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206 changes: 206 additions & 0 deletions practice/PD1.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "681405ed",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4320e7b8",
"metadata": {},
"outputs": [],
"source": [
"\"\"\"\n",
"#DataFrame:\n",
" main data struct in Pandas\n",
" a table w/many funcs.\n",
"\n",
"\"\"\"\n",
"\n",
"df=pd.DataFrame([[1,2,3],\n",
" [4,5,6],\n",
" [7,8,9]], \n",
" columns=[\"A\", \"B\", \"C\"],#Adds labels to the columns\n",
" index=[\"a\", \"b\", \"c\"]) #labels to rows"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6f9159ef",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" A B C\n",
"a 1 2 3\n",
"b 4 5 6\n",
"c 7 8 9\n"
]
}
],
"source": [
"print(df.head())#first five rows (here there are only three)\n",
"\n",
"\"\"\"\n",
"Outout w/o columns & indices defnd.:\n",
" 0 1 2\n",
"0 1 2 3\n",
"1 4 5 6\n",
"2 7 8 9\n",
"\n",
"Outout w/columns defnd.:\n",
" A B C\n",
"0 1 2 3\n",
"1 4 5 6\n",
"2 7 8 9\n",
"\n",
"Outout w/indices defnd.:\n",
" A B C\n",
"a 1 2 3\n",
"b 4 5 6\n",
"c 7 8 9\n",
"\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f34c9950",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Index: 3 entries, a to c\n",
"Data columns (total 3 columns):\n",
" # Column Non-Null Count Dtype\n",
"--- ------ -------------- -----\n",
" 0 A 3 non-null int64\n",
" 1 B 3 non-null int64\n",
" 2 C 3 non-null int64\n",
"dtypes: int64(3)\n",
"memory usage: 96.0+ bytes\n",
"None\n"
]
}
],
"source": [
"print(df.info())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0a140f8c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" A B C\n",
"count 3.0 3.0 3.0\n",
"mean 4.0 5.0 6.0\n",
"std 3.0 3.0 3.0\n",
"min 1.0 2.0 3.0\n",
"25% 2.5 3.5 4.5\n",
"50% 4.0 5.0 6.0\n",
"75% 5.5 6.5 7.5\n",
"max 7.0 8.0 9.0\n"
]
}
],
"source": [
"print(f\"\\n{df.describe()}\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "905150e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"A 3\n",
"B 3\n",
"C 3\n",
"dtype: int64\n",
"\n",
"3\n"
]
}
],
"source": [
"print(df.nunique())#entire DF\n",
"print(f\"\\n{df[\"A\"].nunique()}\")#partclr col"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "42e927bf",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 3)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78326e0f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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