912 lines
164 KiB
Plaintext
912 lines
164 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "9193a41f",
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"metadata": {},
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"source": [
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"# Анализ данных в JupyterLab\n",
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"\n",
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"Цель работы — изучить библиотеки pandas, numpy, matplotlib, seaborn и tqdm для анализа и визуализации данных."
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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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"id": "12408ef7",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from tqdm import tqdm\n",
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"import time\n",
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"\n",
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"%matplotlib inline"
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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": 12,
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"id": "bb544f84",
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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>Имя</th>\n",
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" <th>Возраст</th>\n",
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" <th>Баллы</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>Анна</td>\n",
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" <td>21</td>\n",
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" <td>89</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>Борис</td>\n",
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" <td>22</td>\n",
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" <td>76</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>Виктор</td>\n",
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" <td>23</td>\n",
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" <td>95</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>Галина</td>\n",
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" <td>24</td>\n",
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" <td>82</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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" Имя Возраст Баллы\n",
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"0 Анна 21 89\n",
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"1 Борис 22 76\n",
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"2 Виктор 23 95\n",
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"3 Галина 24 82"
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]
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},
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"execution_count": 12,
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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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"data = {\n",
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" \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
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" \"Возраст\": [21, 22, 23, 24],\n",
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" \"Баллы\": [89, 76, 95, 82]\n",
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"}\n",
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"\n",
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"df = pd.DataFrame(data)\n",
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"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": 13,
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"id": "c2f12d44",
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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: 4 entries, 0 to 3\n",
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"Data columns (total 3 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Имя 4 non-null object\n",
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" 1 Возраст 4 non-null int64 \n",
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" 2 Баллы 4 non-null int64 \n",
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"dtypes: int64(2), object(1)\n",
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"memory usage: 224.0+ bytes\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Имя 0\n",
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"Возраст 0\n",
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"Баллы 0\n",
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"dtype: int64"
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]
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},
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"execution_count": 13,
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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.head()\n",
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"df.info()\n",
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"df.describe()\n",
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"df.isnull().sum()"
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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": 14,
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"id": "f765c087",
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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>Имя</th>\n",
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" <th>Возраст</th>\n",
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" <th>Баллы</th>\n",
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" <th>Баллы_с_бонусом</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>Анна</td>\n",
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" <td>21</td>\n",
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" <td>89</td>\n",
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" <td>106.8</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>Борис</td>\n",
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" <td>22</td>\n",
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" <td>76</td>\n",
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" <td>91.2</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>Виктор</td>\n",
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" <td>23</td>\n",
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" <td>95</td>\n",
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" <td>114.0</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>Галина</td>\n",
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" <td>24</td>\n",
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" <td>82</td>\n",
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" <td>98.4</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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" Имя Возраст Баллы Баллы_с_бонусом\n",
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"0 Анна 21 89 106.8\n",
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"1 Борис 22 76 91.2\n",
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"2 Виктор 23 95 114.0\n",
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"3 Галина 24 82 98.4"
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]
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},
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"execution_count": 14,
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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[\"Баллы_с_бонусом\"] = df[\"Баллы\"] * 1.2\n",
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"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": 15,
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"id": "9741e648",
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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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" 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>Имя</th>\n",
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" <th>Возраст</th>\n",
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" <th>Баллы</th>\n",
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" <th>Баллы_с_бонусом</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>1</th>\n",
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" <td>Борис</td>\n",
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" <td>22</td>\n",
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" <td>76</td>\n",
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" <td>91.2</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>Виктор</td>\n",
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" <td>23</td>\n",
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" <td>95</td>\n",
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" <td>114.0</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>Галина</td>\n",
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" <td>24</td>\n",
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" <td>82</td>\n",
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" <td>98.4</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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" Имя Возраст Баллы Баллы_с_бонусом\n",
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"1 Борис 22 76 91.2\n",
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"2 Виктор 23 95 114.0\n",
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"3 Галина 24 82 98.4"
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]
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},
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"execution_count": 15,
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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[df[\"Возраст\"] > 21]"
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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": 16,
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"id": "6f0a2504",
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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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" vertical-align: middle;\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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" .dataframe thead tr th {\n",
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" text-align: left;\n",
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" }\n",
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"\n",
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" .dataframe thead tr:last-of-type 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>\n",
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" <th></th>\n",
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" <th colspan=\"2\" halign=\"left\">Баллы</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th>mean</th>\n",
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" <th>max</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Возраст</th>\n",
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" <th></th>\n",
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" <th></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>21</th>\n",
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" <td>89.0</td>\n",
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" <td>89</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>22</th>\n",
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" <td>76.0</td>\n",
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" <td>76</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>23</th>\n",
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" <td>95.0</td>\n",
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" <td>95</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>24</th>\n",
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" <td>82.0</td>\n",
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" <td>82</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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" Баллы \n",
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" mean max\n",
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"Возраст \n",
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"21 89.0 89\n",
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"22 76.0 76\n",
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"23 95.0 95\n",
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"24 82.0 82"
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]
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},
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"execution_count": 16,
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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.groupby(\"Возраст\").agg({\n",
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" \"Баллы\": [\"mean\", \"max\"]\n",
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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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"execution_count": 17,
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"id": "3a42befd",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.scatter(df[\"Возраст\"], df[\"Баллы\"])\n",
|
||
"plt.xlabel(\"Возраст\")\n",
|
||
"plt.ylabel(\"Баллы\")\n",
|
||
"plt.title(\"Возраст vs Баллы\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "d2ac6d77",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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+vbz00kvSpUsXn+tkZWWZyfMriAEAQHAKOIwEKikpSTp16uQ1T0OI1pD4Ex8fLxMmTJCLof6o5VLS7Hu+m+1dKDW4PsC1Af5uBEEH1tTUVAkPD/eap4+1tuP333/3uU5cXJxkZGS4p5SUlAu9mwAAIFhrRopCO7rqBAAAgt8FrxmJiIiQtLQ0r3n6uEqVKlKxYsUL/fQAAKC0h5GYmBhZu3at17zVq1eb+QAAAAGHkZMnT5ohujq5hu7qzwcOHHD394iNjXUv//DDD8uePXtkxIgRsm3bNnnttddkyZIlMmzYMI4+AAAIPIx89913cs0115hJDR8+3Pw8duxY8/iXX35xBxOlw3qXL19uakP080l0iO/cuXP9DusFAAClS8AdWG+66SZxHMfv7319uqqus3nz5sD3DgAABD2+mwYAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAEDJCyOzZs2S+vXrS1hYmLRr106Sk5PzXX7GjBnSpEkTqVixokRFRcmwYcPkzJkzRd1nAABQmsPI4sWLZfjw4TJu3DjZtGmTtGrVSrp06SJHjhzxufx7770no0aNMstv3bpV5s2bZ7bx9NNPn4/9BwAApS2MTJ8+XQYOHCj9+/eX5s2bS0JCglSqVEnmz5/vc/kNGzZIhw4d5L777jO1KZ07d5Y+ffoUWJsCAABKh4DCSHZ2tmzcuFE6der0fxsoW9Y8TkpK8rlO+/btzTqu8LFnzx5ZsWKFdO3a1e/zZGVlSWZmptcEAACCU7lAFk5PT5dz585JeHi413x9vG3bNp/raI2IrnfdddeJ4zhy9uxZefjhh/NtpomPj5cJEyYEsmsAAKCEuuCjaRITE2Xy5Mny2muvmT4mH3/8sSxfvlwmTpzod524uDjJyMhwTykpKRd6NwEAQEmoGalZs6aEhIRIWlqa13x9HBER4XOdMWPGyP333y8PPvigedyiRQs5deqUPPTQQzJ69GjTzJNbaGiomQAAQPALqGakQoUKEh0dLWvXrnXPy8nJMY9jYmJ8rnP69Ok8gUMDjdJmGwAAULoFVDOidFhvv379pE2bNtK2bVvzGSJa06Gja1RsbKxERkaafh+qe/fuZgTONddcYz6TZNeuXaa2ROe7QgkAACi9Ag4jvXv3lqNHj8rYsWMlNTVVWrduLStXrnR3aj1w4IBXTcgzzzwjZcqUMf8fOnRIatWqZYLIpEmTzm9JAABA6QgjasiQIWby12HV6wnKlTMfeKYTAABAbnw3DQAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAgJIXRmbNmiX169eXsLAwadeunSQnJ+e7/G+//SaDBw+WOnXqSGhoqFx55ZWyYsWKou4zAAAIIuUCXWHx4sUyfPhwSUhIMEFkxowZ0qVLF9m+fbvUrl07z/LZ2dly6623mt99+OGHEhkZKfv375dq1aqdrzIAAIDSFEamT58uAwcOlP79+5vHGkqWL18u8+fPl1GjRuVZXucfO3ZMNmzYIOXLlzfztFYFAAAg4GYareXYuHGjdOrUyT2vbNmy5nFSUpLPdT799FOJiYkxzTTh4eFy9dVXy+TJk+XcuXN+nycrK0syMzO9JgAAEJwCCiPp6ekmRGio8KSPU1NTfa6zZ88e0zyj62k/kTFjxsi0adPkueee8/s88fHxUrVqVfcUFRUVyG4CAIAS5IKPpsnJyTH9RWbPni3R0dHSu3dvGT16tGne8ScuLk4yMjLcU0pKyoXeTQAAUBL6jNSsWVNCQkIkLS3Na74+joiI8LmOjqDRviK6nkuzZs1MTYo2+1SoUCHPOjriRicAABD8AqoZ0eCgtRtr1671qvnQx9ovxJcOHTrIrl27zHIuO3bsMCHFVxABAAClS8DNNDqsd86cOfLmm2/K1q1bZdCgQXLq1Cn36JrY2FjTzOKiv9fRNEOHDjUhREfeaAdW7dAKAAAQ8NBe7fNx9OhRGTt2rGlqad26taxcudLdqfXAgQNmhI2Ldj5dtWqVDBs2TFq2bGk+Z0SDyciRIzn6AAAg8DCihgwZYiZfEhMT88zTJpyvv/6aww0AAPLgu2kAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAAAlL4zMmjVL6tevL2FhYdKuXTtJTk4u1HqLFi2SMmXKSM+ePYvytAAAIAgFHEYWL14sw4cPl3HjxsmmTZukVatW0qVLFzly5Ei+6+3bt0+efPJJuf766//M/gIAgNIeRqZPny4DBw6U/v37S/PmzSUhIUEqVaok8+fP97vOuXPnpG/fvjJhwgRp2LBhgc+RlZUlmZmZXhMAAAhOAYWR7Oxs2bhxo3Tq1On/NlC2rHmclJTkd71nn31WateuLQMGDCjU88THx0vVqlXdU1RUVCC7CQAAgjWMpKenm1qO8PBwr/n6ODU11ec669evl3nz5smcOXMK/TxxcXGSkZHhnlJSUgLZTQAAUIKUu5AbP3HihNx///0miNSsWbPQ64WGhpoJAAAEv4DCiAaKkJAQSUtL85qvjyMiIvIsv3v3btNxtXv37u55OTk5//+Jy5WT7du3S6NGjYq+9wAAoHQ101SoUEGio6Nl7dq1XuFCH8fExORZvmnTpvLjjz/Kli1b3FOPHj2kY8eO5mf6ggAAgICbaXRYb79+/aRNmzbStm1bmTFjhpw6dcqMrlGxsbESGRlpOqHq55BcffXVXutXq1bN/J97PgAAKJ0CDiO9e/eWo0ePytixY02n1datW8vKlSvdnVoPHDhgRtgAAABcsA6sQ4YMMZMviYmJ+a67cOHCojwlAAAIUlRhAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAKHlhZNasWVK/fn0JCwuTdu3aSXJyst9l58yZI9dff71Ur17dTJ06dcp3eQAAULoEHEYWL14sw4cPl3HjxsmmTZukVatW0qVLFzly5IjP5RMTE6VPnz6ybt06SUpKkqioKOncubMcOnTofOw/AAAobWFk+vTpMnDgQOnfv780b95cEhISpFKlSjJ//nyfy7/77rvyyCOPSOvWraVp06Yyd+5cycnJkbVr156P/QcAAKUpjGRnZ8vGjRtNU4t7A2XLmsda61EYp0+flj/++EMuu+wyv8tkZWVJZmam1wQAAIJTQGEkPT1dzp07J+Hh4V7z9XFqamqhtjFy5EipW7euV6DJLT4+XqpWreqetGkHAAAEp4s6mub555+XRYsWydKlS03nV3/i4uIkIyPDPaWkpFzM3QQAABdRuUAWrlmzpoSEhEhaWprXfH0cERGR77ovvviiCSNr1qyRli1b5rtsaGiomQAAQPALqGakQoUKEh0d7dX51NUZNSYmxu96L7zwgkycOFFWrlwpbdq0+XN7DAAASm/NiNJhvf369TOhom3btjJjxgw5deqUGV2jYmNjJTIy0vT7UFOmTJGxY8fKe++9Zz6bxNW3pHLlymYCAAClW8BhpHfv3nL06FETMDRY6JBdrfFwdWo9cOCAGWHj8vrrr5tROPfcc4/XdvRzSsaPH38+ygAAAEpTGFFDhgwxk78POfO0b9++ou0ZAAAoFfhuGgAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAQMkLI7NmzZL69etLWFiYtGvXTpKTk/Nd/oMPPpCmTZua5Vu0aCErVqwo6v4CAIDSHkYWL14sw4cPl3HjxsmmTZukVatW0qVLFzly5IjP5Tds2CB9+vSRAQMGyObNm6Vnz55m+umnn87H/gMAgBKuXKArTJ8+XQYOHCj9+/c3jxMSEmT58uUyf/58GTVqVJ7lZ86cKbfddps89dRT5vHEiRNl9erV8uqrr5p1fcnKyjKTS0ZGhvk/MzNTzrecrNNS0lyI4wDfuD7gD9cGuDYKf79yHCf/BZ0AZGVlOSEhIc7SpUu95sfGxjo9evTwuU5UVJTz0ksvec0bO3as07JlS7/PM27cON1rJo4B1wDXANcA1wDXgJT8Y5CSkpJvvgioZiQ9PV3OnTsn4eHhXvP18bZt23yuk5qa6nN5ne9PXFycaQpyycnJkWPHjkmNGjWkTJkyEqw0QUZFRUlKSopUqVJFgllpKmtpKy9lDV6c2+CUeQH/PmmNyIkTJ6Ru3brnt5nmYggNDTWTp2rVqklpoRdDsN+wSmNZS1t5KWvw4twGpyoX6O9T1apVz28H1po1a0pISIikpaV5zdfHERERPtfR+YEsDwAASpeAwkiFChUkOjpa1q5d69WEoo9jYmJ8rqPzPZdX2oHV3/IAAKB0CbiZRvty9OvXT9q0aSNt27aVGTNmyKlTp9yja2JjYyUyMlLi4+PN46FDh8qNN94o06ZNk27dusmiRYvku+++k9mzZ5//0pRw2jSlQ6ZzN1EFo9JU1tJWXsoavDi3wSm0GPx9KqO9WANdSYflTp061XRCbd26tbz88svmw8/UTTfdZD4QbeHChV4fevbMM8/Ivn37pHHjxvLCCy9I165dz29JAABAiVSkMAIAAHC+8N00AADAKsIIAACwijACAACsIowAAACrCCMXiI4o0o+uzz0NHjzYjCry9TuddOSRP//4xz/yLK9fQmibfkXAmDFjpEGDBlKxYkVp1KiR+UJEz77R+vPYsWOlTp06ZplOnTrJzp07C9z2rFmzzLEMCwszI7aSk5OlOJf1jz/+kJEjR0qLFi3kkksuMR+BrMPdDx8+nO92x48fn+fcNm3aVIr7eS3qNVnczmthy+vvdaujC0vSuVX6Ed2PP/641KtXz5S3ffv28u233wbda7agsgbTa7aw57ZYvm4D+J48BODIkSPOL7/84p5Wr15tvixo3bp1ztmzZ71+p9OECROcypUrOydOnPC7zX79+jm33Xab13rHjh2zfl4mTZrk1KhRw/n3v//t7N271/nggw9MWWbOnOle5vnnn3eqVq3qLFu2zPn+++/NFys2aNDA+f333/1ud9GiRU6FChWc+fPnO//73/+cgQMHOtWqVXPS0tKc4lrW3377zenUqZOzePFiZ9u2bU5SUpLTtm1bJzo6Ot/t6pdDXnXVVV7n9ujRo05xP69FuSaL43ktbHlzv261DGXKlHF2795dos6t6tWrl9O8eXPnv//9r7Nz506zn1WqVHEOHjwYVK/ZgsoaTK/Zwp7b4vi6JYxcJEOHDnUaNWrk5OTk+Px969atnQceeCDfbegFdOeddzrFTbdu3fLs+1133eX07dvX/KxljoiIcKZOner+vf4BCA0Ndd5//32/29U/CIMHD3Y/PnfunFO3bl0nPj7eKa5l9SU5OdkE0f379/tdRv9YtGrVyilOClPWolyTxfG8FvXcatlvvvnmfLdbHM/t6dOnzTewa/DydO211zqjR48OqtdsQWUNptdsYctbHF+3NNNcBNnZ2fLOO+/IAw884PNbhzdu3ChbtmyRAQMGFLitxMREqV27tjRp0kQGDRokv/76q9imVYD6kf87duwwj7///ntZv3693H777ebx3r17zQfkaTWv5xcnaTVfUlKS32Omx8VznbJly5rH/tYpDmX1JSMjw5z3gr7sUavAtYq4YcOG0rdvXzlw4IDYVNiyBnJNFtfzWpRzq9+xtXz58kK9bovbuT179qxpltLqdk9apa9lDqbXbEFlDabXbCDlLXav2/MSaZAvrf7TpHro0CGfvx80aJDTrFmzAo+iviP55JNPnB9++MFZunSpWeevf/2rafaxSRPyyJEjTXV1uXLlzP+TJ092//6rr74y7zIOHz7std69995rqhN90WOl62zYsMFr/lNPPWUSenEta25apa3vSO677758t7tixQpnyZIlpjp85cqVTkxMjPOXv/zFyczMdIpzWQO9JovreS3KuZ0yZYpTvXr1fJstiuu5VbofN954ozkner7efvttp2zZss6VV14ZVK/ZgsoaTK/Zwpa3OL5uCSMXQefOnZ077rjDb5Watsu++OKLAW9X26n1AlmzZo1jk17Yl19+uflfL+633nrLueyyy5yFCxea3wfTH7aCyuopOzvb6d69u3PNNdc4GRkZAT3P8ePHTRvv3LlznZJQ1sJek8X1vBalvE2aNHGGDBkS8PMUh3Ordu3a5dxwww3mfOibJb0ZaZNU06ZNg+o1W1BZg+k1G2h5i9PrljByge3bt88kUu0E5ov+wStfvrzp8FoUNWvWdBISEhyb9A/4q6++6jVv4sSJ5o+154W+efNmr2X0xfLYY4/53GZWVpZ5EWlq9xQbG2s60hXXsnr+UevZs6fTsmVLJz09vUjP1aZNG2fUqFFOcS9rINdkcT2vgZb3iy++MNf0li1bSuS59XTy5El36NCg0bVr16B6zRZU1mB6zQZS3uL2uqXPyAW2YMEC0y6n31jsy7x586RHjx5Sq1atgLd98OBB086nQ+9sOn36tGk/9BQSEiI5OTnmZx0qGRERYdrjXTIzM+Wbb76RmJgYn9usUKGCREdHe62j29PH/tYpDmV1DRXs1auXaU9es2aN1KhRI+DnOXnypOzevdvquS1MWQO9JovreQ20vPq61XK0atWqRJ5bTzqcVffl+PHjsmrVKrnzzjuD6jVbUFmD6TVb2PIWy9fteYk08NsGrW2I2g7tiw650nbpzz77zOfv9R3Zxx9/bH7WIb9PPvmkGXamww61Ok3bNRs3buycOXPG6hnQntmRkZHuIZG6z5qyR4wY4V5GhwnqMDBXO6X25M49TFBHJbzyyiteQ8m0975Wk//888/OQw89ZLaRmprqFNey6rsrfaeg77L1XbPn0Dl9d+GvrE888YSTmJhotqlV5DrUULdb1Bqzi1HWwl6TJeG8FvY6Vlp9X6lSJef111/3uZ2ScG6V9nPQvz179uxxPv/8czMypF27duYaDqbXbEFlDabXbGHKW1xft4SRC2jVqlWmqnP79u0+fx8XF+dERUWZ0OLz5Ig4CxYscPct0b4ntWrVMs069erVM+O8bb/IlXbY0qHLGrzCwsKchg0bmiFkni9kHSo4ZswYJzw83FzQt9xyS57jomXS4XKe9MWg29Xx7do2+fXXXzvFuaz64tbz5mvSz5jxV9bevXs7derUMeXUG6I+1nbf4lzWwl6TJeG8FvY6Vm+88YZTsWJFM9TVl5Jwbl0d67WMul86jFeHbXqWKVheswWVNZhes4Upb3F93ZbRf85PHQsAAEDg6DMCAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAABAbPp/r6x5pYdj9uAAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"plt.hist(df[\"Баллы\"])\n",
|
||
"plt.title(\"Распределение баллов\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "597cc642",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "dac6c5dc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
|
||
"plt.title(\"Boxplot баллов\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "36305c5d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 50/50 [00:01<00:00, 31.99it/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"import time\n",
|
||
"\n",
|
||
"for i in tqdm(range(50)):\n",
|
||
" time.sleep(0.02)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"id": "433ab32b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>name</th>\n",
|
||
" <th>country</th>\n",
|
||
" <th>subcountry</th>\n",
|
||
" <th>geonameid</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>les Escaldes</td>\n",
|
||
" <td>Andorra</td>\n",
|
||
" <td>Escaldes-Engordany</td>\n",
|
||
" <td>3040051</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Andorra la Vella</td>\n",
|
||
" <td>Andorra</td>\n",
|
||
" <td>Andorra la Vella</td>\n",
|
||
" <td>3041563</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Warīsān</td>\n",
|
||
" <td>United Arab Emirates</td>\n",
|
||
" <td>Dubai</td>\n",
|
||
" <td>290503</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>Umm Suqaym</td>\n",
|
||
" <td>United Arab Emirates</td>\n",
|
||
" <td>Dubai</td>\n",
|
||
" <td>290581</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>Umm Al Quwain City</td>\n",
|
||
" <td>United Arab Emirates</td>\n",
|
||
" <td>UmmalQaywayn</td>\n",
|
||
" <td>290594</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" name country subcountry geonameid\n",
|
||
"0 les Escaldes Andorra Escaldes-Engordany 3040051\n",
|
||
"1 Andorra la Vella Andorra Andorra la Vella 3041563\n",
|
||
"2 Warīsān United Arab Emirates Dubai 290503\n",
|
||
"3 Umm Suqaym United Arab Emirates Dubai 290581\n",
|
||
"4 Umm Al Quwain City United Arab Emirates UmmalQaywayn 290594"
|
||
]
|
||
},
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_real = pd.read_csv(\"data/filtered_data.csv\")\n",
|
||
"df_real.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"id": "2bae2839",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 33514 entries, 0 to 33513\n",
|
||
"Data columns (total 4 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 name 33514 non-null object\n",
|
||
" 1 country 33514 non-null object\n",
|
||
" 2 subcountry 33391 non-null object\n",
|
||
" 3 geonameid 33514 non-null int64 \n",
|
||
"dtypes: int64(1), object(3)\n",
|
||
"memory usage: 1.0+ MB\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"name 0\n",
|
||
"country 0\n",
|
||
"subcountry 123\n",
|
||
"geonameid 0\n",
|
||
"dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_real.info()\n",
|
||
"df_real.describe()\n",
|
||
"df_real.isnull().sum()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 26,
|
||
"id": "78e97f4d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['name', 'country', 'subcountry', 'geonameid'], dtype='object')"
|
||
]
|
||
},
|
||
"execution_count": 26,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_real.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"id": "514ff8dc",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"244"
|
||
]
|
||
},
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_real[\"country\"].nunique()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "ca9c8306",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"country\n",
|
||
"India 3776\n",
|
||
"United States 3397\n",
|
||
"Brazil 2345\n",
|
||
"China 2081\n",
|
||
"Japan 1296\n",
|
||
"Germany 1144\n",
|
||
"Russian Federation 1106\n",
|
||
"United Kingdom 860\n",
|
||
"Spain 735\n",
|
||
"France 691\n",
|
||
"Name: count, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_real[\"country\"].value_counts().head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "21fbb519",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"name\n",
|
||
"Richmond 9\n",
|
||
"San Pedro 8\n",
|
||
"Springfield 8\n",
|
||
"Victoria 8\n",
|
||
"San Fernando 8\n",
|
||
"Santa Cruz 7\n",
|
||
"Santa Rosa 7\n",
|
||
"La Paz 7\n",
|
||
"Aurora 7\n",
|
||
"La Unión 7\n",
|
||
"Name: count, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df_real[\"name\"].value_counts().head(10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "95dd03e5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"top_countries = df_real[\"country\"].value_counts().head(10)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8,5))\n",
|
||
"top_countries.plot(kind=\"bar\")\n",
|
||
"plt.title(\"Топ стран по количеству записей\")\n",
|
||
"plt.xlabel(\"Страна\")\n",
|
||
"plt.ylabel(\"Количество\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "b31f9553",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.countplot(data=df_real, y=\"country\", order=df_real[\"country\"].value_counts().head(10).index)\n",
|
||
"plt.title(\"Распределение стран\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "a745a0b1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"top_regions = df_real[\"subcountry\"].value_counts().head(10)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(8,5))\n",
|
||
"top_regions.plot(kind=\"barh\")\n",
|
||
"plt.title(\"Топ регионов\")\n",
|
||
"plt.xlabel(\"Количество\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ef9b3354",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"100%|██████████| 33514/33514 [00:00<00:00, 38718.40it/s]"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Средняя длина названия города: 9.080563346661096\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"ename": "",
|
||
"evalue": "",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
|
||
"\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
|
||
"\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
|
||
"\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"lengths = []\n",
|
||
"\n",
|
||
"for _, row in tqdm(df_real.iterrows(), total=len(df_real)):\n",
|
||
" lengths.append(len(row[\"name\"]))\n",
|
||
"\n",
|
||
"print(\"Средняя длина названия города:\", sum(lengths)/len(lengths))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6e8d3310",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Вывод по реальному датасету\n",
|
||
"\n",
|
||
"В ходе анализа были исследованы данные о географических объектах. \n",
|
||
"Определено распределение записей по странам и регионам, выявлены наиболее часто встречающиеся значения. \n",
|
||
"Построенные графики позволили наглядно оценить структуру данных."
|
||
]
|
||
}
|
||
],
|
||
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|
||
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