927 lines
150 KiB
Plaintext
927 lines
150 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"# загрузка данных\n",
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"df = pd.read_csv(\"data/students_education.csv\")\n",
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"# сохранение данных\n",
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"df.to_csv(\"lab1.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 1205 entries, 0 to 1204\n",
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"Data columns (total 11 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Education Level 1205 non-null object\n",
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" 1 Institution Type 1205 non-null object\n",
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" 2 Gender 1205 non-null object\n",
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" 3 Age 1205 non-null int64 \n",
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" 4 Device 1204 non-null object\n",
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" 5 IT Student 1205 non-null object\n",
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" 6 Location 1205 non-null object\n",
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" 7 Financial Condition 1201 non-null object\n",
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" 8 Internet Type 1204 non-null object\n",
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" 9 Network Type 1205 non-null object\n",
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" 10 Flexibility Level 1205 non-null object\n",
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"dtypes: int64(1), object(10)\n",
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"memory usage: 103.7+ KB\n"
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]
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}
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],
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"source": [
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"# получение сведений о датафрейме\n",
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Education Level object\n",
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"Institution Type object\n",
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"Gender object\n",
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"Age int64\n",
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"Device object\n",
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"IT Student object\n",
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"Location object\n",
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"Financial Condition object\n",
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"Internet Type object\n",
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"Network Type object\n",
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"Flexibility Level object\n",
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"dtype: object"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.dtypes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0 University\n",
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"1 University\n",
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"2 College\n",
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"3 School\n",
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"4 School\n",
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" ... \n",
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"95 University\n",
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"96 School\n",
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"97 School\n",
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"98 University\n",
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"99 College\n",
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"Name: Education Level, Length: 100, dtype: object\n"
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]
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}
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],
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"source": [
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"# вывод первых 100 строк из столбца Education Level\n",
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"education = df.iloc[0:100, 0]\n",
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"print(education)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Education Level Institution Type Gender Age Device IT Student \\\n",
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"54 School Private Male 9 Mobile No \n",
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"55 School Private Female 9 Mobile No \n",
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"1155 School Private Female 9 Mobile No \n",
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"886 School Private Female 9 Mobile No \n",
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"916 School Private Female 9 Mobile No \n",
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"... ... ... ... ... ... ... \n",
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"1157 University Public Male 27 Computer Yes \n",
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"714 University Public Female 27 Mobile No \n",
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"717 University Public Female 27 Mobile No \n",
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"16 University Public Female 27 Computer Yes \n",
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"1190 University Private Male 27 Mobile Yes \n",
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"\n",
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" Location Financial Condition Internet Type Network Type Flexibility Level \n",
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"54 Town Poor Mobile Data 4G Low \n",
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"55 Town Mid Mobile Data 4G Moderate \n",
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"1155 Town Poor Mobile Data 4G Moderate \n",
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"886 Town Poor Mobile Data 4G Moderate \n",
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"916 Town Mid Mobile Data 4G Moderate \n",
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"... ... ... ... ... ... \n",
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"1157 Town Rich Wifi 4G High \n",
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"714 Town Mid Wifi 4G Low \n",
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"717 Town Mid Mobile Data 4G Low \n",
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"16 Town Poor Mobile Data 4G Low \n",
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"1190 Town Mid Wifi 3G Moderate \n",
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"\n",
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"[1205 rows x 11 columns]\n"
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]
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}
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],
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"source": [
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"# сортировка датафрейма по возрасту\n",
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"sorted_df = df.sort_values(by=\"Age\")\n",
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"print(sorted_df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Education Level Institution Type Gender Age Device IT Student \\\n",
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"0 University Private Male 23 Tab No \n",
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"1 University Private Female 23 Mobile No \n",
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"2 College Public Female 18 Mobile No \n",
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"4 School Private Female 18 Mobile No \n",
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"8 College Public Male 18 Mobile No \n",
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"... ... ... ... ... ... ... \n",
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"1198 College Public Male 18 Mobile Yes \n",
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"1199 University Private Male 23 Computer Yes \n",
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"1200 College Private Female 18 Mobile No \n",
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"1201 College Private Female 18 Mobile No \n",
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"1203 College Private Female 18 Mobile No \n",
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"\n",
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" Location Financial Condition Internet Type Network Type Flexibility Level \n",
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"0 Town Mid Wifi 4G Moderate \n",
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"1 Town NaN Mobile Data 4G Moderate \n",
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"2 Town Mid Wifi 4G Moderate \n",
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"4 Town Poor NaN 3G Low \n",
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"8 Town Mid Wifi 4G Low \n",
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"... ... ... ... ... ... \n",
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"1198 Rural Mid Mobile Data 4G Low \n",
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"1199 Town Mid Wifi 4G Low \n",
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"1200 Town Mid Wifi 4G Low \n",
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"1201 Rural Mid Wifi 4G Moderate \n",
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"1203 Rural Mid Wifi 4G Low \n",
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"\n",
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"[720 rows x 11 columns]\n"
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]
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}
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],
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"source": [
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"# вывод студентов, которым 18 лет и больше\n",
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"Age = df[df['Age'] >= 18]\n",
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"print(Age)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Education Level</th>\n",
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" <th>Institution Type</th>\n",
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" <th>Gender</th>\n",
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" <th>Age</th>\n",
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" <th>Device</th>\n",
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" <th>IT Student</th>\n",
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" <th>Financial Condition</th>\n",
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" <th>Internet Type</th>\n",
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" <th>Network Type</th>\n",
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" <th>Flexibility Level</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>University</td>\n",
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" <td>Private</td>\n",
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" <td>Male</td>\n",
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" <td>23</td>\n",
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" <td>Tab</td>\n",
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" <td>No</td>\n",
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" <td>Mid</td>\n",
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" <td>Wifi</td>\n",
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" <td>4G</td>\n",
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" <td>Moderate</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>University</td>\n",
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" <td>Private</td>\n",
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" <td>Female</td>\n",
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" <td>23</td>\n",
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" <td>Mobile</td>\n",
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" <td>No</td>\n",
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" <td>NaN</td>\n",
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" <td>Mobile Data</td>\n",
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" <td>4G</td>\n",
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" <td>Moderate</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>College</td>\n",
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" <td>Public</td>\n",
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" <td>Female</td>\n",
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" <td>18</td>\n",
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" <td>Mobile</td>\n",
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" <td>No</td>\n",
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" <td>Mid</td>\n",
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" <td>Wifi</td>\n",
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" <td>4G</td>\n",
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" <td>Moderate</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>School</td>\n",
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" <td>Private</td>\n",
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" <td>Female</td>\n",
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" <td>11</td>\n",
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" <td>Mobile</td>\n",
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" <td>No</td>\n",
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|
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" <td>NaN</td>\n",
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|
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" <td>Mobile Data</td>\n",
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|
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" <td>4G</td>\n",
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|
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" <td>Moderate</td>\n",
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|
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" </tr>\n",
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|
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" <tr>\n",
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" <th>4</th>\n",
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" <td>School</td>\n",
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" <td>Private</td>\n",
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" <td>Female</td>\n",
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|
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" <td>18</td>\n",
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" <td>Mobile</td>\n",
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" <td>No</td>\n",
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|
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" <td>Poor</td>\n",
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" <td>NaN</td>\n",
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|
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" <td>3G</td>\n",
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" <td>Low</td>\n",
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" </tr>\n",
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|
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" </tbody>\n",
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"</table>\n",
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|||
|
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"</div>"
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|||
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],
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|||
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"text/plain": [
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|||
|
|
" Education Level Institution Type Gender Age Device IT Student \\\n",
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|||
|
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"0 University Private Male 23 Tab No \n",
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|||
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"1 University Private Female 23 Mobile No \n",
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"2 College Public Female 18 Mobile No \n",
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"3 School Private Female 11 Mobile No \n",
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|||
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"4 School Private Female 18 Mobile No \n",
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|
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"\n",
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|
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" Financial Condition Internet Type Network Type Flexibility Level \n",
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|||
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"0 Mid Wifi 4G Moderate \n",
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"1 NaN Mobile Data 4G Moderate \n",
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"2 Mid Wifi 4G Moderate \n",
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"3 NaN Mobile Data 4G Moderate \n",
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"4 Poor NaN 3G Low "
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]
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|||
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|
},
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|||
|
|
"execution_count": 7,
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|||
|
|
"metadata": {},
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|||
|
|
"output_type": "execute_result"
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|
}
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|||
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],
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|||
|
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"source": [
|
|||
|
|
"df.drop(['Location'], axis=1).head()"
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]
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|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
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|
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"data": {
|
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|
|
"text/html": [
|
|||
|
|
"<div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
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|
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" }\n",
|
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|
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"\n",
|
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|
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" .dataframe tbody tr th {\n",
|
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|
|
" vertical-align: top;\n",
|
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|
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" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
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|
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" text-align: right;\n",
|
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|
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" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Education Level</th>\n",
|
|||
|
|
" <th>Institution Type</th>\n",
|
|||
|
|
" <th>Gender</th>\n",
|
|||
|
|
" <th>Age</th>\n",
|
|||
|
|
" <th>Device</th>\n",
|
|||
|
|
" <th>IT Student</th>\n",
|
|||
|
|
" <th>Location</th>\n",
|
|||
|
|
" <th>Financial Condition</th>\n",
|
|||
|
|
" <th>Internet Type</th>\n",
|
|||
|
|
" <th>Network Type</th>\n",
|
|||
|
|
" <th>Flexibility Level</th>\n",
|
|||
|
|
" <th>age_group</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>0</th>\n",
|
|||
|
|
" <td>University</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>23</td>\n",
|
|||
|
|
" <td>Tab</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>Mid</td>\n",
|
|||
|
|
" <td>Wifi</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>University</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>23</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>Mobile Data</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>College</td>\n",
|
|||
|
|
" <td>Public</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>Mid</td>\n",
|
|||
|
|
" <td>Wifi</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>School</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>Mobile Data</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>young</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>School</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>Poor</td>\n",
|
|||
|
|
" <td>NaN</td>\n",
|
|||
|
|
" <td>3G</td>\n",
|
|||
|
|
" <td>Low</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>...</th>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" <td>...</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1200</th>\n",
|
|||
|
|
" <td>College</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>Mid</td>\n",
|
|||
|
|
" <td>Wifi</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Low</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1201</th>\n",
|
|||
|
|
" <td>College</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>Mid</td>\n",
|
|||
|
|
" <td>Wifi</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1202</th>\n",
|
|||
|
|
" <td>School</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Male</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>Mid</td>\n",
|
|||
|
|
" <td>Mobile Data</td>\n",
|
|||
|
|
" <td>3G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>young</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1203</th>\n",
|
|||
|
|
" <td>College</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>18</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Rural</td>\n",
|
|||
|
|
" <td>Mid</td>\n",
|
|||
|
|
" <td>Wifi</td>\n",
|
|||
|
|
" <td>4G</td>\n",
|
|||
|
|
" <td>Low</td>\n",
|
|||
|
|
" <td>average</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1204</th>\n",
|
|||
|
|
" <td>School</td>\n",
|
|||
|
|
" <td>Private</td>\n",
|
|||
|
|
" <td>Female</td>\n",
|
|||
|
|
" <td>11</td>\n",
|
|||
|
|
" <td>Mobile</td>\n",
|
|||
|
|
" <td>No</td>\n",
|
|||
|
|
" <td>Town</td>\n",
|
|||
|
|
" <td>Poor</td>\n",
|
|||
|
|
" <td>Mobile Data</td>\n",
|
|||
|
|
" <td>3G</td>\n",
|
|||
|
|
" <td>Moderate</td>\n",
|
|||
|
|
" <td>young</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"<p>1205 rows × 12 columns</p>\n",
|
|||
|
|
"</div>"
|
|||
|
|
],
|
|||
|
|
"text/plain": [
|
|||
|
|
" Education Level Institution Type Gender Age Device IT Student \\\n",
|
|||
|
|
"0 University Private Male 23 Tab No \n",
|
|||
|
|
"1 University Private Female 23 Mobile No \n",
|
|||
|
|
"2 College Public Female 18 Mobile No \n",
|
|||
|
|
"3 School Private Female 11 Mobile No \n",
|
|||
|
|
"4 School Private Female 18 Mobile No \n",
|
|||
|
|
"... ... ... ... ... ... ... \n",
|
|||
|
|
"1200 College Private Female 18 Mobile No \n",
|
|||
|
|
"1201 College Private Female 18 Mobile No \n",
|
|||
|
|
"1202 School Private Male 11 Mobile No \n",
|
|||
|
|
"1203 College Private Female 18 Mobile No \n",
|
|||
|
|
"1204 School Private Female 11 Mobile No \n",
|
|||
|
|
"\n",
|
|||
|
|
" Location Financial Condition Internet Type Network Type \\\n",
|
|||
|
|
"0 Town Mid Wifi 4G \n",
|
|||
|
|
"1 Town NaN Mobile Data 4G \n",
|
|||
|
|
"2 Town Mid Wifi 4G \n",
|
|||
|
|
"3 Town NaN Mobile Data 4G \n",
|
|||
|
|
"4 Town Poor NaN 3G \n",
|
|||
|
|
"... ... ... ... ... \n",
|
|||
|
|
"1200 Town Mid Wifi 4G \n",
|
|||
|
|
"1201 Rural Mid Wifi 4G \n",
|
|||
|
|
"1202 Town Mid Mobile Data 3G \n",
|
|||
|
|
"1203 Rural Mid Wifi 4G \n",
|
|||
|
|
"1204 Town Poor Mobile Data 3G \n",
|
|||
|
|
"\n",
|
|||
|
|
" Flexibility Level age_group \n",
|
|||
|
|
"0 Moderate average \n",
|
|||
|
|
"1 Moderate average \n",
|
|||
|
|
"2 Moderate average \n",
|
|||
|
|
"3 Moderate young \n",
|
|||
|
|
"4 Low average \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"1200 Low average \n",
|
|||
|
|
"1201 Moderate average \n",
|
|||
|
|
"1202 Moderate young \n",
|
|||
|
|
"1203 Low average \n",
|
|||
|
|
"1204 Moderate young \n",
|
|||
|
|
"\n",
|
|||
|
|
"[1205 rows x 12 columns]"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# добавление нового столбца Возрастная группа\n",
|
|||
|
|
"def age_group(value):\n",
|
|||
|
|
" if value < 18:\n",
|
|||
|
|
" return \"young\"\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" return \"average\"\n",
|
|||
|
|
"\n",
|
|||
|
|
"df['age_group'] = df['Age'].map(age_group)\n",
|
|||
|
|
"display(df)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" Education Level Institution Type Gender Age Device IT Student \\\n",
|
|||
|
|
"0 University Private Male 23 Tab No \n",
|
|||
|
|
"2 College Public Female 18 Mobile No \n",
|
|||
|
|
"5 School Private Male 11 Mobile No \n",
|
|||
|
|
"8 College Public Male 18 Mobile No \n",
|
|||
|
|
"9 School Private Male 11 Mobile No \n",
|
|||
|
|
"... ... ... ... ... ... ... \n",
|
|||
|
|
"1200 College Private Female 18 Mobile No \n",
|
|||
|
|
"1201 College Private Female 18 Mobile No \n",
|
|||
|
|
"1202 School Private Male 11 Mobile No \n",
|
|||
|
|
"1203 College Private Female 18 Mobile No \n",
|
|||
|
|
"1204 School Private Female 11 Mobile No \n",
|
|||
|
|
"\n",
|
|||
|
|
" Location Financial Condition Internet Type Network Type \\\n",
|
|||
|
|
"0 Town Mid Wifi 4G \n",
|
|||
|
|
"2 Town Mid Wifi 4G \n",
|
|||
|
|
"5 Town Poor Mobile Data 3G \n",
|
|||
|
|
"8 Town Mid Wifi 4G \n",
|
|||
|
|
"9 Town Mid Mobile Data 3G \n",
|
|||
|
|
"... ... ... ... ... \n",
|
|||
|
|
"1200 Town Mid Wifi 4G \n",
|
|||
|
|
"1201 Rural Mid Wifi 4G \n",
|
|||
|
|
"1202 Town Mid Mobile Data 3G \n",
|
|||
|
|
"1203 Rural Mid Wifi 4G \n",
|
|||
|
|
"1204 Town Poor Mobile Data 3G \n",
|
|||
|
|
"\n",
|
|||
|
|
" Flexibility Level age_group \n",
|
|||
|
|
"0 Moderate average \n",
|
|||
|
|
"2 Moderate average \n",
|
|||
|
|
"5 Low young \n",
|
|||
|
|
"8 Low average \n",
|
|||
|
|
"9 Moderate young \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"1200 Low average \n",
|
|||
|
|
"1201 Moderate average \n",
|
|||
|
|
"1202 Moderate young \n",
|
|||
|
|
"1203 Low average \n",
|
|||
|
|
"1204 Moderate young \n",
|
|||
|
|
"\n",
|
|||
|
|
"[1199 rows x 12 columns]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# удаление строк с пустыми значениями\n",
|
|||
|
|
"df_cleaned = df.dropna()\n",
|
|||
|
|
"print(df_cleaned)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"name": "stdout",
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
" Education Level Institution Type Gender Age Device IT Student \\\n",
|
|||
|
|
"0 University Private Male 23 Tab No \n",
|
|||
|
|
"1 University Private Female 23 Mobile No \n",
|
|||
|
|
"2 College Public Female 18 Mobile No \n",
|
|||
|
|
"3 School Private Female 11 Mobile No \n",
|
|||
|
|
"4 School Private Female 18 Mobile No \n",
|
|||
|
|
"... ... ... ... ... ... ... \n",
|
|||
|
|
"1200 College Private Female 18 Mobile No \n",
|
|||
|
|
"1201 College Private Female 18 Mobile No \n",
|
|||
|
|
"1202 School Private Male 11 Mobile No \n",
|
|||
|
|
"1203 College Private Female 18 Mobile No \n",
|
|||
|
|
"1204 School Private Female 11 Mobile No \n",
|
|||
|
|
"\n",
|
|||
|
|
" Location Financial Condition Internet Type Network Type \\\n",
|
|||
|
|
"0 Town Mid Wifi 4G \n",
|
|||
|
|
"1 Town Mid Mobile Data 4G \n",
|
|||
|
|
"2 Town Mid Wifi 4G \n",
|
|||
|
|
"3 Town Mid Mobile Data 4G \n",
|
|||
|
|
"4 Town Poor NaN 3G \n",
|
|||
|
|
"... ... ... ... ... \n",
|
|||
|
|
"1200 Town Mid Wifi 4G \n",
|
|||
|
|
"1201 Rural Mid Wifi 4G \n",
|
|||
|
|
"1202 Town Mid Mobile Data 3G \n",
|
|||
|
|
"1203 Rural Mid Wifi 4G \n",
|
|||
|
|
"1204 Town Poor Mobile Data 3G \n",
|
|||
|
|
"\n",
|
|||
|
|
" Flexibility Level age_group \n",
|
|||
|
|
"0 Moderate average \n",
|
|||
|
|
"1 Moderate average \n",
|
|||
|
|
"2 Moderate average \n",
|
|||
|
|
"3 Moderate young \n",
|
|||
|
|
"4 Low average \n",
|
|||
|
|
"... ... ... \n",
|
|||
|
|
"1200 Low average \n",
|
|||
|
|
"1201 Moderate average \n",
|
|||
|
|
"1202 Moderate young \n",
|
|||
|
|
"1203 Low average \n",
|
|||
|
|
"1204 Moderate young \n",
|
|||
|
|
"\n",
|
|||
|
|
"[1205 rows x 12 columns]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# Вычисление моды (наиболее часто встречающегося значения) для пустых значений\n",
|
|||
|
|
"mode_Financial = df['Financial Condition'].mode()[0] \n",
|
|||
|
|
"df.fillna({'Financial Condition':mode_Financial}, inplace=True)\n",
|
|||
|
|
"print(df)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# линейная диаграмма (средний возраст по уровню образования)\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"plt.plot(df[[\"Education Level\", \"Age\"]].groupby(\"Education Level\").mean())\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Axes: ylabel='Frequency'>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# гистограмма\n",
|
|||
|
|
"df.plot.hist(column=[\"Age\"], bins=80)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Age Axes(0.125,0.11;0.775x0.77)\n",
|
|||
|
|
"dtype: object"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x800 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# ящик с усами\n",
|
|||
|
|
"df.plot.box(column=\"Age\", by=\"Gender\", figsize=(10, 8))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Axes: xlabel='Education Level'>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# диаграмма с областями \n",
|
|||
|
|
"data = (df[[ \"Education Level\", \"Age\"]].groupby(['Education Level']).mean())\n",
|
|||
|
|
"data.plot.area()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Axes: xlabel='Age', ylabel='Education Level'>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# диаграмма рассеяния \n",
|
|||
|
|
"df.plot.scatter(x =\"Age\", y =\"Education Level\")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Axes: ylabel='Age'>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 640x480 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"# круговая диаграмма \n",
|
|||
|
|
"data = (df[[ \"Device\", \"Age\"]].groupby(['Device']).count())\n",
|
|||
|
|
"data.plot.pie(x ='Device', y ='Age')"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"kernelspec": {
|
|||
|
|
"display_name": ".venv",
|
|||
|
|
"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": "3.12.5"
|
|||
|
|
},
|
|||
|
|
"orig_nbformat": 4
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|