328 lines
143 KiB
Plaintext
328 lines
143 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Начало ЛР\n",
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"\n",
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"Выгрузка данных из CSV в датафрейм"
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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": 23,
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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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" .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>Location</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>Town</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>Town</td>\n",
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" <td>Mid</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>Town</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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" <td>Town</td>\n",
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" <td>Mid</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>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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" <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>Town</td>\n",
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" <td>Poor</td>\n",
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" <td>Mobile Data</td>\n",
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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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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Education Level Institution Type Gender Age Device IT Student Location \\\n",
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"0 University Private Male 23 Tab No Town \n",
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"1 University Private Female 23 Mobile No Town \n",
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"2 College Public Female 18 Mobile No Town \n",
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"3 School Private Female 11 Mobile No Town \n",
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"4 School Private Female 18 Mobile No Town \n",
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"\n",
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" Financial Condition Internet Type Network Type Flexibility Level \n",
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"0 Mid Wifi 4G Moderate \n",
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"1 Mid Mobile Data 4G Moderate \n",
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"2 Mid Wifi 4G Moderate \n",
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"3 Mid Mobile Data 4G Moderate \n",
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"4 Poor Mobile Data 3G Low "
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]
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},
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"execution_count": 23,
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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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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"dataframe = pd.read_csv(\".//static//csv//students_adaptability_level_online_education.csv\")\n",
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"dataframe.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Диаграмма 1 (Круговая)\n",
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"\n",
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"Данная диаграмма (круговая) отображает распределение людей по типу соединения к интернету (4G, 3G, 2G). Это позволяет сделать вывод о том, что люди с низким уровнем заработка имеют чаще всего 3G и 4G (одинаково), чем 2G."
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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": 36,
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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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"Text(0.5, 1.0, 'Распределение людей низкого уровня заработка по типу соединения')"
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]
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},
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"execution_count": 36,
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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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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 600x600 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"rural_df = dataframe[dataframe['Financial Condition'] == \"Poor\"]\n",
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"\n",
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"network_type_rural_count = rural_df['Network Type'].value_counts()\n",
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"\n",
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"plt.figure(figsize=(6,6))\n",
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"plt.pie(network_type_rural_count, labels=network_type_rural_count.index, autopct='%1.2f%%')\n",
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"plt.title(\"Распределение людей низкого уровня заработка по типу соединения\")"
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]
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},
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|
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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|
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"# Диаграмма 2 (Линейная)\n",
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"\n",
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"Данная диаграмма выполнена на срезе данных и отображает количество студентов в разных возрастных группах. Из нее можно сделать вывод, что в подростковом возрасте (10-15) люди менее интересуются IT направлением, но в возрасте 20-25 лет количество учеников IT направлений начинает преобладать над не IT сферой (и в это же время количество не IT студентов начинает убывать)."
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]
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},
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|
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{
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|
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"cell_type": "code",
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|
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"execution_count": 45,
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|
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"metadata": {},
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"outputs": [
|
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{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x71e288175ac0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 45,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"df_cut = dataframe.iloc[0:1500]\n",
|
|||
|
|
"\n",
|
|||
|
|
"it_student_df = df_cut[df_cut['IT Student'] == \"Yes\"].copy()\n",
|
|||
|
|
"nonit_student_df = df_cut[df_cut['IT Student'] == \"No\"].copy()\n",
|
|||
|
|
"\n",
|
|||
|
|
"age_bins = range(10, 36, 5)\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"it_student_df['age_group'] = pd.cut(it_student_df['Age'], bins=age_bins)\n",
|
|||
|
|
"it_student_count = it_student_df.groupby('age_group', observed=False)['IT Student'].value_counts().reset_index()\n",
|
|||
|
|
"mean_count_it = it_student_count.groupby('age_group', observed=False)['count'].mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
"nonit_student_df['age_group'] = pd.cut(nonit_student_df['Age'], bins=age_bins)\n",
|
|||
|
|
"nonit_student_count = nonit_student_df.groupby('age_group', observed=False)['IT Student'].value_counts().reset_index()\n",
|
|||
|
|
"mean_count_nonit = nonit_student_count.groupby('age_group', observed=False)['count'].mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.figure(figsize=(10,6))\n",
|
|||
|
|
"plt.plot(mean_count_it.index.astype(str), mean_count_it , marker='o', label='IT студент')\n",
|
|||
|
|
"plt.plot(mean_count_nonit.index.astype(str), mean_count_nonit , marker='o', label='Не IT студент')\n",
|
|||
|
|
"plt.xlabel(\"Возрастная группа\")\n",
|
|||
|
|
"plt.ylabel(\"Количество студентов\")\n",
|
|||
|
|
"plt.title('Количество студентов на IT и не IT направлении')\n",
|
|||
|
|
"plt.xticks(rotation=45)\n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {},
|
|||
|
|
"source": [
|
|||
|
|
"# Диаграмма 3 (Гистограмма)\n",
|
|||
|
|
"\n",
|
|||
|
|
"Данная диаграмма отображает количество людей по типу соединения к сети Интернет. На основе этой диаграммы можно сделать вывод, что чаще всего используют мобильный интернет, чем Wi-Fi"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Text(0, 0.5, 'Количество')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"execution_count": 46,
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "execute_result"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1000x600 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"output_type": "display_data"
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"plt.figure(figsize=(10,6))\n",
|
|||
|
|
"plt.hist(dataframe.head(20000)['Internet Type'], bins=4, edgecolor='black')\n",
|
|||
|
|
"plt.title(\"Распределение людей по типу подключения к сети Интернет\")\n",
|
|||
|
|
"plt.xlabel(\"Тип подключения к сети Интернет\")\n",
|
|||
|
|
"plt.ylabel(\"Количество\")"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"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": "3.12.6"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"nbformat": 4,
|
|||
|
|
"nbformat_minor": 2
|
|||
|
|
}
|