 24\n", + "Resort 14\n", + "Villa 4\n", + "Vacation rental 3\n", + "Unknown 2\n", + "Riad 1\n", + "Guesthouse 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 182, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['accommodation_type'] = travel_trip_hoaithuong_df['accommodation_type'].fillna('Unknown')\n", + "travel_trip_hoaithuong_df['accommodation_type'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "9984dfd6-d64f-4f42-b81f-98c9348fc02a", + "metadata": {}, + "outputs": [], + "source": [ + "mapping = {\n", + " 'Riad': 'Villa',\n", + " 'Guesthouse': 'Vacation rental'\n", + "}\n", + "travel_trip_hoaithuong_df['accommodation_type'] = travel_trip_hoaithuong_df['accommodation_type'].replace(mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "7bcd31a9-b3b5-449d-bc05-f81d548a16c5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "accommodation_type\n", + "Hotel 60\n", + "Airbnb 30\n", + "Hostel 24\n", + "Resort 14\n", + "Villa 5\n", + "Vacation rental 4\n", + "Unknown 2\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 193, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['accommodation_type'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9a0312b-3d0f-4561-bd4c-7591e903ce7d", + "metadata": {}, + "outputs": [], + "source": [ + "# Clean columns accommodation_cost" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "id": "11f5f892-2539-47cf-887f-d895e51e5fdb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('O')" + ] + }, + "execution_count": 183, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['accommodation_cost'].dtype" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "962d2623-d149-48b2-9316-25c10187073f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 137\n", + "unique 53\n", + "top 1200\n", + "freq 7\n", + "Name: accommodation_cost, dtype: object" + ] + }, + "execution_count": 184, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df['accommodation_cost'].describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "id": "b0213cdd-019e-4c30-8243-5577bcb8945a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1200.0\n", + "1 800.0\n", + "2 1000.0\n", + "3 2000.0\n", + "4 700.0\n", + " ... \n", + "134 2500.0\n", + "135 5000.0\n", + "136 2000.0\n", + "137 6000.0\n", + "138 7000.0\n", + "Name: accommodation_cost, Length: 139, dtype: float64" + ] + }, + "execution_count": 188, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "median_cost = travel_trip_hoaithuong_df['accommodation_cost'].median()\n", + "travel_trip_hoaithuong_df['accommodation_cost'] = travel_trip_hoaithuong_df['accommodation_cost'].fillna(median_cost)\n", + "travel_trip_hoaithuong_df['accommodation_cost']" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "id": "b440a948-399f-44b0-a607-732a6cd4257e", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/63/sqz7vhsn42v0_qx7gj0dnxfr0000gn/T/ipykernel_31891/1662941867.py:1: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1200.0' '800.0' '1000.0' '2000.0' '700.0' '1500.0' '500.0' '900.0'\n", + " '1200.0' '2500.0' '1000.0' '800.0' '3000.0' '1400.0' '600.0' '900.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1400.0' '800.0'\n", + " '500.0' '2200.0' '1200.0' '900.0' '600.0' '1500.0' '500.0' '400.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1200.0' '400.0' '800.0' '1500.0'\n", + " '900.0' '2200.0' '1100.0' '1000.0' '300.0' '1300.0' '1800.0' '1100.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1100.0' '100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0' '1100.0'\n", + " '1100.0' '1000.0' '800.0' '1200.0' '1500.0' '300.0' '900.0' '2000.0'\n", + " '1100.0' '1000.0' '1500.0' '200.0' '1000.0' '1200.0' '800.0' '900.0'\n", + " '500.0' '1300.0' '700.0' '1200.0' '900.0' '400.0' '800.0' '1100.0'\n", + " '5000.0' '7000.0' '3000.0' '6000.0' '4000.0' '8000.0' '2500.0' '5000.0'\n", + " '2000.0' '6000.0' '7000.0']' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n", + " travel_trip_hoaithuong_df.loc[:, \"accommodation_cost\"] = (\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1200.0, 800.0, 1000.0, 2000.0, 700.0, 1500.0, 500.0, 900.0, 2500.0,\n", + " 3000.0, 1400.0, 600.0, 1100.0, 2200.0, 400.0, 300.0, 1300.0,\n", + " 1800.0, 100.0, 200.0, 5000.0, 7000.0, 6000.0, 4000.0, 8000.0],\n", + " dtype=object)" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "travel_trip_hoaithuong_df.loc[:, \"accommodation_cost\"] = (\n", + " travel_trip_hoaithuong_df[\"accommodation_cost\"]\n", + " .astype(str) # in case there are numbers/NaN in there\n", + " .str.strip()\n", + " .str.strip(\"$\")\n", + " .str.replace(\",\", \"\", regex=False)\n", + " .str.replace(\" USD\", \"\", regex=False)\n", + " .str.strip()\n", + ")\n", + "\n", + "travel_trip_hoaithuong_df.loc[:, \"accommodation_cost\"] = pd.to_numeric(\n", + " travel_trip_hoaithuong_df[\"accommodation_cost\"],\n", + " errors=\"coerce\"\n", + ")\n", + "\n", + "travel_trip_hoaithuong_df.accommodation_cost.unique()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e70cd09b-adc6-425f-be9e-85d231377d23", + "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": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/slides/Hogwarts_compressed.pdf b/slides/Hogwarts_compressed.pdf new file mode 100644 index 00000000..776250b5 Binary files /dev/null and b/slides/Hogwarts_compressed.pdf differ diff --git a/slides/Hogwarts_final.pdf b/slides/Hogwarts_final.pdf new file mode 100644 index 00000000..9a7ce364 Binary files /dev/null and b/slides/Hogwarts_final.pdf differ diff --git a/slides/Hogwarts_final_to_present.pdf b/slides/Hogwarts_final_to_present.pdf new file mode 100644 index 00000000..b5ae7d79 Binary files /dev/null and b/slides/Hogwarts_final_to_present.pdf differ