2208 lines
339 KiB
Plaintext
2208 lines
339 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Практическая работа №1 \n",
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"## по предмету \"Системы искусственного интеллекта\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"В данной практической работе Вы будете работать с базой данных, посвященной баскетболу, которая включает в себя информацию об игроках, играх и командах. \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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"source": [
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"### Задание 1\n",
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"Вам необходимо запустить в docker базу данных PostgreSQL и выгрузить туда все данные из файла nba.sqlite, приложенного к лабороторной работе.\n",
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"Выгрузить данные можно с помощью утилиты pgloader (https://pgloader.readthedocs.io/en/latest/ref/sqlite.html)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Задание 2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Defaulting to user installation because normal site-packages is not writeable\n",
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"Collecting psycopg2-binary\n",
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" Downloading psycopg2_binary-2.9.10-cp39-cp39-win_amd64.whl (1.2 MB)\n",
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" ---------------------------------------- 1.2/1.2 MB 3.0 MB/s eta 0:00:00\n",
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"Installing collected packages: psycopg2-binary\n",
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"Successfully installed psycopg2-binary-2.9.10\n"
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]
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}
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],
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"source": [
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"!pip install psycopg2-binary"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Загрузите данные из таблицы . Посмотрите, какие есть типы игр в сезоне (season_type) и количество этих игр."
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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": 77,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"import psycopg2\n",
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"import pandas as pd\n",
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"\n",
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"conn = psycopg2.connect(\n",
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" dbname='nba',\n",
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" user='admin',\n",
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" password='admin',\n",
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" host='localhost',\n",
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" port=5432\n",
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")\n",
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"\n",
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"df = pd.read_sql('SELECT * from game', conn)"
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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": 78,
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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",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
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||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<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>season_id</th>\n",
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" <th>team_id_home</th>\n",
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" <th>team_abbreviation_home</th>\n",
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" <th>team_name_home</th>\n",
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||
" <th>game_id</th>\n",
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||
" <th>game_date</th>\n",
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" <th>matchup_home</th>\n",
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" <th>wl_home</th>\n",
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" <th>min</th>\n",
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" <th>fgm_home</th>\n",
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||
" <th>...</th>\n",
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||
" <th>reb_away</th>\n",
|
||
" <th>ast_away</th>\n",
|
||
" <th>stl_away</th>\n",
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||
" <th>blk_away</th>\n",
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||
" <th>tov_away</th>\n",
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||
" <th>pf_away</th>\n",
|
||
" <th>pts_away</th>\n",
|
||
" <th>plus_minus_away</th>\n",
|
||
" <th>video_available_away</th>\n",
|
||
" <th>season_type</th>\n",
|
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" </tr>\n",
|
||
" </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>21946</td>\n",
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" <td>1610610035</td>\n",
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||
" <td>HUS</td>\n",
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||
" <td>Toronto Huskies</td>\n",
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||
" <td>0024600001</td>\n",
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||
" <td>1946-11-01</td>\n",
|
||
" <td>HUS vs. NYK</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>68.0</td>\n",
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||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</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>21946</td>\n",
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" <td>1610610034</td>\n",
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||
" <td>BOM</td>\n",
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||
" <td>St. Louis Bombers</td>\n",
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||
" <td>0024600003</td>\n",
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||
" <td>1946-11-02</td>\n",
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||
" <td>BOM vs. PIT</td>\n",
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||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
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||
" <td>20.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>51.0</td>\n",
|
||
" <td>-5</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610032</td>\n",
|
||
" <td>PRO</td>\n",
|
||
" <td>Providence Steamrollers</td>\n",
|
||
" <td>0024600002</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>PRO vs. BOS</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>53.0</td>\n",
|
||
" <td>-6</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610025</td>\n",
|
||
" <td>CHS</td>\n",
|
||
" <td>Chicago Stags</td>\n",
|
||
" <td>0024600004</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>CHS vs. NYK</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>47.0</td>\n",
|
||
" <td>-16</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610028</td>\n",
|
||
" <td>DEF</td>\n",
|
||
" <td>Detroit Falcons</td>\n",
|
||
" <td>0024600005</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>DEF vs. WAS</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>50.0</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 55 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" season_id team_id_home team_abbreviation_home team_name_home \\\n",
|
||
"0 21946 1610610035 HUS Toronto Huskies \n",
|
||
"1 21946 1610610034 BOM St. Louis Bombers \n",
|
||
"2 21946 1610610032 PRO Providence Steamrollers \n",
|
||
"3 21946 1610610025 CHS Chicago Stags \n",
|
||
"4 21946 1610610028 DEF Detroit Falcons \n",
|
||
"\n",
|
||
" game_id game_date matchup_home wl_home min fgm_home ... reb_away \\\n",
|
||
"0 0024600001 1946-11-01 HUS vs. NYK L 0 25.0 ... NaN \n",
|
||
"1 0024600003 1946-11-02 BOM vs. PIT W 0 20.0 ... NaN \n",
|
||
"2 0024600002 1946-11-02 PRO vs. BOS W 0 21.0 ... NaN \n",
|
||
"3 0024600004 1946-11-02 CHS vs. NYK W 0 21.0 ... NaN \n",
|
||
"4 0024600005 1946-11-02 DEF vs. WAS L 0 10.0 ... NaN \n",
|
||
"\n",
|
||
" ast_away stl_away blk_away tov_away pf_away pts_away plus_minus_away \\\n",
|
||
"0 NaN NaN NaN NaN NaN 68.0 2 \n",
|
||
"1 NaN NaN NaN NaN 25.0 51.0 -5 \n",
|
||
"2 NaN NaN NaN NaN NaN 53.0 -6 \n",
|
||
"3 NaN NaN NaN NaN 22.0 47.0 -16 \n",
|
||
"4 NaN NaN NaN NaN NaN 50.0 17 \n",
|
||
"\n",
|
||
" video_available_away season_type \n",
|
||
"0 0 Regular Season \n",
|
||
"1 0 Regular Season \n",
|
||
"2 0 Regular Season \n",
|
||
"3 0 Regular Season \n",
|
||
"4 0 Regular Season \n",
|
||
"\n",
|
||
"[5 rows x 55 columns]"
|
||
]
|
||
},
|
||
"execution_count": 78,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 79,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Regular Season 60192\n",
|
||
"Playoffs 3842\n",
|
||
"Pre Season 1536\n",
|
||
"All Star 65\n",
|
||
"All-Star 63\n",
|
||
"Name: season_type, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 79,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df['season_type'].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 80,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Regular Season 60192\n",
|
||
"Playoffs 3842\n",
|
||
"Pre Season 1536\n",
|
||
"All Star 128\n",
|
||
"Name: season_type, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 80,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.replace('All-Star', 'All Star', inplace=True)\n",
|
||
"df['season_type'].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Нам понадобятся только регулярные игры сезона и play-off. Отделите эти игры в новый датафрейм."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 81,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Regular Season 60192\n",
|
||
"Playoffs 3842\n",
|
||
"Name: season_type, dtype: int64"
|
||
]
|
||
},
|
||
"execution_count": 81,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df = df[(df['season_type'] == 'Regular Season') | (df['season_type'] == 'Playoffs')]\n",
|
||
"df['season_type'].value_counts()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"В датах игр выделите только год, убрав день и месяц, в которых была проведена игра"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 82,
|
||
"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>season_id</th>\n",
|
||
" <th>team_id_home</th>\n",
|
||
" <th>team_abbreviation_home</th>\n",
|
||
" <th>team_name_home</th>\n",
|
||
" <th>game_id</th>\n",
|
||
" <th>game_date</th>\n",
|
||
" <th>matchup_home</th>\n",
|
||
" <th>wl_home</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>fgm_home</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>stl_away</th>\n",
|
||
" <th>blk_away</th>\n",
|
||
" <th>tov_away</th>\n",
|
||
" <th>pf_away</th>\n",
|
||
" <th>pts_away</th>\n",
|
||
" <th>plus_minus_away</th>\n",
|
||
" <th>video_available_away</th>\n",
|
||
" <th>season_type</th>\n",
|
||
" <th>game_date_pd</th>\n",
|
||
" <th>game_year</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610035</td>\n",
|
||
" <td>HUS</td>\n",
|
||
" <td>Toronto Huskies</td>\n",
|
||
" <td>0024600001</td>\n",
|
||
" <td>1946-11-01</td>\n",
|
||
" <td>HUS vs. NYK</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>68.0</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-01</td>\n",
|
||
" <td>1946</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610034</td>\n",
|
||
" <td>BOM</td>\n",
|
||
" <td>St. Louis Bombers</td>\n",
|
||
" <td>0024600003</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>BOM vs. PIT</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>20.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>51.0</td>\n",
|
||
" <td>-5</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610032</td>\n",
|
||
" <td>PRO</td>\n",
|
||
" <td>Providence Steamrollers</td>\n",
|
||
" <td>0024600002</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>PRO vs. BOS</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>53.0</td>\n",
|
||
" <td>-6</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610025</td>\n",
|
||
" <td>CHS</td>\n",
|
||
" <td>Chicago Stags</td>\n",
|
||
" <td>0024600004</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>CHS vs. NYK</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>47.0</td>\n",
|
||
" <td>-16</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610028</td>\n",
|
||
" <td>DEF</td>\n",
|
||
" <td>Detroit Falcons</td>\n",
|
||
" <td>0024600005</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>DEF vs. WAS</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>50.0</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 57 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" season_id team_id_home team_abbreviation_home team_name_home \\\n",
|
||
"0 21946 1610610035 HUS Toronto Huskies \n",
|
||
"1 21946 1610610034 BOM St. Louis Bombers \n",
|
||
"2 21946 1610610032 PRO Providence Steamrollers \n",
|
||
"3 21946 1610610025 CHS Chicago Stags \n",
|
||
"4 21946 1610610028 DEF Detroit Falcons \n",
|
||
"\n",
|
||
" game_id game_date matchup_home wl_home min fgm_home ... stl_away \\\n",
|
||
"0 0024600001 1946-11-01 HUS vs. NYK L 0 25.0 ... NaN \n",
|
||
"1 0024600003 1946-11-02 BOM vs. PIT W 0 20.0 ... NaN \n",
|
||
"2 0024600002 1946-11-02 PRO vs. BOS W 0 21.0 ... NaN \n",
|
||
"3 0024600004 1946-11-02 CHS vs. NYK W 0 21.0 ... NaN \n",
|
||
"4 0024600005 1946-11-02 DEF vs. WAS L 0 10.0 ... NaN \n",
|
||
"\n",
|
||
" blk_away tov_away pf_away pts_away plus_minus_away \\\n",
|
||
"0 NaN NaN NaN 68.0 2 \n",
|
||
"1 NaN NaN 25.0 51.0 -5 \n",
|
||
"2 NaN NaN NaN 53.0 -6 \n",
|
||
"3 NaN NaN 22.0 47.0 -16 \n",
|
||
"4 NaN NaN NaN 50.0 17 \n",
|
||
"\n",
|
||
" video_available_away season_type game_date_pd game_year \n",
|
||
"0 0 Regular Season 1946-11-01 1946 \n",
|
||
"1 0 Regular Season 1946-11-02 1946 \n",
|
||
"2 0 Regular Season 1946-11-02 1946 \n",
|
||
"3 0 Regular Season 1946-11-02 1946 \n",
|
||
"4 0 Regular Season 1946-11-02 1946 \n",
|
||
"\n",
|
||
"[5 rows x 57 columns]"
|
||
]
|
||
},
|
||
"execution_count": 82,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df['game_date_pd'] = pd.to_datetime(df['game_date'])\n",
|
||
"df['game_year'] = df['game_date_pd'].apply(lambda date: date.year)\n",
|
||
"df.head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Нас интересует общий счет, поэтому уберите столбцы pts_home и pts_away, заменив их стобцом с их суммой"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 83,
|
||
"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>season_id</th>\n",
|
||
" <th>team_id_home</th>\n",
|
||
" <th>team_abbreviation_home</th>\n",
|
||
" <th>team_name_home</th>\n",
|
||
" <th>game_id</th>\n",
|
||
" <th>game_date</th>\n",
|
||
" <th>matchup_home</th>\n",
|
||
" <th>wl_home</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>fgm_home</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>stl_away</th>\n",
|
||
" <th>blk_away</th>\n",
|
||
" <th>tov_away</th>\n",
|
||
" <th>pf_away</th>\n",
|
||
" <th>plus_minus_away</th>\n",
|
||
" <th>video_available_away</th>\n",
|
||
" <th>season_type</th>\n",
|
||
" <th>game_date_pd</th>\n",
|
||
" <th>game_year</th>\n",
|
||
" <th>pts</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610035</td>\n",
|
||
" <td>HUS</td>\n",
|
||
" <td>Toronto Huskies</td>\n",
|
||
" <td>0024600001</td>\n",
|
||
" <td>1946-11-01</td>\n",
|
||
" <td>HUS vs. NYK</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-01</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>134.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610034</td>\n",
|
||
" <td>BOM</td>\n",
|
||
" <td>St. Louis Bombers</td>\n",
|
||
" <td>0024600003</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>BOM vs. PIT</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>20.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>-5</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>107.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610032</td>\n",
|
||
" <td>PRO</td>\n",
|
||
" <td>Providence Steamrollers</td>\n",
|
||
" <td>0024600002</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>PRO vs. BOS</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>-6</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>112.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610025</td>\n",
|
||
" <td>CHS</td>\n",
|
||
" <td>Chicago Stags</td>\n",
|
||
" <td>0024600004</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>CHS vs. NYK</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>-16</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>110.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610028</td>\n",
|
||
" <td>DEF</td>\n",
|
||
" <td>Detroit Falcons</td>\n",
|
||
" <td>0024600005</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>DEF vs. WAS</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>83.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 56 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" season_id team_id_home team_abbreviation_home team_name_home \\\n",
|
||
"0 21946 1610610035 HUS Toronto Huskies \n",
|
||
"1 21946 1610610034 BOM St. Louis Bombers \n",
|
||
"2 21946 1610610032 PRO Providence Steamrollers \n",
|
||
"3 21946 1610610025 CHS Chicago Stags \n",
|
||
"4 21946 1610610028 DEF Detroit Falcons \n",
|
||
"\n",
|
||
" game_id game_date matchup_home wl_home min fgm_home ... stl_away \\\n",
|
||
"0 0024600001 1946-11-01 HUS vs. NYK L 0 25.0 ... NaN \n",
|
||
"1 0024600003 1946-11-02 BOM vs. PIT W 0 20.0 ... NaN \n",
|
||
"2 0024600002 1946-11-02 PRO vs. BOS W 0 21.0 ... NaN \n",
|
||
"3 0024600004 1946-11-02 CHS vs. NYK W 0 21.0 ... NaN \n",
|
||
"4 0024600005 1946-11-02 DEF vs. WAS L 0 10.0 ... NaN \n",
|
||
"\n",
|
||
" blk_away tov_away pf_away plus_minus_away video_available_away \\\n",
|
||
"0 NaN NaN NaN 2 0 \n",
|
||
"1 NaN NaN 25.0 -5 0 \n",
|
||
"2 NaN NaN NaN -6 0 \n",
|
||
"3 NaN NaN 22.0 -16 0 \n",
|
||
"4 NaN NaN NaN 17 0 \n",
|
||
"\n",
|
||
" season_type game_date_pd game_year pts \n",
|
||
"0 Regular Season 1946-11-01 1946 134.0 \n",
|
||
"1 Regular Season 1946-11-02 1946 107.0 \n",
|
||
"2 Regular Season 1946-11-02 1946 112.0 \n",
|
||
"3 Regular Season 1946-11-02 1946 110.0 \n",
|
||
"4 Regular Season 1946-11-02 1946 83.0 \n",
|
||
"\n",
|
||
"[5 rows x 56 columns]"
|
||
]
|
||
},
|
||
"execution_count": 83,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df['pts'] = df['pts_home'] + df['pts_away']\n",
|
||
"df.drop(columns=['pts_home', 'pts_away'], inplace=True)\n",
|
||
"df.head(5)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Постройте график, где по оси Х будет год игр, а по оси Y - среднее количество очков за игру в этом году."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 84,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 85,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"avg_points = df.groupby('game_year')['pts'].mean().reset_index()\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12,6))\n",
|
||
"plt.plot(avg_points['game_year'], avg_points['pts'])\n",
|
||
"plt.xlabel('Год')\n",
|
||
"plt.ylabel('Среднее количество очков')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Задание 3"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Выполните запрос к таблице draft_history, получив датафрейм."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 60,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"draft_history = pd.read_sql('SELECT * from draft_history', conn)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 61,
|
||
"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>person_id</th>\n",
|
||
" <th>player_name</th>\n",
|
||
" <th>season</th>\n",
|
||
" <th>round_number</th>\n",
|
||
" <th>round_pick</th>\n",
|
||
" <th>overall_pick</th>\n",
|
||
" <th>draft_type</th>\n",
|
||
" <th>team_id</th>\n",
|
||
" <th>team_city</th>\n",
|
||
" <th>team_name</th>\n",
|
||
" <th>team_abbreviation</th>\n",
|
||
" <th>organization</th>\n",
|
||
" <th>organization_type</th>\n",
|
||
" <th>player_profile_flag</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>79299</td>\n",
|
||
" <td>Clifton McNeeley</td>\n",
|
||
" <td>1947</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Draft</td>\n",
|
||
" <td>1610610031</td>\n",
|
||
" <td>Pittsburgh</td>\n",
|
||
" <td>Ironmen</td>\n",
|
||
" <td>PIT</td>\n",
|
||
" <td>Texas-El Paso</td>\n",
|
||
" <td>College/University</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>78109</td>\n",
|
||
" <td>Glen Selbo</td>\n",
|
||
" <td>1947</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>Draft</td>\n",
|
||
" <td>1610610035</td>\n",
|
||
" <td>Toronto</td>\n",
|
||
" <td>Huskies</td>\n",
|
||
" <td>HUS</td>\n",
|
||
" <td>Wisconsin</td>\n",
|
||
" <td>College/University</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>76649</td>\n",
|
||
" <td>Eddie Ehlers</td>\n",
|
||
" <td>1947</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>Draft</td>\n",
|
||
" <td>1610612738</td>\n",
|
||
" <td>Boston</td>\n",
|
||
" <td>Celtics</td>\n",
|
||
" <td>BOS</td>\n",
|
||
" <td>Purdue</td>\n",
|
||
" <td>College/University</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>79302</td>\n",
|
||
" <td>Walt Dropo</td>\n",
|
||
" <td>1947</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>Draft</td>\n",
|
||
" <td>1610610032</td>\n",
|
||
" <td>Providence</td>\n",
|
||
" <td>Steamrollers</td>\n",
|
||
" <td>PRO</td>\n",
|
||
" <td>Connecticut</td>\n",
|
||
" <td>College/University</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>77048</td>\n",
|
||
" <td>Dick Holub</td>\n",
|
||
" <td>1947</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>Draft</td>\n",
|
||
" <td>1610612752</td>\n",
|
||
" <td>New York</td>\n",
|
||
" <td>Knicks</td>\n",
|
||
" <td>NYK</td>\n",
|
||
" <td>Long Island-Brooklyn</td>\n",
|
||
" <td>College/University</td>\n",
|
||
" <td>1</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" person_id player_name season round_number round_pick overall_pick \\\n",
|
||
"0 79299 Clifton McNeeley 1947 1 1 1 \n",
|
||
"1 78109 Glen Selbo 1947 1 2 2 \n",
|
||
"2 76649 Eddie Ehlers 1947 1 3 3 \n",
|
||
"3 79302 Walt Dropo 1947 1 4 4 \n",
|
||
"4 77048 Dick Holub 1947 1 5 5 \n",
|
||
"\n",
|
||
" draft_type team_id team_city team_name team_abbreviation \\\n",
|
||
"0 Draft 1610610031 Pittsburgh Ironmen PIT \n",
|
||
"1 Draft 1610610035 Toronto Huskies HUS \n",
|
||
"2 Draft 1610612738 Boston Celtics BOS \n",
|
||
"3 Draft 1610610032 Providence Steamrollers PRO \n",
|
||
"4 Draft 1610612752 New York Knicks NYK \n",
|
||
"\n",
|
||
" organization organization_type player_profile_flag \n",
|
||
"0 Texas-El Paso College/University 0 \n",
|
||
"1 Wisconsin College/University 1 \n",
|
||
"2 Purdue College/University 1 \n",
|
||
"3 Connecticut College/University 0 \n",
|
||
"4 Long Island-Brooklyn College/University 1 "
|
||
]
|
||
},
|
||
"execution_count": 61,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"draft_history.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Постройте график, где по оси X будет год, а по оси Y - количество выбранных игроков в этот год (каждая строка в таблице - выбранный игрок)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 62,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"players = draft_history.groupby('season').size().reset_index(name='num_players')\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12,6))\n",
|
||
"plt.plot(players['season'], players['num_players'])\n",
|
||
"plt.xlabel('Год')\n",
|
||
"plt.ylabel('Количество игроков')\n",
|
||
"plt.grid(True)\n",
|
||
"plt.xticks(rotation=90)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Задание 4"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Необходимо построить график, показывающий топ 10 школ, игроки из которых были выбраны. Для этого вам понадобятся таблицы common_player_info и draft_history."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 63,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"common_player_info = pd.read_sql('SELECT * from common_player_info', conn)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 64,
|
||
"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>person_id</th>\n",
|
||
" <th>first_name</th>\n",
|
||
" <th>last_name</th>\n",
|
||
" <th>display_first_last</th>\n",
|
||
" <th>display_last_comma_first</th>\n",
|
||
" <th>display_fi_last</th>\n",
|
||
" <th>player_slug</th>\n",
|
||
" <th>birthdate</th>\n",
|
||
" <th>school</th>\n",
|
||
" <th>country</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>playercode</th>\n",
|
||
" <th>from_year</th>\n",
|
||
" <th>to_year</th>\n",
|
||
" <th>dleague_flag</th>\n",
|
||
" <th>nba_flag</th>\n",
|
||
" <th>games_played_flag</th>\n",
|
||
" <th>draft_year</th>\n",
|
||
" <th>draft_round</th>\n",
|
||
" <th>draft_number</th>\n",
|
||
" <th>greatest_75_flag</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>76001</td>\n",
|
||
" <td>Alaa</td>\n",
|
||
" <td>Abdelnaby</td>\n",
|
||
" <td>Alaa Abdelnaby</td>\n",
|
||
" <td>Abdelnaby, Alaa</td>\n",
|
||
" <td>A. Abdelnaby</td>\n",
|
||
" <td>alaa-abdelnaby</td>\n",
|
||
" <td>1968-06-24</td>\n",
|
||
" <td>Duke</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_alaa_abdelnaby</td>\n",
|
||
" <td>1990.0</td>\n",
|
||
" <td>1994.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1990</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>76002</td>\n",
|
||
" <td>Zaid</td>\n",
|
||
" <td>Abdul-Aziz</td>\n",
|
||
" <td>Zaid Abdul-Aziz</td>\n",
|
||
" <td>Abdul-Aziz, Zaid</td>\n",
|
||
" <td>Z. Abdul-Aziz</td>\n",
|
||
" <td>zaid-abdul-aziz</td>\n",
|
||
" <td>1946-04-07</td>\n",
|
||
" <td>Iowa State</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_zaid_abdul-aziz</td>\n",
|
||
" <td>1968.0</td>\n",
|
||
" <td>1977.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1968</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>76003</td>\n",
|
||
" <td>Kareem</td>\n",
|
||
" <td>Abdul-Jabbar</td>\n",
|
||
" <td>Kareem Abdul-Jabbar</td>\n",
|
||
" <td>Abdul-Jabbar, Kareem</td>\n",
|
||
" <td>K. Abdul-Jabbar</td>\n",
|
||
" <td>kareem-abdul-jabbar</td>\n",
|
||
" <td>1947-04-16</td>\n",
|
||
" <td>UCLA</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_kareem_abdul-jabbar</td>\n",
|
||
" <td>1969.0</td>\n",
|
||
" <td>1988.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1969</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Y</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>949</td>\n",
|
||
" <td>Shareef</td>\n",
|
||
" <td>Abdur-Rahim</td>\n",
|
||
" <td>Shareef Abdur-Rahim</td>\n",
|
||
" <td>Abdur-Rahim, Shareef</td>\n",
|
||
" <td>S. Abdur-Rahim</td>\n",
|
||
" <td>shareef-abdur-rahim</td>\n",
|
||
" <td>1976-12-11</td>\n",
|
||
" <td>California</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>shareef_abdur-rahim</td>\n",
|
||
" <td>1996.0</td>\n",
|
||
" <td>2007.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1996</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>76006</td>\n",
|
||
" <td>Forest</td>\n",
|
||
" <td>Able</td>\n",
|
||
" <td>Forest Able</td>\n",
|
||
" <td>Able, Forest</td>\n",
|
||
" <td>F. Able</td>\n",
|
||
" <td>forest-able</td>\n",
|
||
" <td>1932-07-27</td>\n",
|
||
" <td>Western Kentucky</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_frosty_able</td>\n",
|
||
" <td>1956.0</td>\n",
|
||
" <td>1956.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1956</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 33 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" person_id first_name last_name display_first_last \\\n",
|
||
"0 76001 Alaa Abdelnaby Alaa Abdelnaby \n",
|
||
"1 76002 Zaid Abdul-Aziz Zaid Abdul-Aziz \n",
|
||
"2 76003 Kareem Abdul-Jabbar Kareem Abdul-Jabbar \n",
|
||
"3 949 Shareef Abdur-Rahim Shareef Abdur-Rahim \n",
|
||
"4 76006 Forest Able Forest Able \n",
|
||
"\n",
|
||
" display_last_comma_first display_fi_last player_slug birthdate \\\n",
|
||
"0 Abdelnaby, Alaa A. Abdelnaby alaa-abdelnaby 1968-06-24 \n",
|
||
"1 Abdul-Aziz, Zaid Z. Abdul-Aziz zaid-abdul-aziz 1946-04-07 \n",
|
||
"2 Abdul-Jabbar, Kareem K. Abdul-Jabbar kareem-abdul-jabbar 1947-04-16 \n",
|
||
"3 Abdur-Rahim, Shareef S. Abdur-Rahim shareef-abdur-rahim 1976-12-11 \n",
|
||
"4 Able, Forest F. Able forest-able 1932-07-27 \n",
|
||
"\n",
|
||
" school country ... playercode from_year \\\n",
|
||
"0 Duke USA ... HISTADD_alaa_abdelnaby 1990.0 \n",
|
||
"1 Iowa State USA ... HISTADD_zaid_abdul-aziz 1968.0 \n",
|
||
"2 UCLA USA ... HISTADD_kareem_abdul-jabbar 1969.0 \n",
|
||
"3 California USA ... shareef_abdur-rahim 1996.0 \n",
|
||
"4 Western Kentucky USA ... HISTADD_frosty_able 1956.0 \n",
|
||
"\n",
|
||
" to_year dleague_flag nba_flag games_played_flag draft_year draft_round \\\n",
|
||
"0 1994.0 N Y Y 1990 1 \n",
|
||
"1 1977.0 N Y Y 1968 1 \n",
|
||
"2 1988.0 N Y Y 1969 1 \n",
|
||
"3 2007.0 N Y Y 1996 1 \n",
|
||
"4 1956.0 N Y Y 1956 None \n",
|
||
"\n",
|
||
" draft_number greatest_75_flag \n",
|
||
"0 25 N \n",
|
||
"1 5 N \n",
|
||
"2 1 Y \n",
|
||
"3 3 N \n",
|
||
"4 None N \n",
|
||
"\n",
|
||
"[5 rows x 33 columns]"
|
||
]
|
||
},
|
||
"execution_count": 64,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"common_player_info.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 73,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"merged = pd.merge(draft_history, common_player_info[['person_id', 'school']], on='person_id', how='left')\n",
|
||
"\n",
|
||
"school_counts = merged.groupby('school').size().reset_index(name='num_players')\n",
|
||
"\n",
|
||
"school_counts = school_counts.sort_values(by='num_players').tail(10)\n",
|
||
"\n",
|
||
"plt.figure(figsize=(12,6))\n",
|
||
"plt.barh(school_counts['school'], school_counts['num_players'])\n",
|
||
"plt.xlabel(\"Количество игроков\")\n",
|
||
"plt.ylabel(\"Школа\")\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Задание 5"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Постройте график сравнения выигранных домашних и выездных игр в каждом году. Для этого используйте таблицу game."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 86,
|
||
"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>season_id</th>\n",
|
||
" <th>team_id_home</th>\n",
|
||
" <th>team_abbreviation_home</th>\n",
|
||
" <th>team_name_home</th>\n",
|
||
" <th>game_id</th>\n",
|
||
" <th>game_date</th>\n",
|
||
" <th>matchup_home</th>\n",
|
||
" <th>wl_home</th>\n",
|
||
" <th>min</th>\n",
|
||
" <th>fgm_home</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>stl_away</th>\n",
|
||
" <th>blk_away</th>\n",
|
||
" <th>tov_away</th>\n",
|
||
" <th>pf_away</th>\n",
|
||
" <th>plus_minus_away</th>\n",
|
||
" <th>video_available_away</th>\n",
|
||
" <th>season_type</th>\n",
|
||
" <th>game_date_pd</th>\n",
|
||
" <th>game_year</th>\n",
|
||
" <th>pts</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610035</td>\n",
|
||
" <td>HUS</td>\n",
|
||
" <td>Toronto Huskies</td>\n",
|
||
" <td>0024600001</td>\n",
|
||
" <td>1946-11-01</td>\n",
|
||
" <td>HUS vs. NYK</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-01</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>134.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610034</td>\n",
|
||
" <td>BOM</td>\n",
|
||
" <td>St. Louis Bombers</td>\n",
|
||
" <td>0024600003</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>BOM vs. PIT</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>20.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>-5</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>107.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610032</td>\n",
|
||
" <td>PRO</td>\n",
|
||
" <td>Providence Steamrollers</td>\n",
|
||
" <td>0024600002</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>PRO vs. BOS</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>-6</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>112.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610025</td>\n",
|
||
" <td>CHS</td>\n",
|
||
" <td>Chicago Stags</td>\n",
|
||
" <td>0024600004</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>CHS vs. NYK</td>\n",
|
||
" <td>W</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>21.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>22.0</td>\n",
|
||
" <td>-16</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>110.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>21946</td>\n",
|
||
" <td>1610610028</td>\n",
|
||
" <td>DEF</td>\n",
|
||
" <td>Detroit Falcons</td>\n",
|
||
" <td>0024600005</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>DEF vs. WAS</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>10.0</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>Regular Season</td>\n",
|
||
" <td>1946-11-02</td>\n",
|
||
" <td>1946</td>\n",
|
||
" <td>83.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 56 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" season_id team_id_home team_abbreviation_home team_name_home \\\n",
|
||
"0 21946 1610610035 HUS Toronto Huskies \n",
|
||
"1 21946 1610610034 BOM St. Louis Bombers \n",
|
||
"2 21946 1610610032 PRO Providence Steamrollers \n",
|
||
"3 21946 1610610025 CHS Chicago Stags \n",
|
||
"4 21946 1610610028 DEF Detroit Falcons \n",
|
||
"\n",
|
||
" game_id game_date matchup_home wl_home min fgm_home ... stl_away \\\n",
|
||
"0 0024600001 1946-11-01 HUS vs. NYK L 0 25.0 ... NaN \n",
|
||
"1 0024600003 1946-11-02 BOM vs. PIT W 0 20.0 ... NaN \n",
|
||
"2 0024600002 1946-11-02 PRO vs. BOS W 0 21.0 ... NaN \n",
|
||
"3 0024600004 1946-11-02 CHS vs. NYK W 0 21.0 ... NaN \n",
|
||
"4 0024600005 1946-11-02 DEF vs. WAS L 0 10.0 ... NaN \n",
|
||
"\n",
|
||
" blk_away tov_away pf_away plus_minus_away video_available_away \\\n",
|
||
"0 NaN NaN NaN 2 0 \n",
|
||
"1 NaN NaN 25.0 -5 0 \n",
|
||
"2 NaN NaN NaN -6 0 \n",
|
||
"3 NaN NaN 22.0 -16 0 \n",
|
||
"4 NaN NaN NaN 17 0 \n",
|
||
"\n",
|
||
" season_type game_date_pd game_year pts \n",
|
||
"0 Regular Season 1946-11-01 1946 134.0 \n",
|
||
"1 Regular Season 1946-11-02 1946 107.0 \n",
|
||
"2 Regular Season 1946-11-02 1946 112.0 \n",
|
||
"3 Regular Season 1946-11-02 1946 110.0 \n",
|
||
"4 Regular Season 1946-11-02 1946 83.0 \n",
|
||
"\n",
|
||
"[5 rows x 56 columns]"
|
||
]
|
||
},
|
||
"execution_count": 86,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 90,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"df['wl_away'] = df['wl_home'].map({'W': 'L', 'L': 'W'})\n",
|
||
"\n",
|
||
"home_wins = df[df['wl_home']=='W'].groupby('game_year').size().reset_index(name='home_wins')\n",
|
||
"away_wins = df[df['wl_away']=='W'].groupby('game_year').size().reset_index(name='away_wins')\n",
|
||
"\n",
|
||
"wins = pd.merge(home_wins, away_wins, on='game_year', how='outer')\n",
|
||
"\n",
|
||
"# строим график\n",
|
||
"plt.figure(figsize=(12,6))\n",
|
||
"plt.plot(wins['game_year'], wins['home_wins'], label='Домашние победы')\n",
|
||
"plt.plot(wins['game_year'], wins['away_wins'], label='Выездные победы')\n",
|
||
"plt.xlabel(\"Год\")\n",
|
||
"plt.ylabel(\"Количество побед\")\n",
|
||
"plt.legend()\n",
|
||
"plt.grid(True)\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Задание 6"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Какова средняя продолжительность карьеры активного игрока NBA? Используйте таблицы common_player_info и player. Активный игрок - это тот, у которого в колонке is_active стоит 1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 91,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"c:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\sql.py:762: UserWarning: pandas only support SQLAlchemy connectable(engine/connection) ordatabase string URI or sqlite3 DBAPI2 connectionother DBAPI2 objects are not tested, please consider using SQLAlchemy\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"player = pd.read_sql('SELECT * from player', conn)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 93,
|
||
"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>person_id</th>\n",
|
||
" <th>first_name</th>\n",
|
||
" <th>last_name</th>\n",
|
||
" <th>display_first_last</th>\n",
|
||
" <th>display_last_comma_first</th>\n",
|
||
" <th>display_fi_last</th>\n",
|
||
" <th>player_slug</th>\n",
|
||
" <th>birthdate</th>\n",
|
||
" <th>school</th>\n",
|
||
" <th>country</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>playercode</th>\n",
|
||
" <th>from_year</th>\n",
|
||
" <th>to_year</th>\n",
|
||
" <th>dleague_flag</th>\n",
|
||
" <th>nba_flag</th>\n",
|
||
" <th>games_played_flag</th>\n",
|
||
" <th>draft_year</th>\n",
|
||
" <th>draft_round</th>\n",
|
||
" <th>draft_number</th>\n",
|
||
" <th>greatest_75_flag</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>76001</td>\n",
|
||
" <td>Alaa</td>\n",
|
||
" <td>Abdelnaby</td>\n",
|
||
" <td>Alaa Abdelnaby</td>\n",
|
||
" <td>Abdelnaby, Alaa</td>\n",
|
||
" <td>A. Abdelnaby</td>\n",
|
||
" <td>alaa-abdelnaby</td>\n",
|
||
" <td>1968-06-24</td>\n",
|
||
" <td>Duke</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_alaa_abdelnaby</td>\n",
|
||
" <td>1990.0</td>\n",
|
||
" <td>1994.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1990</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>25</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>76002</td>\n",
|
||
" <td>Zaid</td>\n",
|
||
" <td>Abdul-Aziz</td>\n",
|
||
" <td>Zaid Abdul-Aziz</td>\n",
|
||
" <td>Abdul-Aziz, Zaid</td>\n",
|
||
" <td>Z. Abdul-Aziz</td>\n",
|
||
" <td>zaid-abdul-aziz</td>\n",
|
||
" <td>1946-04-07</td>\n",
|
||
" <td>Iowa State</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_zaid_abdul-aziz</td>\n",
|
||
" <td>1968.0</td>\n",
|
||
" <td>1977.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1968</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>76003</td>\n",
|
||
" <td>Kareem</td>\n",
|
||
" <td>Abdul-Jabbar</td>\n",
|
||
" <td>Kareem Abdul-Jabbar</td>\n",
|
||
" <td>Abdul-Jabbar, Kareem</td>\n",
|
||
" <td>K. Abdul-Jabbar</td>\n",
|
||
" <td>kareem-abdul-jabbar</td>\n",
|
||
" <td>1947-04-16</td>\n",
|
||
" <td>UCLA</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_kareem_abdul-jabbar</td>\n",
|
||
" <td>1969.0</td>\n",
|
||
" <td>1988.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1969</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>Y</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>949</td>\n",
|
||
" <td>Shareef</td>\n",
|
||
" <td>Abdur-Rahim</td>\n",
|
||
" <td>Shareef Abdur-Rahim</td>\n",
|
||
" <td>Abdur-Rahim, Shareef</td>\n",
|
||
" <td>S. Abdur-Rahim</td>\n",
|
||
" <td>shareef-abdur-rahim</td>\n",
|
||
" <td>1976-12-11</td>\n",
|
||
" <td>California</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>shareef_abdur-rahim</td>\n",
|
||
" <td>1996.0</td>\n",
|
||
" <td>2007.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1996</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>76006</td>\n",
|
||
" <td>Forest</td>\n",
|
||
" <td>Able</td>\n",
|
||
" <td>Forest Able</td>\n",
|
||
" <td>Able, Forest</td>\n",
|
||
" <td>F. Able</td>\n",
|
||
" <td>forest-able</td>\n",
|
||
" <td>1932-07-27</td>\n",
|
||
" <td>Western Kentucky</td>\n",
|
||
" <td>USA</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>HISTADD_frosty_able</td>\n",
|
||
" <td>1956.0</td>\n",
|
||
" <td>1956.0</td>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>1956</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>N</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 33 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" person_id first_name last_name display_first_last \\\n",
|
||
"0 76001 Alaa Abdelnaby Alaa Abdelnaby \n",
|
||
"1 76002 Zaid Abdul-Aziz Zaid Abdul-Aziz \n",
|
||
"2 76003 Kareem Abdul-Jabbar Kareem Abdul-Jabbar \n",
|
||
"3 949 Shareef Abdur-Rahim Shareef Abdur-Rahim \n",
|
||
"4 76006 Forest Able Forest Able \n",
|
||
"\n",
|
||
" display_last_comma_first display_fi_last player_slug birthdate \\\n",
|
||
"0 Abdelnaby, Alaa A. Abdelnaby alaa-abdelnaby 1968-06-24 \n",
|
||
"1 Abdul-Aziz, Zaid Z. Abdul-Aziz zaid-abdul-aziz 1946-04-07 \n",
|
||
"2 Abdul-Jabbar, Kareem K. Abdul-Jabbar kareem-abdul-jabbar 1947-04-16 \n",
|
||
"3 Abdur-Rahim, Shareef S. Abdur-Rahim shareef-abdur-rahim 1976-12-11 \n",
|
||
"4 Able, Forest F. Able forest-able 1932-07-27 \n",
|
||
"\n",
|
||
" school country ... playercode from_year \\\n",
|
||
"0 Duke USA ... HISTADD_alaa_abdelnaby 1990.0 \n",
|
||
"1 Iowa State USA ... HISTADD_zaid_abdul-aziz 1968.0 \n",
|
||
"2 UCLA USA ... HISTADD_kareem_abdul-jabbar 1969.0 \n",
|
||
"3 California USA ... shareef_abdur-rahim 1996.0 \n",
|
||
"4 Western Kentucky USA ... HISTADD_frosty_able 1956.0 \n",
|
||
"\n",
|
||
" to_year dleague_flag nba_flag games_played_flag draft_year draft_round \\\n",
|
||
"0 1994.0 N Y Y 1990 1 \n",
|
||
"1 1977.0 N Y Y 1968 1 \n",
|
||
"2 1988.0 N Y Y 1969 1 \n",
|
||
"3 2007.0 N Y Y 1996 1 \n",
|
||
"4 1956.0 N Y Y 1956 None \n",
|
||
"\n",
|
||
" draft_number greatest_75_flag \n",
|
||
"0 25 N \n",
|
||
"1 5 N \n",
|
||
"2 1 Y \n",
|
||
"3 3 N \n",
|
||
"4 None N \n",
|
||
"\n",
|
||
"[5 rows x 33 columns]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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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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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>id</th>\n",
|
||
" <th>full_name</th>\n",
|
||
" <th>first_name</th>\n",
|
||
" <th>last_name</th>\n",
|
||
" <th>is_active</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>76001</td>\n",
|
||
" <td>Alaa Abdelnaby</td>\n",
|
||
" <td>Alaa</td>\n",
|
||
" <td>Abdelnaby</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>76002</td>\n",
|
||
" <td>Zaid Abdul-Aziz</td>\n",
|
||
" <td>Zaid</td>\n",
|
||
" <td>Abdul-Aziz</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>76003</td>\n",
|
||
" <td>Kareem Abdul-Jabbar</td>\n",
|
||
" <td>Kareem</td>\n",
|
||
" <td>Abdul-Jabbar</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>51</td>\n",
|
||
" <td>Mahmoud Abdul-Rauf</td>\n",
|
||
" <td>Mahmoud</td>\n",
|
||
" <td>Abdul-Rauf</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1505</td>\n",
|
||
" <td>Tariq Abdul-Wahad</td>\n",
|
||
" <td>Tariq</td>\n",
|
||
" <td>Abdul-Wahad</td>\n",
|
||
" <td>0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" id full_name first_name last_name is_active\n",
|
||
"0 76001 Alaa Abdelnaby Alaa Abdelnaby 0\n",
|
||
"1 76002 Zaid Abdul-Aziz Zaid Abdul-Aziz 0\n",
|
||
"2 76003 Kareem Abdul-Jabbar Kareem Abdul-Jabbar 0\n",
|
||
"3 51 Mahmoud Abdul-Rauf Mahmoud Abdul-Rauf 0\n",
|
||
"4 1505 Tariq Abdul-Wahad Tariq Abdul-Wahad 0"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"display(common_player_info.head())\n",
|
||
"display(player.head())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 101,
|
||
"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>person_id</th>\n",
|
||
" <th>is_active</th>\n",
|
||
" <th>from_year</th>\n",
|
||
" <th>to_year</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>76001</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1990.0</td>\n",
|
||
" <td>1994.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>76002</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1968.0</td>\n",
|
||
" <td>1977.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>76003</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1969.0</td>\n",
|
||
" <td>1988.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>949</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1996.0</td>\n",
|
||
" <td>2007.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>76006</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>1956.0</td>\n",
|
||
" <td>1956.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" person_id is_active from_year to_year\n",
|
||
"0 76001 0 1990.0 1994.0\n",
|
||
"1 76002 0 1968.0 1977.0\n",
|
||
"2 76003 0 1969.0 1988.0\n",
|
||
"3 949 0 1996.0 2007.0\n",
|
||
"4 76006 0 1956.0 1956.0"
|
||
]
|
||
},
|
||
"execution_count": 101,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"player.rename(columns={'id': 'person_id'}, inplace=True)\n",
|
||
"merged = pd.merge(player[['person_id', 'is_active']], common_player_info[['person_id', 'from_year', 'to_year']], on='person_id', how='inner')\n",
|
||
"merged.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 105,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"5.121428571428571"
|
||
]
|
||
},
|
||
"execution_count": 105,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"merged = merged[merged['is_active'] == 1]\n",
|
||
"merged['career_len'] = merged['to_year'] - merged['from_year']\n",
|
||
"merged['career_len'].mean()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Задание 7"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Каково соотношение позиций, на которых играют игроки? Используйте таблицу common_player_info."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 111,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Guard 1416\n",
|
||
"Forward 1306\n",
|
||
"Center 497\n",
|
||
"Guard-Forward 134\n",
|
||
"Forward-Center 112\n",
|
||
"Center-Forward 68\n",
|
||
"Forward-Guard 54\n",
|
||
" 45\n",
|
||
"Name: position, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"position_counts = common_player_info['position'].value_counts()\n",
|
||
"display(position_counts)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 113,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"position_counts = position_counts[position_counts.index != '']\n",
|
||
"\n",
|
||
"plt.figure(figsize=(10,6))\n",
|
||
"position_counts.plot(kind='bar')\n",
|
||
"plt.xlabel(\"Позиция\")\n",
|
||
"plt.ylabel(\"Количество игроков\")\n",
|
||
"plt.show()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"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": 2
|
||
}
|