{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analiza zależności ilości kibiców w baseball mlb" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Unnamed: 0 attendance away_team away_team_errors \\\n", "0 0 40030.0 New York Mets 1 \n", "1 1 21621.0 Philadelphia Phillies 0 \n", "2 2 12622.0 Minnesota Twins 0 \n", "3 3 18531.0 Washington Nationals 0 \n", "4 4 18572.0 Colorado Rockies 1 \n", "... ... ... ... ... \n", "2458 2458 31042.0 Toronto Blue Jays 2 \n", "2459 2459 39500.0 St. Louis Cardinals 0 \n", "2460 2460 20098.0 San Francisco Giants 0 \n", "2461 2461 17883.0 Detroit Tigers 0 \n", "2462 2462 10298.0 Boston Red Sox 1 \n", "\n", " away_team_hits away_team_runs date field_type game_type \\\n", "0 7 3 2016-04-03 on grass Night Game \n", "1 5 2 2016-04-06 on grass Night Game \n", "2 5 2 2016-04-06 on grass Night Game \n", "3 8 3 2016-04-06 on grass Night Game \n", "4 8 4 2016-04-06 on grass Day Game \n", "... ... ... ... ... ... \n", "2458 7 5 2016-04-03 on turf Day Game \n", "2459 5 1 2016-04-03 on grass Day Game \n", "2460 6 3 2016-04-06 on grass Day Game \n", "2461 13 7 2016-04-06 on grass Day Game \n", "2462 10 6 2016-04-06 on grass Night Game \n", "\n", " home_team ... temperature wind_speed \\\n", "0 Kansas City Royals ... 74.0 14.0 \n", "1 Cincinnati Reds ... 55.0 24.0 \n", "2 Baltimore Orioles ... 48.0 7.0 \n", "3 Atlanta Braves ... 65.0 10.0 \n", "4 Arizona Diamondbacks ... 77.0 0.0 \n", "... ... ... ... ... \n", "2458 Tampa Bay Rays ... 72.0 0.0 \n", "2459 Pittsburgh Pirates ... 39.0 14.0 \n", "2460 Milwaukee Brewers ... 66.0 0.0 \n", "2461 Miami Marlins ... 71.0 0.0 \n", "2462 Cleveland Indians ... 60.0 7.0 \n", "\n", " wind_direction sky total_runs game_hours_dec \\\n", "0 from Right to Left Sunny 7 3.216667 \n", "1 from Right to Left Overcast 5 2.383333 \n", "2 out to Leftfield Unknown 6 3.183333 \n", "3 from Right to Left Cloudy 4 2.883333 \n", "4 in unknown direction In Dome 7 2.650000 \n", "... ... ... ... ... \n", "2458 in unknown direction In Dome 8 2.850000 \n", "2459 out to Leftfield Unknown 5 3.033333 \n", "2460 in unknown direction In Dome 7 3.316667 \n", "2461 in unknown direction In Dome 10 3.366667 \n", "2462 out to Leftfield Unknown 13 3.483333 \n", "\n", " season home_team_win home_team_loss home_team_outcome \n", "0 regular season 1 0 Win \n", "1 regular season 1 0 Win \n", "2 regular season 1 0 Win \n", "3 regular season 0 1 Loss \n", "4 regular season 0 1 Loss \n", "... ... ... ... ... \n", "2458 regular season 0 1 Loss \n", "2459 regular season 1 0 Win \n", "2460 regular season 1 0 Win \n", "2461 regular season 0 1 Loss \n", "2462 regular season 1 0 Win \n", "\n", "[2463 rows x 26 columns]" ], "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsdatefield_typegame_typehome_team...temperaturewind_speedwind_directionskytotal_runsgame_hours_decseasonhome_team_winhome_team_losshome_team_outcome
0040030.0New York Mets1732016-04-03on grassNight GameKansas City Royals...74.014.0from Right to LeftSunny73.216667regular season10Win
1121621.0Philadelphia Phillies0522016-04-06on grassNight GameCincinnati Reds...55.024.0from Right to LeftOvercast52.383333regular season10Win
2212622.0Minnesota Twins0522016-04-06on grassNight GameBaltimore Orioles...48.07.0out to LeftfieldUnknown63.183333regular season10Win
3318531.0Washington Nationals0832016-04-06on grassNight GameAtlanta Braves...65.010.0from Right to LeftCloudy42.883333regular season01Loss
4418572.0Colorado Rockies1842016-04-06on grassDay GameArizona Diamondbacks...77.00.0in unknown directionIn Dome72.650000regular season01Loss
..................................................................
2458245831042.0Toronto Blue Jays2752016-04-03on turfDay GameTampa Bay Rays...72.00.0in unknown directionIn Dome82.850000regular season01Loss
2459245939500.0St. Louis Cardinals0512016-04-03on grassDay GamePittsburgh Pirates...39.014.0out to LeftfieldUnknown53.033333regular season10Win
2460246020098.0San Francisco Giants0632016-04-06on grassDay GameMilwaukee Brewers...66.00.0in unknown directionIn Dome73.316667regular season10Win
2461246117883.0Detroit Tigers01372016-04-06on grassDay GameMiami Marlins...71.00.0in unknown directionIn Dome103.366667regular season01Loss
2462246210298.0Boston Red Sox11062016-04-06on grassNight GameCleveland Indians...60.07.0out to LeftfieldUnknown133.483333regular season10Win
\n

2463 rows × 26 columns

\n
" }, "metadata": {}, "execution_count": 5 } ], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"baseball_reference_2016_clean.csv\")\n", "\n", "data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['Unnamed: 0', 'attendance', 'away_team', 'away_team_errors',\n", " 'away_team_hits', 'away_team_runs', 'date', 'field_type', 'game_type',\n", " 'home_team', 'home_team_errors', 'home_team_hits', 'home_team_runs',\n", " 'start_time', 'venue', 'day_of_week', 'temperature', 'wind_speed',\n", " 'wind_direction', 'sky', 'total_runs', 'game_hours_dec', 'season',\n", " 'home_team_win', 'home_team_loss', 'home_team_outcome'],\n", " dtype='object')" ] }, "metadata": {}, "execution_count": 6 } ], "source": [ "data.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pogoda\n", "\n", "![image](sky.jpg)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['Sunny', 'Overcast', 'Unknown', 'Cloudy', 'In Dome', 'Drizzle',\n", " 'Rain', 'Night'], dtype=object)" ] }, "metadata": {}, "execution_count": 7 } ], "source": [ "data['sky'].unique()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "sunny = data[data['sky'] == 'Sunny']\n", "overcast = data[data['sky'] == 'Overcast']\n", "cloudy = data[data['sky'] == 'Cloudy']\n", "in_dome = data[data['sky'] == 'In Dome']\n", "drizzle = data[data['sky'] == 'Drizzle']\n", "rain = data[data['sky'] == 'Rain']\n", "night = data[data['sky'] == 'Night']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Średnia ilość kibiców w zależności od pogody" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:29:42.935602\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import matplotlib.pyplot as plt\n", " \n", "left = [1, 2, 3, 4, 5, 6, 7]\n", "\n", "height = [sunny['attendance'].mean(), overcast['attendance'].mean(), cloudy['attendance'].mean(), \n", "in_dome['attendance'].mean(), drizzle['attendance'].mean(), rain['attendance'].mean(), night['attendance'].mean()]\n", "\n", "tick_label = ['sunny', 'overcast', 'cloudy', 'in dome', 'drizzle', 'rain', 'night']\n", "\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['blue', 'green', 'red'])\n", " \n", "plt.xlabel('Weather')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Weather')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mediana" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:29:48.241904\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import matplotlib.pyplot as plt\n", " \n", "left = [1, 2, 3, 4, 5, 6, 7]\n", "\n", "height = [sunny['attendance'].median(), overcast['attendance'].median(), cloudy['attendance'].median(), \n", "in_dome['attendance'].median(), drizzle['attendance'].median(), rain['attendance'].median(), night['attendance'].median()]\n", "\n", "tick_label = ['sunny', 'overcast', 'cloudy', 'in dome', 'drizzle', 'rain', 'night']\n", "\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['blue', 'green', 'red'])\n", " \n", "plt.xlabel('Weather')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Weather')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "W nocy prawdopodobnie najwięcej, gdyż większa grupa odbiorców ma dostęp do meczy online z całego świata. \n", "Pod kopułą może być najmniej widzów, gdyż takie stadiony mają mniejsze trybuny." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dzień tygodnia\n", "\n", "![image2](week.jpg)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['Sunday', 'Wednesday', 'Tuesday', 'Monday', 'Thursday', 'Saturday',\n", " 'Friday'], dtype=object)" ] }, "metadata": {}, "execution_count": 11 } ], "source": [ "data['day_of_week'].unique()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "monday = data[data['day_of_week'] == 'Monday']\n", "tuesday = data[data['day_of_week'] == 'Tuesday']\n", "wednesday = data[data['day_of_week'] == 'Wednesday']\n", "thursday = data[data['day_of_week'] == 'Thursday']\n", "friday = data[data['day_of_week'] == 'Friday']\n", "saturday = data[data['day_of_week'] == 'Saturday']\n", "sunday = data[data['day_of_week'] == 'Sunday']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Średnia ilość kibiców w danym dniu" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:29:53.431566\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import matplotlib.pyplot as plt\n", " \n", "left = [1, 2, 3, 4, 5, 6, 7]\n", "\n", "height = [monday['attendance'].mean(), tuesday['attendance'].mean(), wednesday['attendance'].mean(), \n", "thursday['attendance'].mean(), friday['attendance'].mean(), saturday['attendance'].mean(), sunday['attendance'].mean()]\n", "\n", "tick_label = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n", "\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['blue', 'green', 'red'])\n", " \n", "plt.xlabel('Day')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Day')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mediana" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:29:59.215279\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import matplotlib.pyplot as plt\n", " \n", "left = [1, 2, 3, 4, 5, 6, 7]\n", "\n", "height = [monday['attendance'].median(), tuesday['attendance'].median(), wednesday['attendance'].median(), \n", "thursday['attendance'].median(), friday['attendance'].median(), saturday['attendance'].median(), sunday['attendance'].median()]\n", "\n", "tick_label = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday']\n", "\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['blue', 'green', 'red'])\n", " \n", "plt.xlabel('Day')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Day')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Najwięcej kibiców jest w weekendy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Zwycięstwo / porażka gospodarzy\n", "![image3](win.jpg)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['Win', 'Loss'], dtype=object)" ] }, "metadata": {}, "execution_count": 15 } ], "source": [ "data['home_team_outcome'].unique()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "win = data[data['home_team_outcome'] == 'Win']\n", "loss = data[data['home_team_outcome'] == 'Loss']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Średnia ilość kibiców przy wygraniu/przegraniu gospodarzy" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:30:04.616815\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "left = [1, 2]\n", "\n", "height = [win['attendance'].mean(), loss['attendance'].mean()]\n", "\n", "tick_label = ['win', 'loss']\n", "\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['blue', 'red'])\n", " \n", "plt.xlabel('Win')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Win')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Mediana" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:30:08.085830\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "left = [1, 2]\n", "\n", "height = [win['attendance'].median(), loss['attendance'].median()]\n", "\n", "tick_label = ['win', 'loss']\n", "\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.8, color = ['blue', 'red'])\n", " \n", "plt.xlabel('Win')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Win')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nie ma to wpływu, raczej nie jest tak, że widać przegraną przed końcem i przez to kibice wychodzą. A nawet jeśli to działa to w miarę równomiernie w obie strony." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Zwycięstwa w kolejnych meczach\n", "\n", "![image4](win-streak.png)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array(['New York Mets', 'Philadelphia Phillies', 'Minnesota Twins',\n", " 'Washington Nationals', 'Colorado Rockies', 'Seattle Mariners',\n", " 'Toronto Blue Jays', 'Los Angeles Dodgers', 'St. Louis Cardinals',\n", " 'Chicago White Sox', 'Houston Astros', 'San Francisco Giants',\n", " 'Detroit Tigers', 'Texas Rangers', 'San Diego Padres',\n", " 'Los Angeles Angels of Anaheim', 'Miami Marlins',\n", " 'Kansas City Royals', 'Pittsburgh Pirates', 'Cincinnati Reds',\n", " 'Atlanta Braves', 'New York Yankees', 'Chicago Cubs',\n", " 'Arizona Diamondbacks', 'Milwaukee Brewers', 'Baltimore Orioles',\n", " 'Cleveland Indians', 'Oakland Athletics', 'Boston Red Sox',\n", " 'Tampa Bay Rays'], dtype=object)" ] }, "metadata": {}, "execution_count": 20 } ], "source": [ "data['away_team'].unique()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "mets = data[data['away_team'] == 'New York Mets']" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Attendance - Win/Lose')" ] }, "metadata": {}, "execution_count": 27 }, { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:31:05.283000\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "left = [i for i in range(len(mets))]\n", "\n", "height = [i for i in mets['attendance']]\n", "\n", "tick_label = ['l' if [i for i in mets['home_team_outcome']][i] == 'Win' else 'w' for i in range(len(mets))]\n", "\n", "plt.figure(figsize=(24, 3)) # width:20, height:3\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.5, color = ['red' if [i for i in mets['home_team_outcome']][i] == 'Win' else 'blue' for i in range(len(mets))])\n", " \n", "plt.xlabel('Win (w) or Lose (l)')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Win/Lose')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "philadelphia = data[data['away_team'] == 'Philadelphia Phillies']" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0.5, 1.0, 'Attendance - Win/Lose')" ] }, "metadata": {}, "execution_count": 29 }, { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n \n \n \n \n 2022-10-18T17:32:31.897068\n image/svg+xml\n \n \n Matplotlib v3.5.1, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "left = [i for i in range(len(philadelphia))]\n", "\n", "height = [i for i in philadelphia['attendance']]\n", "\n", "tick_label = ['l' if [i for i in philadelphia['home_team_outcome']][i] == 'Win' else 'w' for i in range(len(philadelphia))]\n", "\n", "plt.figure(figsize=(24, 3)) # width:20, height:3\n", "plt.bar(left, height, tick_label = tick_label,\n", " width = 0.5, color = ['red' if [i for i in philadelphia['home_team_outcome']][i] == 'Win' else 'blue' for i in range(len(philadelphia))])\n", " \n", "plt.xlabel('Win (w) or Lose (l)')\n", "plt.ylabel('Attendance')\n", "plt.title('Attendance - Win/Lose')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Czasami można wywnioskować, że po wygranym meczu przychodzi więcej kibiców na następny, ale nie zawsze, to raczej nie jest częsta zasada." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.10-final" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "3dafdb3de6203a1118d6c063d9a807622a512a5be3d463a10b75ce9c56521739" } } }, "nbformat": 4, "nbformat_minor": 2 }