{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Análisis de datos con Pandas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Lista de archivos a utilizar\n", "#https://www.footballdb.com/stats/penalties-player.html\n", "#Num = Number of Penalties\n", "#Yds = Yards Penalized\n", "#FS = False Start\n", "#HLD = Holding\n", "#OFF = Offsides\n", "#IC = Illegal Contact\n", "#PI = Pass Interference\n", "#RP = Roughing the Passer\n", "#PF = Personal Foul\n", "#UR = Unnecessary Roughness\n", "#UC = Unsportsmanlike Conduct\n", "\n", "archivos = ['penalties_2015.xls',\n", " 'penalties_2016.xls',\n", " 'penalties_2017.xls',\n", " 'penalties_2018.xls',\n", " 'penalties_2019.xls']\n", "folder = '../datos/nfl/penalties/'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#Para almacenar los datos se utilizará un\n", "#diccionario d[AÑO] = DATAFRAME\n", "\n", "tablas = {}\n", "years = ['2015', '2016', '2017', '2018', '2019']\n", "\n", "for arch, year in zip(archivos, years):\n", " #Ruta del archivo\n", " ruta = folder + arch\n", " \n", " #Abre archivo\n", " dataframe = pd.read_excel(ruta)\n", " \n", " #Agrega columna Year al dataframe\n", " dataframe['Year'] = year\n", " \n", " #Agrega al diccionario\n", " tablas[year] = dataframe" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PlayerPosTeamNumYdsFSHLDOFFICPIRPPFURUCOtherYear
20150Brandon Browner (DB), NODBNO2020208113003042015
1Ndamukong Suh (DT), MiaDTMia1467011000000122015
2Dre Kirkpatrick (DB), CinDBCin1311804023000042015
3Greg Robinson (OT), StlOTStl1311448000000012015
4Jerry Hughes (DE), BufDEBuf1310900100103082015
......................................................
201945Jadeveon Clowney (DE), SeaDESea84900100100062019
46Jimmy Moreland (DB), WasDBWas84803011000032019
47Michael Bennett (DE), DalDEDal83600500000032019
48Richard Sherman (DB), SFDBSF710002004001002019
49Darryl Roberts (DB), NYJDBNYJ78902003000022019
\n", "

250 rows × 16 columns

\n", "
" ], "text/plain": [ " Player Pos Team Num Yds FS HLD OFF IC PI \\\n", "2015 0 Brandon Browner (DB), NO DB NO 20 202 0 8 1 1 3 \n", " 1 Ndamukong Suh (DT), Mia DT Mia 14 67 0 1 1 0 0 \n", " 2 Dre Kirkpatrick (DB), Cin DB Cin 13 118 0 4 0 2 3 \n", " 3 Greg Robinson (OT), Stl OT Stl 13 114 4 8 0 0 0 \n", " 4 Jerry Hughes (DE), Buf DE Buf 13 109 0 0 1 0 0 \n", "... ... .. ... ... ... .. ... ... .. .. \n", "2019 45 Jadeveon Clowney (DE), Sea DE Sea 8 49 0 0 1 0 0 \n", " 46 Jimmy Moreland (DB), Was DB Was 8 48 0 3 0 1 1 \n", " 47 Michael Bennett (DE), Dal DE Dal 8 36 0 0 5 0 0 \n", " 48 Richard Sherman (DB), SF DB SF 7 100 0 2 0 0 4 \n", " 49 Darryl Roberts (DB), NYJ DB NYJ 7 89 0 2 0 0 3 \n", "\n", " RP PF UR UC Other Year \n", "2015 0 0 0 3 0 4 2015 \n", " 1 0 0 0 0 12 2015 \n", " 2 0 0 0 0 4 2015 \n", " 3 0 0 0 0 1 2015 \n", " 4 1 0 3 0 8 2015 \n", "... .. .. .. .. ... ... \n", "2019 45 1 0 0 0 6 2019 \n", " 46 0 0 0 0 3 2019 \n", " 47 0 0 0 0 3 2019 \n", " 48 0 0 1 0 0 2019 \n", " 49 0 0 0 0 2 2019 \n", "\n", "[250 rows x 16 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Concatenamos tablas\n", "df_penal = pd.concat(tablas)\n", "df_penal\n", "#MULTI-INDEX\n", "#df_penal.loc[ ('2015', 0) ]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PlayerPosTeamNumYdsFSHLDOFFICPIRPPFURUCOtherYear
20150Brandon BrownerDBNO2020208113003042015
1Ndamukong SuhDTMia1467011000000122015
2Dre KirkpatrickDBCin1311804023000042015
3Greg RobinsonOTStl1311448000000012015
4Jerry HughesDEBuf1310900100103082015
......................................................
201945Jadeveon ClowneyDESea84900100100062019
46Jimmy MorelandDBWas84803011000032019
47Michael BennettDEDal83600500000032019
48Richard ShermanDBSF710002004001002019
49Darryl RobertsDBNYJ78902003000022019
\n", "

250 rows × 16 columns

\n", "
" ], "text/plain": [ " Player Pos Team Num Yds FS HLD OFF IC PI RP PF \\\n", "2015 0 Brandon Browner DB NO 20 202 0 8 1 1 3 0 0 \n", " 1 Ndamukong Suh DT Mia 14 67 0 1 1 0 0 0 0 \n", " 2 Dre Kirkpatrick DB Cin 13 118 0 4 0 2 3 0 0 \n", " 3 Greg Robinson OT Stl 13 114 4 8 0 0 0 0 0 \n", " 4 Jerry Hughes DE Buf 13 109 0 0 1 0 0 1 0 \n", "... ... .. ... ... ... .. ... ... .. .. .. .. \n", "2019 45 Jadeveon Clowney DE Sea 8 49 0 0 1 0 0 1 0 \n", " 46 Jimmy Moreland DB Was 8 48 0 3 0 1 1 0 0 \n", " 47 Michael Bennett DE Dal 8 36 0 0 5 0 0 0 0 \n", " 48 Richard Sherman DB SF 7 100 0 2 0 0 4 0 0 \n", " 49 Darryl Roberts DB NYJ 7 89 0 2 0 0 3 0 0 \n", "\n", " UR UC Other Year \n", "2015 0 3 0 4 2015 \n", " 1 0 0 12 2015 \n", " 2 0 0 4 2015 \n", " 3 0 0 1 2015 \n", " 4 3 0 8 2015 \n", "... .. .. ... ... \n", "2019 45 0 0 6 2019 \n", " 46 0 0 3 2019 \n", " 47 0 0 3 2019 \n", " 48 1 0 0 2019 \n", " 49 0 0 2 2019 \n", "\n", "[250 rows x 16 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Ya que la columna Player\n", "#contiene información que también\n", "#se encuentra en Pos y Team\n", "#resulta conveniente limpiarla\n", "def limpia_player(obs):\n", " '''\n", " obs es una observación\n", " de la columna Player\n", " '''\n", " \n", " #Hace split del string\n", " spl = obs.split(' ')\n", " nombre = spl[0]\n", " apellido = spl[1]\n", " \n", " return nombre + ' ' + apellido\n", " \n", "df_penal['Player'] = df_penal['Player'].apply(limpia_player)\n", "df_penal" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PlayerPosTeamNumYdsFSHLDOFFICPIRPPFURUCOtherYear
20150Brandon BrownerDBNew Orleans Saints2020208113003042015
1Ndamukong SuhDTMiami Dolphins1467011000000122015
2Dre KirkpatrickDBCincinnati Bengals1311804023000042015
3Greg RobinsonOTSaint Louis Rams1311448000000012015
4Jerry HughesDEBuffalo Bills1310900100103082015
......................................................
201945Jadeveon ClowneyDESeattle Seahawks84900100100062019
46Jimmy MorelandDBWashington Redskins84803011000032019
47Michael BennettDEDallas Cowboys83600500000032019
48Richard ShermanDBSan Francisco 49ers710002004001002019
49Darryl RobertsDBNew York Jets78902003000022019
\n", "

250 rows × 16 columns

\n", "
" ], "text/plain": [ " Player Pos Team Num Yds FS HLD OFF \\\n", "2015 0 Brandon Browner DB New Orleans Saints 20 202 0 8 1 \n", " 1 Ndamukong Suh DT Miami Dolphins 14 67 0 1 1 \n", " 2 Dre Kirkpatrick DB Cincinnati Bengals 13 118 0 4 0 \n", " 3 Greg Robinson OT Saint Louis Rams 13 114 4 8 0 \n", " 4 Jerry Hughes DE Buffalo Bills 13 109 0 0 1 \n", "... ... .. ... ... ... .. ... ... \n", "2019 45 Jadeveon Clowney DE Seattle Seahawks 8 49 0 0 1 \n", " 46 Jimmy Moreland DB Washington Redskins 8 48 0 3 0 \n", " 47 Michael Bennett DE Dallas Cowboys 8 36 0 0 5 \n", " 48 Richard Sherman DB San Francisco 49ers 7 100 0 2 0 \n", " 49 Darryl Roberts DB New York Jets 7 89 0 2 0 \n", "\n", " IC PI RP PF UR UC Other Year \n", "2015 0 1 3 0 0 3 0 4 2015 \n", " 1 0 0 0 0 0 0 12 2015 \n", " 2 2 3 0 0 0 0 4 2015 \n", " 3 0 0 0 0 0 0 1 2015 \n", " 4 0 0 1 0 3 0 8 2015 \n", "... .. .. .. .. .. .. ... ... \n", "2019 45 0 0 1 0 0 0 6 2019 \n", " 46 1 1 0 0 0 0 3 2019 \n", " 47 0 0 0 0 0 0 3 2019 \n", " 48 0 4 0 0 1 0 0 2019 \n", " 49 0 3 0 0 0 0 2 2019 \n", "\n", "[250 rows x 16 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Cambiar columna Team\n", "#por el nombre del equipo\n", "codigos = pd.read_csv('../datos/nfl/team_codes.csv')\n", "def cambia_team(team):\n", " nombre = codigos[codigos['Code'] == team.upper()]['Name'].values\n", " if len(nombre) != 0:\n", " nombre = nombre[0].strip()\n", " else:\n", " nombre = team\n", " return nombre\n", "\n", "df_penal['Team'] = df_penal['Team'].apply(cambia_team)\n", "df_penal" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Player\n", "Greg Robinson 41\n", "Taylor Lewan 38\n", "Germain Ifedi 38\n", "Donovan Smith 34\n", "Jadeveon Clowney 34\n", " ..\n", "Wes Schweitzer 7\n", "Johnny Holton 7\n", "Prince Amukamara 7\n", "Morris Claiborne 7\n", "Ahkello Witherspoon 7\n", "Name: Num, Length: 181, dtype: int64" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Que jugador ha cometido\n", "#el mayor número de faltas\n", "#Se agrupan datos por la columna Player\n", "gr_jugador = df_penal.groupby('Player')\n", "df_jugador = gr_jugador.sum()['Num']\n", "\n", "#Para un objeto Series\n", "#No es necesario especificar\n", "#La columna por la que se ordenan\n", "#sus valores\n", "df_jugador = df_jugador.sort_values(ascending = False)\n", "df_jugador" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.barh(y = df_jugador.index[10::-1], width = df_jugador[10::-1])\n", "plt.xlabel('Faltas acumuladas 2015-2019')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Player Team \n", "Germain Ifedi Seattle Seahawks 38\n", "Taylor Lewan Tennessee Titans 38\n", "Donovan Smith Tampa Bay Buccaneers 34\n", "Morgan Moses Washington Redskins 32\n", "Garett Bolles Denver Broncos 32\n", " ..\n", "T.J. Lang Detroit Lions 7\n", "Russell Wilson Seattle Seahawks 7\n", "Stephon Gilmore New England Patriots 7\n", "Eli Apple New York Giants 7\n", "Johnny Holton Oakland Raiders 7\n", "Name: Num, Length: 198, dtype: int64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Faltas por jugador y equipo\n", "gr_jug_eq = df_penal.groupby(['Player', 'Team'])\n", "ser_jug_eq = gr_jug_eq.sum()['Num'].sort_values(ascending = False)\n", "ser_jug_eq" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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sVjM98CnAjjXzrgY2johNgPuAw9tcl5mZNdAwwCPieuCpmnlXRcSr+eafSV9sbGZmw6gdY+BfAC5vw3bMzGwAWgpwSd8GXgXO6GediZK6JXX39PS00pyZmVUMOsAl7QvsAnw2IqLeehExOSK6IqKro6NjsM2ZmVmNQf07WUk7At8EtomIl9pbkpmZNaOZtxGeCfwJ2EDSLEn7AT8FlgOuljRd0olDXKeZmdVo2AOPiL36mP2rIajFzMwGwJ/ENDMrlAPczKxQDnAzs0I5wM3MCuUANzMrlAPczKxQDnAzs0IN6pOYZvbm1Dnp0pEu4U1r5jE7t32b7oGbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFauYbeU6RNEfSjMq8lSRdLen+/HvFoS3TzMxqNdMDnwLsWDNvEnBNRKwHXJNvm5nZMGoY4BFxPfBUzezdgdPy9GnAHm2uy8zMGhjsGPhbImI2QP69Sr0VJU2U1C2pu6enZ5DNmZlZrSG/iBkRkyOiKyK6Ojo6hro5M7NFxmAD/ElJqwHk33PaV5KZmTVjsAF+EbBvnt4X+H17yjEzs2Y18zbCM4E/ARtImiVpP+AY4MOS7gc+nG+bmdkwaviFDhGxV51FH2pzLWZmNgD+JKaZWaEc4GZmhXKAm5kVygFuZlYoB7iZWaEc4GZmhXKAm5kVygFuZlYoB7iZWaEc4GZmhXKAm5kVygFuZlYoB7iZWaEc4GZmhXKAm5kVqqUAl/RVSXdJmiHpTElLtaswMzPr36ADXNIawMFAV0RsDCwG7NmuwszMrH+tDqGMBpaWNBpYBni89ZLMzKwZgw7wiHgM+DHwCDAbeDYirqpdT9JESd2Sunt6egZfqZmZzaeVIZQVgd2BdYDVgWUl7V27XkRMjoiuiOjq6OgYfKVmZjafVoZQdgAeioieiPgncAGwZXvKMjOzRloJ8EeA90laRpJI31J/d3vKMjOzRloZA78JOA+4Fbgzb2tym+oyM7MGRrdy54g4EjiyTbWYmdkA+JOYZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRXKAW5mVigHuJlZoVoKcEljJZ0n6R5Jd0t6f7sKMzOz/rX0jTzA/weuiIjxkpYAlmlDTWZm1oRBB7ik5YGtgQkAEfEK8Ep7yjIzs0ZaGUJZF+gBTpV0m6STJS1bu5KkiZK6JXX39PS00JyZmVW1EuCjgc2AX0TEu4EXgUm1K0XE5Ijoioiujo6OFpozM7OqVgJ8FjArIm7Kt88jBbqZmQ2DQQd4RDwBPCppgzzrQ8Bf21KVmZk11Oq7UA4CzsjvQPkb8PnWSzIzs2a0FOARMR3oalMtZmY2AP4kpplZoRzgZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRXKAW5mVigHuJlZoRzgZmaFcoCbmRWq5QCXtFj+UuNL2lGQmZk1px098EOAu9uwHTMzG4CWAlzSmsDOwMntKcfMzJrVag/8J8BhwOv1VpA0UVK3pO6enp4WmzMzs16DDnBJuwBzIuKW/taLiMkR0RURXR0dHYNtzszMarTSA/8AsJukmcBZwPaSTm9LVWZm1tCgAzwiDo+INSOiE9gTuDYi9m5bZWZm1i+/D9zMrFCj27GRiJgKTG3HtszMrDnugZuZFcoBbmZWKAe4mVmhHOBmZoVygJuZFcoBbmZWKAe4mVmhHOBmZoVygJuZFcoBbmZWKAe4mVmhHOBmZoVygJuZFcoBbmZWKAe4mVmhWvlOzLUkXSfpbkl3STqknYWZmVn/WvlCh1eBr0fErZKWA26RdHVE/LVNtZmZWT9a+U7M2RFxa55+HrgbWKNdhZmZWf/aMgYuqRN4N3BTH8smSuqW1N3T09OO5szMjDYEuKQxwPnAVyLiudrlETE5Iroioqujo6PV5szMLGspwCUtTgrvMyLigvaUZGZmzWjlXSgCfgXcHRHHta8kMzNrRis98A8AnwO2lzQ9/+zUprrMzKyBQb+NMCJuANTGWszMbAD8SUwzs0I5wM3MCuUANzMrlAPczKxQDnAzs0I5wM3MCuUANzMrlAPczKxQDnAzs0I5wM3MCuUANzMrlAPczKxQDnAzs0I5wM3MCuUANzMrlAPczKxQrX4n5o6S7pX0gKRJ7SrKzMwaa+U7MRcDfgZ8DNgI2EvSRu0qzMzM+tdKD3wL4IGI+FtEvAKcBezenrLMzKyRQX8nJrAG8Gjl9izgvbUrSZoITMw3X5B0bwttlmQcMHeki2iG/mukK1goFHO8wMcsW5SO2Vv7mtlKgPf1hcaxwIyIycDkFtopkqTuiOga6TqsOT5e5fExa20IZRawVuX2msDjrZVjZmbNaiXA/wKsJ2kdSUsAewIXtacsMzNrZNBDKBHxqqQvA1cCiwGnRMRdbausfIvcsFHhfLzKs8gfM0UsMGxtZmYF8CcxzcwK5QA3MytUMQEuaVVJZ0l6UNJfJV0maX1JnZJmtLmttm1T0raSLqkz/1lJ0yXdIekPklZpR5sLG0nflnRX3s/pkhb4vEDN+v8paYcG62wracs6yyZI+mkrNeftHCBpnybX7T2et0m6R9KPW23f5pH0Qj/Lbpd05nDWs7Bo5X3gw0aSgAuB0yJizzxvU+AtzP9hotJMi4hdACT9EPgScGR1BUmjI+LVkSiuHSS9H9gF2CwiXpY0Dliiv/tExBFNbHpb4AXgxpaLrF/HiQO8y7SI2EXS0sBtki6MiP8ZitoskfQOUkd0a0nLRsSLI13TcCqlB74d8M/qH1RETI+IadWVJC0m6VhJf8m9vS/m+WdL2qmy3hRJn6y3fs02OyVNk3Rr/tkyz99W0lRJ5+Ue1xn5hab3n3zdI+kG4BONdi7fbzng6Xz7KEmTJV0F/FrSUpJOlXRn7uFtl9e7TNImefo2SUfk6e9J2r9BjcfkM5k7hri3uBowNyJeBoiIuRHxeK7hiPzYz8j721vbFEnj8/RMSf+RH/s7JW0oqRM4APhq7tFv1UwhkvbK25ghzftcXLV3J2m8pCl5+ihJh+bpgyuP11n9tRMR/wtMJ31aGUlbSLoxH6MbJW2Q50+Q9DtJF0t6SNKXJX0tr/dnSSsNtO1F0GeA3wBXAbuNcC3DLyIW+h/gYOD4Oss6gRl5eiLwnTy9JNANrAN8nNR7h9T7exRYup/1q9tcBlgqT68HdOfpbYFnSR9gGgX8CfggsFTe/nqkT6ueA1zSR92995+e178HWD4vOwq4BVg63/46cGqe3hB4JLczidRrX570vvwr8zrXARv0U+NKwL3MexfS2CE8dmPyPt4H/BzYprJspcr0b4Bd8/QUYHyengkclKf/HTi58hgdWqfNCcBPa+atnh+3DtKZ57XAHnnZC5X1xgNTatsgfUhtyXqPV36sL8nTK+bjt2q+vTwwOk/vAJxfqfMB0ot3Rz5WB+RlxwNfaabtReGneoxq5t9H+pj5R4CLRrrO4f4ppQferI8A+0iaDtwErEwK0suB7SUtSfrviddH6iXVW79qceAkSXcC55L+82KvmyNiVkS8TgqpTlLAPhQR90d6hp3eT73TImLTiFgLOBX4UWXZRblGSKH7G4CIuAd4GFgfmAZsnZdfCoyRtAzQGRG9/3OmrxqfA/4BnCzpE8BL/dTYkoh4AXgP6cWyBzhb0oS8eDtJN+XHdnvgnXU2c0H+fQup/sHYHJgaET2RhqTOID12zboDOEPS3kC9Ia2tJN0BPEEK8yfy/BWAc5WuqxzP/Pt5XUQ8HxE9pAC/OM+/k3n72kzbixxJmwM9EfEwcA2wmaQVR7isYVVKgN9FCoFGROqtbZp/1omIqyLiH8BU4KPAp0n/ObHu+jXb/CrwJPAvQBfzj9++XJl+jXnXFAbz5vqLmD9QqmN5ff3fGUi97i5gK+B64Dbg30hBV7fGHGBbAOcDewBXDKLepkXEaxExNSKOBL4MfFLSUqQe+fiIeBdwEumsoi+9+1B9jAeq3mMI8x+vejXsTPr3ye8BbpHUVx3TImIT4F3AgUrXaQC+RwrqjYFda9qoHp/XK7dfZ96+NtP2omgvYENJM4EHSWc6nxzRioZZKQF+LbCkpH/rnSFpc0nb1Kx3JekPZ/G8zvqSls3LzgI+Twq7K5tYv9cKwOzcg/0c6VOn/bkHWEfS2/LtvZrcxw+SnoR9uR74bG+NwNrAvZH+je+jwKeAP5N65Ifm33VJGgOsEBGXAV8BNu1v/VZI2kBS9axmU9IZRG+Izc31jB/gpp8nDT006yZgG0njlP6X/V7AH/OyJyW9Q9Io0nBb7T6MAtaKiOuAw4CxpKGhPkXEfcAPgW/mWSsAj+XpCQOoecBtLyry4/KvwCYR0RkRnaR/Z93s39ubQhGv5BERkj4O/ETpm3/+QRob/UrNqieTTjtvzRfEekg9TEgXOX5NGpp4pYn1e/0cOF/Sv5LGlvu9yh0R/1D6F7qXSpoL3ABsXGf1rfLwjUinz/vXWe/nwIl5qOFVYELki4KksP5QRLwkaRppvLvfACcF3+9zL1iks4yhMgb4b0ljSbU/AEyMiGcknUQaKphJOpsYiIuB8yTtTjqLqt3nCZKqx/J9wOGkYyjgsoj4fV42CbiE9GI4gwUDcjHgdEkr5PseHxHPNKjvROBQSeuQhsZOk/Q1UmdkIAbT9pvRMpJmVW4fBzwWEY9V5l0PbCRptYiYPbzljQx/lN7MrFClDKGYmVkNB7iZWaEc4GZmhXKAm5kVygFuZlYoB7iZWaEc4GZmhfo/7K443IwaZ7MAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Faltas de un jugador en particular\n", "#por equipo\n", "jug = 'Greg Robinson'\n", "plt.bar(x = ser_jug_eq[jug].index, height = ser_jug_eq[jug])\n", "plt.title(f'Faltas del jugador {jug} por equipo')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Team\n", "Tampa Bay Buccaneers 127\n", "Seattle Seahawks 123\n", "Washington Redskins 122\n", "Houston Texans 102\n", "Denver Broncos 100\n", "Oakland Raiders 100\n", "Miami Dolphins 98\n", "Jacksonville Jaguars 96\n", "Philadelphia Eagles 88\n", "New Orleans Saints 84\n", "Kansas City Chiefs 83\n", "Cleveland Browns 78\n", "Dallas Cowboys 76\n", "Chicago Bears 73\n", "Atlanta Falcons 70\n", "Cincinnati Bengals 70\n", "San Francisco 49ers 70\n", "Pittsburgh Steelers 58\n", "Buffalo Bills 57\n", "Minnesota Vikings 55\n", "Green Bay Packers 55\n", "New York Giants 54\n", "Carolina Panthers 53\n", "New England Patriots 49\n", "New York Jets 48\n", "Tennessee Titans 46\n", "Arizona Cardinals 42\n", "Indianapolis Colts 41\n", "LA 40\n", "LAC 39\n", "Baltimore Ravens 34\n", "Detroit Lions 33\n", "San Diego Chargers 20\n", "Saint Louis Rams 13\n", "Name: Num, dtype: int64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Equipos con más faltas\n", "gr_equipo = df_penal.groupby('Team')\n", "ser_equipo = gr_equipo.sum()['Num'].sort_values(ascending = False)\n", "ser_equipo" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.barh(y = ser_equipo.index[10::-1], width = ser_equipo[10::-1])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PFURUCPF+UR+UC
Team
Denver Broncos010010
Buffalo Bills0909
Tampa Bay Buccaneers0808
Seattle Seahawks0707
Pittsburgh Steelers0505
Washington Redskins0404
Kansas City Chiefs0404
Jacksonville Jaguars0404
New York Jets0404
New Orleans Saints0404
Baltimore Ravens0404
San Francisco 49ers0303
Cincinnati Bengals0303
Tennessee Titans0303
Philadelphia Eagles0303
Oakland Raiders0303
Carolina Panthers0202
LA0202
Atlanta Falcons0202
Minnesota Vikings0202
New England Patriots0202
San Diego Chargers0101
Arizona Cardinals0101
Miami Dolphins0101
Indianapolis Colts0101
Green Bay Packers0101
Detroit Lions0101
Dallas Cowboys0101
Chicago Bears0101
LAC0101
New York Giants0000
Houston Texans0000
Saint Louis Rams0000
Cleveland Browns0000
\n", "
" ], "text/plain": [ " PF UR UC PF+UR+UC\n", "Team \n", "Denver Broncos 0 10 0 10\n", "Buffalo Bills 0 9 0 9\n", "Tampa Bay Buccaneers 0 8 0 8\n", "Seattle Seahawks 0 7 0 7\n", "Pittsburgh Steelers 0 5 0 5\n", "Washington Redskins 0 4 0 4\n", "Kansas City Chiefs 0 4 0 4\n", "Jacksonville Jaguars 0 4 0 4\n", "New York Jets 0 4 0 4\n", "New Orleans Saints 0 4 0 4\n", "Baltimore Ravens 0 4 0 4\n", "San Francisco 49ers 0 3 0 3\n", "Cincinnati Bengals 0 3 0 3\n", "Tennessee Titans 0 3 0 3\n", "Philadelphia Eagles 0 3 0 3\n", "Oakland Raiders 0 3 0 3\n", "Carolina Panthers 0 2 0 2\n", "LA 0 2 0 2\n", "Atlanta Falcons 0 2 0 2\n", "Minnesota Vikings 0 2 0 2\n", "New England Patriots 0 2 0 2\n", "San Diego Chargers 0 1 0 1\n", "Arizona Cardinals 0 1 0 1\n", "Miami Dolphins 0 1 0 1\n", "Indianapolis Colts 0 1 0 1\n", "Green Bay Packers 0 1 0 1\n", "Detroit Lions 0 1 0 1\n", "Dallas Cowboys 0 1 0 1\n", "Chicago Bears 0 1 0 1\n", "LAC 0 1 0 1\n", "New York Giants 0 0 0 0\n", "Houston Texans 0 0 0 0\n", "Saint Louis Rams 0 0 0 0\n", "Cleveland Browns 0 0 0 0" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Equipo con el mayor número de faltas\n", "#dentro de ciertas categorías\n", "#PF = Personal Foul\n", "#UR = Unnecessary Roughness\n", "#UC = Unsportsmanlike Conduct\n", "categorias = ['PF', 'UR', 'UC']\n", "str_categ = '+'.join(categorias)\n", "gr_equipo = df_penal.groupby('Team')\n", "df_eq_categ = gr_equipo.sum()[categorias]\n", "df_eq_categ[str_categ] = df_eq_categ.sum(axis = 1)\n", "df_eq_categ.sort_values(by = str_categ, ascending = False, inplace = True)\n", "df_eq_categ\n", "\n", "#Para validar\n", "#df_penal.loc[df_penal['Team'] == 'Was', categorias].sum(axis = 0)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PFURUCPF+UR+UC
Player
Jerry Hughes0505
Ryan Jensen0404
Darryl Skrine0404
Derek Barnett0303
Brandon Browner0303
...............
Isaiah Oliver0000
J.J. Watt0000
Ja'Wuan James0000
Jadeveon Clowney0000
Zach Strief0000
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181 rows × 4 columns

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" ], "text/plain": [ " PF UR UC PF+UR+UC\n", "Player \n", "Jerry Hughes 0 5 0 5\n", "Ryan Jensen 0 4 0 4\n", "Darryl Skrine 0 4 0 4\n", "Derek Barnett 0 3 0 3\n", "Brandon Browner 0 3 0 3\n", "... .. .. .. ...\n", "Isaiah Oliver 0 0 0 0\n", "J.J. Watt 0 0 0 0\n", "Ja'Wuan James 0 0 0 0\n", "Jadeveon Clowney 0 0 0 0\n", "Zach Strief 0 0 0 0\n", "\n", "[181 rows x 4 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Jugador con el mayor número de faltas\n", "#dentro de ciertas categorías\n", "#PF = Personal Foul\n", "#UR = Unnecessary Roughness\n", "#UC = Unsportsmanlike Conduct\n", "categorias = ['PF', 'UR', 'UC']\n", "str_categ = '+'.join(categorias)\n", "df_jug_categ = gr_jugador.sum()[categorias]\n", "df_jug_categ[str_categ] = df_jug_categ.sum(axis = 1)\n", "df_jug_categ.sort_values(by = str_categ, ascending = False, inplace = True)\n", "df_jug_categ" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.barh(y = df_jug_categ.index[10::-1],\n", " #loc no funciona con índices enteros\n", " #es por eso que se complica la expresión\n", " width = df_jug_categ.loc[df_jug_categ.index[10::-1], '+'.join(categorias)])\n", "plt.title(f'Faltas acumuladas dentro de las categorias {\"+\".join(categorias)}')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PFURUCPF+UR+UC
Year
2015029029
2016025025
2018017017
2019015015
2017011011
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" ], "text/plain": [ " PF UR UC PF+UR+UC\n", "Year \n", "2015 0 29 0 29\n", "2016 0 25 0 25\n", "2018 0 17 0 17\n", "2019 0 15 0 15\n", "2017 0 11 0 11" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Años con más amonestaciones dentro\n", "#de cierta categoría\n", "#PF = Personal Foul\n", "#UR = Unnecessary Roughness\n", "#UC = Unsportsmanlike Conduct\n", "gr_year = df_penal.groupby('Year')\n", "categorias = ['PF', 'UR', 'UC']\n", "str_categ = '+'.join(categorias)\n", "df_year_categ = gr_year.sum()[categorias]\n", "df_year_categ[str_categ] = df_year_categ.sum(axis = 1)\n", "df_year_categ.sort_values(by = str_categ, ascending = False, inplace = True)\n", "df_year_categ" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }