1626 lines
235 KiB
Plaintext
1626 lines
235 KiB
Plaintext
{
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"cells": [
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"cell_type": "markdown",
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"metadata": {
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"id": "kU6mKXJpxPwQ"
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},
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"source": [
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"# Лабораторная работа №1\n",
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"## по дисциплине \"Системы искусственного интеллекта\"\n",
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"\n",
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"Лабораторная работа посвящена работе с табличными данными с помощью библиотеки pandas и визуализации с помощью matplotlib.\n",
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"Для выполнения работы вам был предоставлен набор данных, содержащий информацию об атлетах, принявших участие в Летних Олимпийских играх 2024 в Париже.\n",
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"Описание каждой колоноки в наборе данных приведено дальше.\n",
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"\n",
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"code - код спортсмена;\n",
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"\n",
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"name - полное имя спортсмена;\n",
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"\n",
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"name_short - сокращенное имя;\n",
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"\n",
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"name_tv - имя, отображаемое в эфире;\n",
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"\n",
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"gender - пол спортсмена;\n",
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"\n",
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"function - роль на олимпиаде;\n",
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"\n",
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"country_code - код страны;\n",
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"\n",
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"country - название страны;\n",
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"\n",
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"country_full - полное название страны;\n",
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"\n",
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"nationality - сокращенная национальность спортсмена;\n",
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"\n",
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"nationality_full - полная национальность;\n",
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"\n",
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"nationality_code - код национальности;\n",
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"\n",
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"height - рост;\n",
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"\n",
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"weight - вес;\n",
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"\n",
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"disciplines - дисциплины, в которых принимает участие спортсмен;\n",
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"\n",
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"events - состязания, в которых участвует спортсмен;\n",
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"\n",
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"birth_date - дата рождения спортсмена.\n"
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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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"id": "aMR4h5Ok15nq"
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},
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"source": [
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"Импортируйте необходимые библиотеки.\n",
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"\n",
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"Загрузите набор данных из csv-файла. Выведите информацию обо всех колонках, количестве данных в наборе и их статистических показателях (среднее, медиана и т.д.)."
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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": 82,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 900
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},
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"id": "PA0A4lVR2IgW",
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"outputId": "2cdac7bb-35d3-417b-bcde-877a6c53e2d9"
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"df = pd.read_csv('./athletes new.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": 15,
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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>code</th>\n",
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" <th>name</th>\n",
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" <th>name_short</th>\n",
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" <th>name_tv</th>\n",
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" <th>gender</th>\n",
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" <th>function</th>\n",
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" <th>country_code</th>\n",
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" <th>country</th>\n",
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" <th>country_full</th>\n",
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" <th>nationality</th>\n",
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" <th>nationality_full</th>\n",
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" <th>nationality_code</th>\n",
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" <th>height</th>\n",
|
||
" <th>weight</th>\n",
|
||
" <th>disciplines</th>\n",
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" <th>events</th>\n",
|
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" <th>birth_date</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>1535420</td>\n",
|
||
" <td>VALENCIA Alejandra</td>\n",
|
||
" <td>VALENCIA A</td>\n",
|
||
" <td>Alejandra VALENCIA</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>Athlete</td>\n",
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||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
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||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>['Archery']</td>\n",
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||
" <td>[\"Women's Individual\", \"Women's Team\", 'Mixed ...</td>\n",
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" <td>1994-10-17</td>\n",
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
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||
" <td>1535429</td>\n",
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||
" <td>RUIZ Angela</td>\n",
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||
" <td>RUIZ A</td>\n",
|
||
" <td>Angela RUIZ</td>\n",
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||
" <td>Female</td>\n",
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||
" <td>Athlete</td>\n",
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||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>['Archery']</td>\n",
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||
" <td>[\"Women's Individual\", \"Women's Team\"]</td>\n",
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||
" <td>2006-07-28</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>1535430</td>\n",
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" <td>GRANDE Matias</td>\n",
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||
" <td>GRANDE M</td>\n",
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||
" <td>Matias GRANDE</td>\n",
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||
" <td>Male</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Men's Individual\", \"Men's Team\", 'Mixed Team']</td>\n",
|
||
" <td>2004-04-26</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1536460</td>\n",
|
||
" <td>ROJAS Carlos</td>\n",
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||
" <td>ROJAS C</td>\n",
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||
" <td>Carlos ROJAS</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
|
||
" <td>2000-01-14</td>\n",
|
||
" </tr>\n",
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||
" <tr>\n",
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||
" <th>4</th>\n",
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||
" <td>1536467</td>\n",
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||
" <td>MARTINEZ WING Bruno</td>\n",
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||
" <td>MARTINEZ WING B</td>\n",
|
||
" <td>Bruno MARTINEZ WING</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
|
||
" <td>1998-03-08</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" code name name_short name_tv gender \\\n",
|
||
"0 1535420 VALENCIA Alejandra VALENCIA A Alejandra VALENCIA Female \n",
|
||
"1 1535429 RUIZ Angela RUIZ A Angela RUIZ Female \n",
|
||
"2 1535430 GRANDE Matias GRANDE M Matias GRANDE Male \n",
|
||
"3 1536460 ROJAS Carlos ROJAS C Carlos ROJAS Male \n",
|
||
"4 1536467 MARTINEZ WING Bruno MARTINEZ WING B Bruno MARTINEZ WING Male \n",
|
||
"\n",
|
||
" function country_code country country_full nationality nationality_full \\\n",
|
||
"0 Athlete MEX Mexico Mexico Mexico Mexico \n",
|
||
"1 Athlete MEX Mexico Mexico Mexico Mexico \n",
|
||
"2 Athlete MEX Mexico Mexico Mexico Mexico \n",
|
||
"3 Athlete MEX Mexico Mexico Mexico Mexico \n",
|
||
"4 Athlete MEX Mexico Mexico Mexico Mexico \n",
|
||
"\n",
|
||
" nationality_code height weight disciplines \\\n",
|
||
"0 MEX 0 0.0 ['Archery'] \n",
|
||
"1 MEX 0 0.0 ['Archery'] \n",
|
||
"2 MEX 0 0.0 ['Archery'] \n",
|
||
"3 MEX 0 0.0 ['Archery'] \n",
|
||
"4 MEX 0 0.0 ['Archery'] \n",
|
||
"\n",
|
||
" events birth_date \n",
|
||
"0 [\"Women's Individual\", \"Women's Team\", 'Mixed ... 1994-10-17 \n",
|
||
"1 [\"Women's Individual\", \"Women's Team\"] 2006-07-28 \n",
|
||
"2 [\"Men's Individual\", \"Men's Team\", 'Mixed Team'] 2004-04-26 \n",
|
||
"3 [\"Men's Individual\", \"Men's Team\"] 2000-01-14 \n",
|
||
"4 [\"Men's Individual\", \"Men's Team\"] 1998-03-08 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(df.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
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||
{
|
||
"name": "stdout",
|
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"output_type": "stream",
|
||
"text": [
|
||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
"RangeIndex: 11115 entries, 0 to 11114\n",
|
||
"Data columns (total 17 columns):\n",
|
||
" # Column Non-Null Count Dtype \n",
|
||
"--- ------ -------------- ----- \n",
|
||
" 0 code 11115 non-null int64 \n",
|
||
" 1 name 11115 non-null object \n",
|
||
" 2 name_short 11115 non-null object \n",
|
||
" 3 name_tv 11115 non-null object \n",
|
||
" 4 gender 11115 non-null object \n",
|
||
" 5 function 11115 non-null object \n",
|
||
" 6 country_code 11115 non-null object \n",
|
||
" 7 country 11115 non-null object \n",
|
||
" 8 country_full 11115 non-null object \n",
|
||
" 9 nationality 11115 non-null object \n",
|
||
" 10 nationality_full 11115 non-null object \n",
|
||
" 11 nationality_code 11115 non-null object \n",
|
||
" 12 height 11115 non-null int64 \n",
|
||
" 13 weight 11099 non-null float64\n",
|
||
" 14 disciplines 11115 non-null object \n",
|
||
" 15 events 11115 non-null object \n",
|
||
" 16 birth_date 11115 non-null object \n",
|
||
"dtypes: float64(1), int64(2), object(14)\n",
|
||
"memory usage: 1.4+ MB\n"
|
||
]
|
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}
|
||
],
|
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"source": [
|
||
"\n",
|
||
"df.info()"
|
||
]
|
||
},
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{
|
||
"cell_type": "code",
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"execution_count": 17,
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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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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
||
" <th>code</th>\n",
|
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" <th>height</th>\n",
|
||
" <th>weight</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>1.111500e+04</td>\n",
|
||
" <td>11115.000000</td>\n",
|
||
" <td>11099.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>1.887426e+06</td>\n",
|
||
" <td>81.835178</td>\n",
|
||
" <td>2.213713</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>3.587687e+05</td>\n",
|
||
" <td>89.504711</td>\n",
|
||
" <td>13.114771</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>1.532872e+06</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>1.888186e+06</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>1.918842e+06</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>1.948950e+06</td>\n",
|
||
" <td>177.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>9.460001e+06</td>\n",
|
||
" <td>222.000000</td>\n",
|
||
" <td>113.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" code height weight\n",
|
||
"count 1.111500e+04 11115.000000 11099.000000\n",
|
||
"mean 1.887426e+06 81.835178 2.213713\n",
|
||
"std 3.587687e+05 89.504711 13.114771\n",
|
||
"min 1.532872e+06 0.000000 0.000000\n",
|
||
"25% 1.888186e+06 0.000000 0.000000\n",
|
||
"50% 1.918842e+06 0.000000 0.000000\n",
|
||
"75% 1.948950e+06 177.000000 0.000000\n",
|
||
"max 9.460001e+06 222.000000 113.000000"
|
||
]
|
||
},
|
||
"metadata": {},
|
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"output_type": "display_data"
|
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}
|
||
],
|
||
"source": [
|
||
"display(df.describe())"
|
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]
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},
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"metadata": {
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"id": "uesSsuuD2bY7"
|
||
},
|
||
"source": [
|
||
"Проверьте наличие пропусков в данных и заполните их, выбрав стратегию работы с пропусками."
|
||
]
|
||
},
|
||
{
|
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{
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = df.replace(0, np.nan)\n",
|
||
"\n",
|
||
"df.fillna(df.mean())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
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|
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"<style scoped>\n",
|
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|
||
" vertical-align: middle;\n",
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||
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|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
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|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
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|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>height</th>\n",
|
||
" <th>weight</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>count</th>\n",
|
||
" <td>5083.000000</td>\n",
|
||
" <td>332.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>mean</th>\n",
|
||
" <td>178.949046</td>\n",
|
||
" <td>74.112709</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>std</th>\n",
|
||
" <td>11.742262</td>\n",
|
||
" <td>20.545441</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>min</th>\n",
|
||
" <td>140.000000</td>\n",
|
||
" <td>2.213713</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>25%</th>\n",
|
||
" <td>170.000000</td>\n",
|
||
" <td>65.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>50%</th>\n",
|
||
" <td>178.000000</td>\n",
|
||
" <td>75.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>75%</th>\n",
|
||
" <td>187.000000</td>\n",
|
||
" <td>88.000000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>max</th>\n",
|
||
" <td>222.000000</td>\n",
|
||
" <td>113.000000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" height weight\n",
|
||
"count 5083.000000 332.000000\n",
|
||
"mean 178.949046 74.112709\n",
|
||
"std 11.742262 20.545441\n",
|
||
"min 140.000000 2.213713\n",
|
||
"25% 170.000000 65.000000\n",
|
||
"50% 178.000000 75.000000\n",
|
||
"75% 187.000000 88.000000\n",
|
||
"max 222.000000 113.000000"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(df.describe())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "gZZoTM-x4yTI"
|
||
},
|
||
"source": [
|
||
"Удалите колонки code, name_short, name_tv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 573
|
||
},
|
||
"id": "0sV9BdDP5eKg",
|
||
"outputId": "02c246ed-b231-4a2f-d3b4-a05fde29dec4"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
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|
||
" 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>gender</th>\n",
|
||
" <th>function</th>\n",
|
||
" <th>country_code</th>\n",
|
||
" <th>country</th>\n",
|
||
" <th>country_full</th>\n",
|
||
" <th>nationality</th>\n",
|
||
" <th>nationality_full</th>\n",
|
||
" <th>nationality_code</th>\n",
|
||
" <th>height</th>\n",
|
||
" <th>weight</th>\n",
|
||
" <th>disciplines</th>\n",
|
||
" <th>events</th>\n",
|
||
" <th>birth_date</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>VALENCIA Alejandra</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Women's Individual\", \"Women's Team\", 'Mixed ...</td>\n",
|
||
" <td>1994-10-17</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>RUIZ Angela</td>\n",
|
||
" <td>Female</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Women's Individual\", \"Women's Team\"]</td>\n",
|
||
" <td>2006-07-28</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>GRANDE Matias</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Men's Individual\", \"Men's Team\", 'Mixed Team']</td>\n",
|
||
" <td>2004-04-26</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>ROJAS Carlos</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
|
||
" <td>2000-01-14</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>MARTINEZ WING Bruno</td>\n",
|
||
" <td>Male</td>\n",
|
||
" <td>Athlete</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>Mexico</td>\n",
|
||
" <td>MEX</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>['Archery']</td>\n",
|
||
" <td>[\"Men's Individual\", \"Men's Team\"]</td>\n",
|
||
" <td>1998-03-08</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" name gender function country_code country country_full \\\n",
|
||
"0 VALENCIA Alejandra Female Athlete MEX Mexico Mexico \n",
|
||
"1 RUIZ Angela Female Athlete MEX Mexico Mexico \n",
|
||
"2 GRANDE Matias Male Athlete MEX Mexico Mexico \n",
|
||
"3 ROJAS Carlos Male Athlete MEX Mexico Mexico \n",
|
||
"4 MARTINEZ WING Bruno Male Athlete MEX Mexico Mexico \n",
|
||
"\n",
|
||
" nationality nationality_full nationality_code height weight disciplines \\\n",
|
||
"0 Mexico Mexico MEX NaN NaN ['Archery'] \n",
|
||
"1 Mexico Mexico MEX NaN NaN ['Archery'] \n",
|
||
"2 Mexico Mexico MEX NaN NaN ['Archery'] \n",
|
||
"3 Mexico Mexico MEX NaN NaN ['Archery'] \n",
|
||
"4 Mexico Mexico MEX NaN NaN ['Archery'] \n",
|
||
"\n",
|
||
" events birth_date \n",
|
||
"0 [\"Women's Individual\", \"Women's Team\", 'Mixed ... 1994-10-17 \n",
|
||
"1 [\"Women's Individual\", \"Women's Team\"] 2006-07-28 \n",
|
||
"2 [\"Men's Individual\", \"Men's Team\", 'Mixed Team'] 2004-04-26 \n",
|
||
"3 [\"Men's Individual\", \"Men's Team\"] 2000-01-14 \n",
|
||
"4 [\"Men's Individual\", \"Men's Team\"] 1998-03-08 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = df.drop(['code', 'name_short', 'name_tv'], axis=1)\n",
|
||
"display(df.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "Q6MxGujF5ecN"
|
||
},
|
||
"source": [
|
||
"Из какой страны было больше всего атлетов? Сколько их было?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "tzcVTPBj5tpi",
|
||
"outputId": "1d9037dc-d87b-4fbb-ccbf-7f90fc4a1909"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"United States 620\n",
|
||
"France 600\n",
|
||
"Australia 476\n",
|
||
"Germany 457\n",
|
||
"Japan 432\n",
|
||
" ... \n",
|
||
"Tuvalu 2\n",
|
||
"Belize 1\n",
|
||
"Somalia 1\n",
|
||
"Nauru 1\n",
|
||
"Liechtenstein 1\n",
|
||
"Name: country, Length: 206, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"countries_athletes = df['country'].value_counts()\n",
|
||
"display(countries_athletes)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "MCnLpnXz5t4_"
|
||
},
|
||
"source": [
|
||
"Найдите 15 стран, из которых было больше всего спортсменов. Постройте гистограмму, где по горизонтальной оси будет отображаться код страны, а по вертикальной - количество спортсменов."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 550
|
||
},
|
||
"id": "lTAa0uOc6nJz",
|
||
"outputId": "acfcdd4a-6ae8-47ab-a8d6-1e38ca8c4ffb"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"countries_athletes.head(15).plot(kind='bar')\n",
|
||
"plt.xlabel('Страна')\n",
|
||
"plt.ylabel('Количество атлетов')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "uA-Ae_Wa67zb"
|
||
},
|
||
"source": [
|
||
"Сколько женщин и мужчин участвовало в Олимпиаде?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 41,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "wBM2BwJR8lPH",
|
||
"outputId": "d7eaa205-d514-4a68-8476-7d6691b975ad"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Male 5655\n",
|
||
"Female 5460\n",
|
||
"Name: gender, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(df['gender'].value_counts())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ZrJDz_ZU8lcB"
|
||
},
|
||
"source": [
|
||
"Выведите количество женщин, которые участвовали только в одной соревновательной группе (колонка events)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 109,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "EwCr00ny-CZn",
|
||
"outputId": "59772e20-94b0-445e-a4f2-755c94615499"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"4280"
|
||
]
|
||
},
|
||
"execution_count": 109,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"female_athletes = df[df['gender'] == 'Female']\n",
|
||
"len(female_athletes[female_athletes['events'].apply(lambda events: len(eval(events)) == 1)])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "_5__lGjSBvBW"
|
||
},
|
||
"source": [
|
||
"Выведите количество спортсменов, у которых национальность не совпадает со страной, за которую они выступают."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "khISD9_HB2LF",
|
||
"outputId": "4bd4b01e-4dbd-4e2b-c3ec-7984004c66d5"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"203"
|
||
]
|
||
},
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"len(df[df['nationality'] != df['country']])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "B9kM7gIK-Cla"
|
||
},
|
||
"source": [
|
||
"Какой процент мужчин моложе 23 участвовало в этой Олимпиаде?"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 91,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "YcEA3Ani-JHv",
|
||
"outputId": "1db7412c-2d7d-494a-96e8-31db4312b5a5"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"12.18%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from datetime import date\n",
|
||
"\n",
|
||
"df['birth_date'] = pd.to_datetime(df['birth_date'])\n",
|
||
"\n",
|
||
"today = pd.to_datetime(date.today())\n",
|
||
"\n",
|
||
"df['age'] = df['birth_date'].apply(\n",
|
||
" lambda bd: today.year - bd.year - 1 if (today.month, today.day) < (bd.month, bd.day) else today.year - bd.year\n",
|
||
")\n",
|
||
"\n",
|
||
"male_athletes = df[df['gender'] == 'Male']\n",
|
||
"young_male_athletes = male_athletes[male_athletes['age'] < 23]\n",
|
||
"print(f'{round(len(young_male_athletes) / len(male_athletes) * 100, 2)}%')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "uK1liVxz-JVi"
|
||
},
|
||
"source": [
|
||
"Постройте график зависимости роста от веса у женщин (scatter plot)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 211
|
||
},
|
||
"id": "D37_Ncmj-e_2",
|
||
"outputId": "663e3b39-d1c1-4711-f5c2-dcc29ea97785"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Use the df DataFrame with filled weight values and filter for female athletes\n",
|
||
"female = df[df['gender'] == 'Female']\n",
|
||
"\n",
|
||
"plt.scatter(female['height'], female['weight'])\n",
|
||
"plt.xlabel('Рост (см)')\n",
|
||
"plt.ylabel('Вес (кг)')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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DAN8RZgAARhmRnqTFE/tozsr8oO251BJ+MaR7g4+TVuUV1qvf27YI8A1hBgBgnBHpScpIc7p7Nj7bV6JXztgzKdQy0pxej6/KK9TU5Vvr9Sq5tmFgTI3/GAAMADBSZIRNg3p00k1XdtVvR6WFuhy3xqZe19ZZmrMyv8GtDqRTWx3U+rPcMQgzAADzbT/4fahLkNT01OtNBSWNPho7fasD+I4wAwAwXii2BrDZpJj2kR7Hmpp67WudbHXgH8bMAACMF4qtAZ4dd6VGXp7s14wkX+tkqwP/EGYAAEbxNqV5QGqCEmMd+k9FdavV8fg7uzTy8mS/dr4ekJqgpPgoFZVVeR0341qbhq0O/EOYAQAYo6EpzefFtm/VICNJReXVWrTmK/3qup4+fyYywqasMWmaunyrbPJcJ4etDgLHmBkAgBFcU5rPHEBbWFalz78pD0lNz7z/pVblFfr1Gdc6Oc54z0dJbHUQOHpmAABhr7EpzaE2Z2W+MtKcfvWmnLlODisANw9hBgAQtlzjYz756ruwXe3XNZXan7Ez0g/r5KD5CDMAgLDkbXxMuGIqdWgRZgAAYaehJf/DFVOpQ4swAwAIK+E8PuZMTKUOD8xmAgCElaaW/A8XTKUOH/TMAADCiinjT2w2acrQVKZShwF6ZgAAYSXcxp+MuCzR6/E6S1qyrsDvdWYQfIQZAEBYcS35H+oHNzZJzjiHth8sa/S8OSvzVVtnwgiftoswAwAIK64l/yWFNNBYkm4dcIGKyht+7GXph3VmEDqEGQBA2Gloyf/W9vk33/t0ninjfNoqBgADAMLS6Uv+f7znW/3xo72tXsOaL7716bxwG+dztqFnBgAQNmrrLH2694je3H5In+49Ikka1KOTBl/UOST1WJJio9o1+LjLplO7drPOTGjRMwMACAveti9Iio9S1pg0VZ+sC1ldfS7oqHVffiub5LGQH+vMhA96ZgAAIefavuDMxfKKyqo0dflW7fvuWIgqk4b17Ox1/I4zPkqLJ/ZhnZkwQM8MACCkGtu+wNKpHpC/bTogZ1yU/lNe1arbHETYpJ8P6q727SLc43eKK6rUJfbUoyV6ZMIDYQYAEFJNbV9gSSoqr9Z9112sZ97/svUK06kVftu3O/UQIzLCpkE9OrXq/eGbkD9mOnTokCZOnKhOnTopJiZGV155pbZs2eJ+37IsZWdnKzk5WdHR0Ro+fLh27twZwooBAMHk67Tm7p1jlJIQ3cLV/CAjrYtmj0prtfshcCENM6WlpRoyZIjsdrveffdd5efn6+mnn9a5557rPmf+/PlasGCBFi1apM2bN8vpdCojI0MVFRWhKxwAEDS+Tmt++dN9OlhyvIWr+cH7+cVsVWCIkD5meuqpp5SSkqIXX3zRfax79+7u/7YsSwsXLtRDDz2ksWPHSpKWLVumxMRE5eTk6K677mrtkgEAQebavqCozPt4GJukxLgobdn/fStXdmqrgow0J2NjwlxIw8xbb72l66+/Xj/72c+0du1ade3aVdOmTdOUKVMkSQUFBSoqKlJmZqb7Mw6HQ1dffbU2bNjgNcxUV1erurra/bq8vFySVFNTo5qamqDW77pesK+LlkW7mYl2M5Ov7fboDf+l+17bLsn79OfLkmJUeqz1emVcSo4e18avis+qdWTC5bvmz/1tlmWFbHesqKhTXYszZ87Uz372M23atEkzZszQn/70J912223asGGDhgwZokOHDik5Odn9uTvvvFP79+/Xe++9V++a2dnZmjNnTr3jOTk5iomJabnfDAAACJrKykqNHz9eZWVliouLa/TckPbM1NXVqV+/fpo7d64kqXfv3tq5c6cWL16s2267zX2ezebZvWdZVr1jLrNnz9bMmTPdr8vLy5WSkqLMzMwm/zD8VVNTo9zcXGVkZMhutwf12mg5tJuZaDcz+dtutXWWtuwv1XdHq9X5HIf6duuoyAibHn87X69uPtgKFdf3l0n9z7qemXD4rrmerPgipGEmKSlJaWmeI8UvvfRS/fOf/5QkOZ1OSVJRUZGSkn5YlKi4uFiJiYler+lwOORwOOodt9vtLdYoLXlttBzazUy0m5l8bTe7pCEX1//7/TcjLtOyjd+0QGUNs+nUwnhXXdTlrBwzE+rvmj/3DulspiFDhmj37t0ex7788kt169ZNkpSamiqn06nc3Fz3+ydOnNDatWs1ePDgVq0VABA6Ow6VheS+bFVghpD2zNx3330aPHiw5s6dq//+7//Wpk2btGTJEi1ZskTSqcdLM2bM0Ny5c9WzZ0/17NlTc+fOVUxMjMaPHx/K0gEArcjXtWj8ZbNJ3kaOuvaEYqsCM4Q0zPTv318rVqzQ7Nmz9dhjjyk1NVULFy7UhAkT3Oc88MADOn78uKZNm6bS0lINHDhQq1evVmxsbAgrBwC0Jl/XovFXYqxDT//sShVXVKnk2AklnOOQM46tCkwT8u0MRo8erdGjRzf4vs1mU3Z2trKzs1uvKABAWGlqLZpAFZVXKyLCphuv7OredwnmCXmYAQCgKZERNmWNSdPdy7cG/dq5+UWa+fftHvtD8ZjJLCHfmwkAgGA4NyawmTd/+WRfvY0ui8qqNHX5VrYzMARhBgAQ9mrrLM1Zmd/g+zZJ0fZI/fWOgXrmv69QQof2Pl23oWExrkdZc1bmq7YuZGvLwkeEGQBASNTWWfp07xG9uf2QPt17pNHQsKmgpF7vyeksSYVlVYqIsOnmPudr7s3p8mX4bmM5xXXNTQUlPlwJocSYGQBAq1uVV6g5K/N9Hqfi68Bc13kj0pN057BULV1f0Ghg8eeaCF/0zAAAWtWqvEJNXb7Vr3Eqvk7Ndp23Kq9QS9Y1P8j4c2+EDmEGANBqXGNfvGWMxsap9O3WscHxLS4RtlPnNXYPf9h0qrfobNqXyVSEGQBAq/F17MvGvUc8jm/ZX9pkL0uddeq8pu7hC1duYjsDMxBmAACtxtfxJ/fkeD5u8mfMTDDGuDjjo7R4Yh/WmTEEA4ABAK3G1/En3x+v0dTlW92Bwt8xM4F45IZL1TnWoS6xbGdgGsIMAKDV+LstwZyV+cpIczb5OZtO9aa4xrf4cw/XZycPSSXAGIrHTACAVuPalsAXp6/z4vpcQ+HE0g/jW06/R1PRhLExbQNhBgDQqkakJ2nxxD46N9q37QcCGQPjuocz3vOx05l5hbExbQOPmQAArW5EepJio+ya8Of/a/LcLrFRPm1n4Hok5ephGZGepIw0p3s37C6xUerbraO27C91v2ZsTNtAmAEAhMRVF3byeRyMr1O6NxWUaFCPTu7jkRE2j9eS6r2G+XjMBAAIicbGtpw5lsXf7QxwdiHMAABCpqGxLWeOZWmNqdkwF4+ZAAAh5W1sy5ljWfydmo2zC2EGABBy3sa2nPl+1pg0TV2+VTbJI9AwvRo8ZgIAGGFEepLuHJYq2xl5xWaT7hyWyvTqsxhhBgBghFV5hVqyrqDehpN1lrRkXYHHXk44uxBmAABhz7XOTGPbE8xZma/aprbWRptEmAEAhD1/1pk5XW2dpU/3HtGb2w/p071HCDttlN8DgC3L0tq1a7V+/Xrt27dPlZWVOu+889S7d29dd911SklJaYk6AQBnsUDWmVmVV6g5K/M9QlBSfJSyxqQxvqaN8bln5vjx45o7d65SUlI0cuRIvf322/r+++8VGRmpr776SllZWUpNTdWoUaO0cePGlqwZAHCW8XedmVV5hZq6fGu93pyisipNXb6V8TVtjM89MxdffLEGDhyo559/Xtdff73s9vobhO3fv185OTkaN26cHn74YU2ZMiWoxQIAzk7+rDPT2PgaS973cYLZfO6Zeffdd/WPf/xDo0eP9hpkJKlbt26aPXu29uzZo+HDhwerRgDAWc6frQ8CHV8Dc/kcZtLT032+aPv27dWzZ8+ACgIAwBtftz5gH6ezT0ArAA8YMEDvvfeeOnbsGOx6AABokC9bH7CP09knoDDz2WefqaamJti1AADQpKa2PmAfp7MP68wAANoUf8bXoG0IKMzYbDbZztwcAwCAMOHr+Bq0DQE9ZrIsS5MnT5bD4fD6/uuvv96sogAAaC5fxtegbQgozNx22230zAAAwl5T42vQNgQUZl566aUglwEAABCYgMbMnDhxosH3Vq1aFXAxAAAA/goozIwcOVKVlZUexyoqKnTHHXfopz/9aVAKAwAA8EVAYaZdu3a69tprVVZWJklavXq10tLS9OWXX2rbtm1BLRAAAKAxAYWZf/3rX0pOTtbQoUN1xx136Oabb9Z9992ndevWsY0BAABoVQENALbb7frHP/6h22+/XS+99JLeffddZWZmBrs2AACAJgUUZj7//HNJ0syZM3X48GH98pe/1CuvvOLeq+nyyy8PXoUAAACNCCjMXHnlle51Zizr1M4X11xzjaRTqwPX1tYGqTwAAIDGBRRmCgoKgl0HAABAQAIKM926dQt2HQAAAAHxeTbTp59+6vNFjx07pp07dwZUEAAAgD98DjO33XabMjIy9Pe//11Hjx71ek5+fr5++9vf6qKLLtLWrVuDViQAAEBDfH7MlJ+frz/96U969NFHNWHCBF188cVKTk5WVFSUSktL9cUXX+jYsWMaO3ascnNzlZ6e3pJ1AwAASPIjzNjtdk2fPl3Tp0/X1q1btX79eu3bt0/Hjx/XFVdcofvuu0/XXHONEhISWrJeAAAADwENAO7Tp4/69OkT7FoAAAD8FtB2BgAAAOGCMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNECCjP/8z//o//93/+td3zRokWaMWNGc2sCAADwWUBh5p///KeGDBlS7/jgwYP1j3/8o9lFAQAA+CqgMHPkyBHFx8fXOx4XF6fvvvuu2UUBAAD4KqAwc9FFF2nVqlX1jr/77ru68MILm10UAACArwJaAXjmzJmaPn26vv32W1177bWSpA8++EBPP/20Fi5cGMz6AAAAGhVQmPnFL36h6upqPfHEE/rd734nSerevbsWL16s2267LagFAgAANCagMCNJU6dO1dSpU/Xtt98qOjpa55xzTjDrAgAA8EnA68ycPHlS77//vl5//XVZliVJOnz4sI4ePRq04gAAAJoSUM/M/v37NWLECB04cEDV1dXKyMhQbGys5s+fr6qqKj3//PPBrhMAAMCrgHpmfvWrX6lfv34qLS1VdHS0+/jNN9+sDz74IGjFAQAANCWgMPPxxx/r4YcfVvv27T2Od+vWTYcOHQqokHnz5slms3msIGxZlrKzs5WcnKzo6GgNHz5cO3fuDOj6AACgbQoozNTV1am2trbe8W+++UaxsbF+X2/z5s1asmSJLr/8co/j8+fP14IFC7Ro0SJt3rxZTqdTGRkZqqioCKRsAADQBgUUZjIyMjzWk7HZbDp69KiysrI0atQov6519OhRTZgwQUuXLlXHjh3dxy3L0sKFC/XQQw9p7NixSk9P17Jly1RZWamcnJxAygYAAG1QQAOAn3nmGV1zzTVKS0tTVVWVxo8frz179qhz587629/+5te17rnnHt1www267rrr9Pjjj7uPFxQUqKioSJmZme5jDodDV199tTZs2KC77rrL6/Wqq6tVXV3tfl1eXi5JqqmpUU1NjV+1NcV1vWBfFy2LdjMT7WYm2s084dJm/tw/oDCTnJys7du369VXX9WWLVtUV1enO+64QxMmTPAYENwU1+c/++yzeu8VFRVJkhITEz2OJyYmav/+/Q1ec968eZozZ06946tXr1ZMTIzPtfkjNze3Ra6LlkW7mYl2MxPtZp5Qt1llZaXP5wa8aF50dLRuv/123X777QF9/uDBg/rVr36l1atXKyoqqsHzbDabx2vLsuodO93s2bM1c+ZM9+vy8nKlpKQoMzNTcXFxAdXakJqaGuXm5iojI0N2uz2o10bLod3MRLuZiXYzT7i0mevJii8CCjNHjhxRp06dJJ0KJUuXLtXx48c1ZswYDRs2zKdrbNmyRcXFxerbt6/7WG1trdatW6dFixZp9+7dkk710CQlJbnPKS4urtdbczqHwyGHw1HvuN1ub7FGaclro+XQbmai3cxEu5kn1G3mz739GgC8Y8cOde/eXV26dNEll1yi7du3q3///nrmmWe0ZMkSXXvttXrjjTd8utaPf/xj7dixQ9u3b3f/6tevnyZMmKDt27frwgsvlNPp9OjmOnHihNauXavBgwf7UzYAAGjD/AozDzzwgHr16qW1a9dq+PDhGj16tEaNGqWysjKVlpbqrrvu0pNPPunTtWJjY5Wenu7xq0OHDurUqZPS09Pda87MnTtXK1asUF5eniZPnqyYmBiNHz8+oN8sAABoe/x6zLR582atWbNGl19+ua688kotWbJE06ZNU0TEqUx077336qqrrgpacQ888ICOHz+uadOmqbS0VAMHDtTq1asDWssGAAC0TX6FmZKSEjmdTknSOeecow4dOighIcH9fseOHZu1oN1HH33k8dpmsyk7O1vZ2dkBXxMAALRtfi+ad+ZMosZmFgEAALQ0v2czTZ482T1bqKqqSnfffbc6dOggSR6L1QEAALQGv8LMpEmTPF5PnDix3jm33XZb8yoCAADwg19h5sUXX2ypOgAAAAIS0EaTAAAA4YIwAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjhTTMzJs3T/3791dsbKy6dOmin/zkJ9q9e7fHOZZlKTs7W8nJyYqOjtbw4cO1c+fOEFUMAADCTUjDzNq1a3XPPfdo48aNys3N1cmTJ5WZmaljx465z5k/f74WLFigRYsWafPmzXI6ncrIyFBFRUUIKwcAAOGiXShvvmrVKo/XL774orp06aItW7Zo2LBhsixLCxcu1EMPPaSxY8dKkpYtW6bExETl5OTorrvuqnfN6upqVVdXu1+Xl5dLkmpqalRTUxPU+l3XC/Z10bJoNzPRbmai3cwTLm3mz/1tlmVZLViLX7766iv17NlTO3bsUHp6ur7++mv16NFDW7duVe/evd3n3XTTTTr33HO1bNmyetfIzs7WnDlz6h3PyclRTExMi9YPAACCo7KyUuPHj1dZWZni4uIaPTdswoxlWbrppptUWlqq9evXS5I2bNigIUOG6NChQ0pOTnafe+edd2r//v1677336l3HW89MSkqKvvvuuyb/MPxVU1Oj3NxcZWRkyG63B/XaaDm0m5loNzPRbuYJlzYrLy9X586dfQozIX3MdLrp06fr888/18cff1zvPZvN5vHasqx6x1wcDoccDke943a7vcUapSWvjZZDu5mJdjMT7WaeULeZP/cOi6nZ9957r9566y19+OGHOv/8893HnU6nJKmoqMjj/OLiYiUmJrZqjQAAIDyFNMxYlqXp06fr9ddf15o1a5SamurxfmpqqpxOp3Jzc93HTpw4obVr12rw4MGtXS4AAAhDIX3MdM899ygnJ0dvvvmmYmNj3T0w8fHxio6Ols1m04wZMzR37lz17NlTPXv21Ny5cxUTE6Px48eHsnQAABAmQhpmFi9eLEkaPny4x/EXX3xRkydPliQ98MADOn78uKZNm6bS0lINHDhQq1evVmxsbCtXCwAAwlFIw4wvE6lsNpuys7OVnZ3d8gUBAADjhMUAYAAAgEARZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAEAAEYjzAAAAKMRZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYrV2oCzBV91lvyxFpaf4AKT37PVXX2rTvyRtCXRYA+K22ztKmghIVV1SpS2yUBqQmKDLC5tdni8qrVHK0WnHRdn3+zfeSpPPOaa9Vnx/WXT2k659Zq2svS9Sf1x9wfzZCks0mxbSPVLxD+qa8tgV+d4iU5PqTtUm6NDFGJcdr1S7Cpv7d47V5X5lO1lnqem60/ji+rxZ/tFt9bNLv/pWvH1+apO+OVmv7N9/rZG2dvvzPUUXZI3TheefowRGXasehsoB+boLNiDDz3HPP6fe//70KCwt12WWXaeHChRo6dGjI6uk+6+0GjxNoAJhkVV6h5qzMV2FZlftYUnyUssakaUR6kt+fPZMj0pJ6SIfKqjyCjCTVSZIlVVTXqqK6Ob8LNOb0iGhJyv9Ppfv1N9t/aLui8mpd9eQHckRa6jNAeu2zg3r5/77xes1P9pbolY2e7enrz01LCPvHTK+99ppmzJihhx56SNu2bdPQoUM1cuRIHThwoOkPt4CGgoyv7wNAuFiVV6ipy7fWCyNFZVWaunyrVuUV+v1ZnL18+blpKWEfZhYsWKA77rhDv/zlL3XppZdq4cKFSklJ0eLFi1u9Fl+DCoEGQLirrbM0Z2W+LC/vuY7NWZmv2rr6ZzT2WZy9mvq5aUlh/ZjpxIkT2rJli2bNmuVxPDMzUxs2bPD6merqalVX/9BfWV5eLkmqqalRTU1Ns+pxRHo2jiPC8vjf0zX3Xmg5rrahjcxCuwXXpoISlRw9Lkdkw+eUHD2ujV8Va0Bqgt+fdWns70mEp+a2WUM/N/7y57tusywrbH/CDh8+rK5du+qTTz7R4MGD3cfnzp2rZcuWaffu3fU+k52drTlz5tQ7npOTo5iYmBatFwAABEdlZaXGjx+vsrIyxcXFNXpuWPfMuNhsnqOjLcuqd8xl9uzZmjlzpvt1eXm5UlJSlJmZ2eQfRlPSs9/zeO2IsPS7fnV65LMIVdd51pOXfX2z7oWWU1NTo9zcXGVkZMhut4e6HPiIdguuTQUl+sWyzU2e95dJ/b32zPjyWanxvycRnoLRZt5+bvzlerLii7AOM507d1ZkZKSKioo8jhcXFysxMdHrZxwOhxwOR73jdru92X8BVtd6b9TqOlu99/jLNvwF42cCrY92C46rLuqihHOiVVRW5XXsi02SMz5KV13Upd5026Y+6423vycR3gJps8Z+bvzlz/c8rAcAt2/fXn379lVubq7H8dzcXI/HTq3F12nXTM8GEO4iI2zKGpMm6dQ/QKdzvc4ak+b1H6TTPwu4NPVz05LCOsxI0syZM/XnP/9Zf/nLX7Rr1y7dd999OnDggO6+++6Q1NNUUCHIADDFiPQkLZ7YR874KI/jzvgoLZ7Yp9H1QlyfTTrjszh7+fJz01LC+jGTJI0bN05HjhzRY489psLCQqWnp+udd95Rt27dQlbTvidv8Dr9miADwDQj0pOUkeYMaAXg0z/b2ArAUrm6xkexAnCIBLICsLRP4/qlGLMCcFjPZgqG8vJyxcfH+zQa2l81NTV65513NGrUKJ7hG4R2MxPtZibazTzh0mb+/Psd9o+ZAAAAGkOYAQAARiPMAAAAoxFmAACA0QgzAADAaIQZAABgNMIMAAAwGmEGAAAYjTADAACMFvbbGTSXa4Fjf7YS91VNTY0qKytVXl7OypYGod3MRLuZiXYzT7i0mevfbV82KmjzYaaiokKSlJKSEuJKAACAvyoqKhQfH9/oOW1+b6a6ujodPnxYsbGxstmCuwFWeXm5UlJSdPDgwaDv+4SWQ7uZiXYzE+1mnnBpM8uyVFFRoeTkZEVEND4qps33zEREROj8889v0XvExcXxJTUQ7WYm2s1MtJt5wqHNmuqRcWEAMAAAMBphBgAAGI0w0wwOh0NZWVlyOByhLgV+oN3MRLuZiXYzj4lt1uYHAAMAgLaNnhkAAGA0wgwAADAaYQYAABiNMAMAAIxGmAnQc889p9TUVEVFRalv375av359qEvCabKzs2Wz2Tx+OZ1O9/uWZSk7O1vJycmKjo7W8OHDtXPnzhBWfHZat26dxowZo+TkZNlsNr3xxhse7/vSTtXV1br33nvVuXNndejQQTfeeKO++eabVvxdnH2aarfJkyfX+/5dddVVHufQbq1r3rx56t+/v2JjY9WlSxf95Cc/0e7duz3OMfn7RpgJwGuvvaYZM2booYce0rZt2zR06FCNHDlSBw4cCHVpOM1ll12mwsJC968dO3a435s/f74WLFigRYsWafPmzXI6ncrIyHDv5YXWcezYMV1xxRVatGiR1/d9aacZM2ZoxYoVevXVV/Xxxx/r6NGjGj16tGpra1vrt3HWaardJGnEiBEe37933nnH433arXWtXbtW99xzjzZu3Kjc3FydPHlSmZmZOnbsmPsco79vFvw2YMAA6+677/Y4dskll1izZs0KUUU4U1ZWlnXFFVd4fa+urs5yOp3Wk08+6T5WVVVlxcfHW88//3wrVYgzSbJWrFjhfu1LO33//feW3W63Xn31Vfc5hw4dsiIiIqxVq1a1Wu1nszPbzbIsa9KkSdZNN93U4Gdot9ArLi62JFlr1661LMv87xs9M346ceKEtmzZoszMTI/jmZmZ2rBhQ4iqgjd79uxRcnKyUlNTdcstt+jrr7+WJBUUFKioqMijDR0Oh66++mraMIz40k5btmxRTU2NxznJyclKT0+nLUPso48+UpcuXXTxxRdrypQpKi4udr9Hu4VeWVmZJCkhIUGS+d83woyfvvvuO9XW1ioxMdHjeGJiooqKikJUFc40cOBAvfzyy3rvvfe0dOlSFRUVafDgwTpy5Ii7nWjD8OZLOxUVFal9+/bq2LFjg+eg9Y0cOVJ//etftWbNGj399NPavHmzrr32WlVXV0ui3ULNsizNnDlTP/rRj5Seni7J/O9bm981u6XYbDaP15Zl1TuG0Bk5cqT7v3v16qVBgwapR48eWrZsmXsgIm1ohkDaibYMrXHjxrn/Oz09Xf369VO3bt309ttva+zYsQ1+jnZrHdOnT9fnn3+ujz/+uN57pn7f6JnxU+fOnRUZGVkvhRYXF9dLtAgfHTp0UK9evbRnzx73rCbaMLz50k5Op1MnTpxQaWlpg+cg9JKSktStWzft2bNHEu0WSvfee6/eeustffjhhzr//PPdx03/vhFm/NS+fXv17dtXubm5Hsdzc3M1ePDgEFWFplRXV2vXrl1KSkpSamqqnE6nRxueOHFCa9eupQ3DiC/t1LdvX9ntdo9zCgsLlZeXR1uGkSNHjujgwYNKSkqSRLuFgmVZmj59ul5//XWtWbNGqampHu8b/30L2dBjg7366quW3W63XnjhBSs/P9+aMWOG1aFDB2vfvn2hLg3/z/3332999NFH1tdff21t3LjRGj16tBUbG+tuoyeffNKKj4+3Xn/9dWvHjh3WrbfeaiUlJVnl5eUhrvzsUlFRYW3bts3atm2bJclasGCBtW3bNmv//v2WZfnWTnfffbd1/vnnW++//761detW69prr7WuuOIK6+TJk6H6bbV5jbVbRUWFdf/991sbNmywCgoKrA8//NAaNGiQ1bVrV9othKZOnWrFx8dbH330kVVYWOj+VVlZ6T7H5O8bYSZAf/zjH61u3bpZ7du3t/r06eOe3obwMG7cOCspKcmy2+1WcnKyNXbsWGvnzp3u9+vq6qysrCzL6XRaDofDGjZsmLVjx44QVnx2+vDDDy1J9X5NmjTJsizf2un48ePW9OnTrYSEBCs6OtoaPXq0deDAgRD8bs4ejbVbZWWllZmZaZ133nmW3W63LrjgAmvSpEn12oR2a13e2kuS9eKLL7rPMfn7ZrMsy2rt3iAAAIBgYcwMAAAwGmEGAAAYjTADAACMRpgBAABGI8wAAACjEWYAAIDRCDMAAMBohBkAAGA0wgyANunIkSPq0qWL9u3bF9TrFhcX67zzztOhQ4eCel0AgSPMAGiWyZMny2azyWazyW6368ILL9Svf/1rHTt2LKR1zZs3T2PGjFH37t2Det0uXbro5z//ubKysoJ6XQCBI8wAaLYRI0aosLBQX3/9tR5//HE999xz+vWvfx2yeo4fP64XXnhBv/zlL1vk+rfffrv++te/qrS0tEWuD8A/hBkAzeZwOOR0OpWSkqLx48drwoQJeuONN9zvr127VgMGDJDD4VBSUpJmzZqlkydPut+vq6vTU089pYsuukgOh0MXXHCBnnjiCUly9/p4+/XRRx95refdd99Vu3btNGjQII/jO3fu1A033KC4uDjFxsZq6NCh2rt3r6QfepgWLFjg8Zmf/OQnstlseumll9zHevXqJafTqRUrVjTjTw1AsBBmAARddHS0ampqJEmHDh3SqFGj1L9/f/373//W4sWL9cILL+jxxx93nz979mw99dRTeuSRR5Sfn6+cnBwlJiZKkgoLC92/JOmf//yn+/XgwYO93n/dunXq16+fx7FDhw5p2LBhioqK0po1a7Rlyxb94he/8AhVXbt21dKlS92vCwsLtWHDBsXExNS7x4ABA7R+/foA/4QABFO7UBcAoG3ZtGmTcnJy9OMf/1iS9NxzzyklJUWLFi2SzWbTJZdcosOHD+vBBx/Uo48+qmPHjukPf/iDFi1apEmTJkmSevTooR/96EeSJKfT6XH9hISEesfOtG/fPiUnJ3sc++Mf/6j4+Hi9+uqrstvtkqSLL77Y45x+/fqpoKBA69ev19ChQ/XCCy/olltu0csvv1zvHl27dtW2bdv8+JMB0FLomQHQbP/61790zjnnKCoqSoMGDdKwYcP07LPPSpJ27dqlQYMGyWazuc8fMmSIjh49qm+++Ua7du1SdXW1O/wEw/HjxxUVFeVxbPv27Ro6dKg7yDRkypQpWrJkierq6vTCCy9oypQpXs+Ljo5WZWVl0GoGEDh6ZgA02zXXXKPFixfLbrcrOTnZIzBYluURZFzHpFPjYaKjo4NeT+fOnesNzvX1Pq6ZSq+++qqcTqd69erl9bySkhKdd955za4VQPPRMwOg2Tp06KCLLrpI3bp1q9fzkZaWpg0bNrgDjCRt2LBBsbGx6tq1q3r27Kno6Gh98MEHQaund+/eys/P9zh2+eWXa/369e6xPA2Jj4/XjTfeqLvvvrvBXhlJysvLU+/evYNSL4DmIcwAaFHTpk3TwYMHde+99+qLL77Qm2++qaysLM2cOVMRERGKiorSgw8+qAceeEAvv/yy9u7dq40bN+qFF14I+J7XX3+9du7c6dE7M336dJWXl+uWW27RZ599pj179uiVV17R7t27631+1qxZ+u1vf6tx48Z5vX5lZaW2bNmizMzMgGsEEDyEGQAtqmvXrnrnnXe0adMmXXHFFbr77rt1xx136OGHH3af88gjj+j+++/Xo48+qksvvVTjxo1TcXFxwPfs1auX+vXrp7///e/uY506ddKaNWt09OhRXX311erbt6+WLl3qdQzNf/3Xf2nWrFnq0KGD1+u/+eabuuCCCzR06NCAawQQPDbr9L5fAGgj3nnnHf36179WXl6eIiKC+//bBgwYoBkzZmj8+PFBvS6AwDAAGECbNGrUKO3Zs0eHDh1SSkpK0K5bXFysn/70p7r11luDdk0AzUPPDAAAMBpjZgAAgNEIMwAAwGiEGQAAYDTCDAAAMBphBgAAGI0wAwAAjEaYAQAARiPMAAAAoxFmAACA0f5/r7YASz7JMsUAAAAASUVORK5CYII=",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"raw = pd.read_csv('./athletes new.csv')\n",
|
||
"\n",
|
||
"female = raw[raw['gender'] == 'Female']\n",
|
||
"\n",
|
||
"plt.scatter(female['height'], female['weight'])\n",
|
||
"plt.xlabel('Рост (см)')\n",
|
||
"plt.ylabel('Вес (кг)')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 108,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"filtered = raw[(raw['weight'] != 0) & (raw['height'] != 0)]\n",
|
||
"\n",
|
||
"female = filtered[filtered['gender'] == 'Female']\n",
|
||
"\n",
|
||
"plt.scatter(female['height'], female['weight'])\n",
|
||
"plt.xlabel('Рост (см)')\n",
|
||
"plt.ylabel('Вес (кг)')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "PE57eW19-fNM"
|
||
},
|
||
"source": [
|
||
"Постройте график распределения, где по оси X будет показан возраст, а по оси Y - процент спортсменов этого возраста, принявших участие в Олимпиаде для женщин и мужчин на одном графике."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 98,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 564
|
||
},
|
||
"id": "GPiewHIz_IUg",
|
||
"outputId": "df0ffc07-85ef-446e-e0cf-e26819daef45"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"male_athletes = df[df['gender'] == 'Male']\n",
|
||
"female_athletes = df[df['gender'] == 'Female']\n",
|
||
"\n",
|
||
"male_counts = male_athletes['age'].value_counts(normalize=True).sort_index() * 100\n",
|
||
"female_counts = female_athletes['age'].value_counts(normalize=True).sort_index() * 100\n",
|
||
"\n",
|
||
"plt.plot(male_counts.index, male_counts.values, label='Мужчины')\n",
|
||
"plt.plot(female_counts.index, female_counts.values, label='Женщины')\n",
|
||
"\n",
|
||
"plt.xlabel('Возраст')\n",
|
||
"plt.ylabel('Процент спортсменов')\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "9ocREq3J_IsU"
|
||
},
|
||
"source": [
|
||
"Постройте круговую диаграмму, чтобы отобразить долю спортсменов по пяти странам с наибольшим количеством участников."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 96,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 675
|
||
},
|
||
"id": "PkpF_i1V_dgH",
|
||
"outputId": "f4d544fc-8499-4d25-89b9-21959b92542c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"countries_counts = df['country'].value_counts().head(5)\n",
|
||
"\n",
|
||
"plt.pie(countries_counts, labels=countries_counts.index, autopct='%1.1f%%')\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "base",
|
||
"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": "3.9.13"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 0
|
||
}
|