{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 探索 NBA 数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们首先安装 `Goldsberry` 包，项目源地址：\n", "\n", "https://github.com/bradleyfay/py-Goldsberry\n", "\n", "使用 `pip` 安装：\n", "\n", " pip install py-goldsberry\n", "\n", "该包的接口与 `pandas` 兼容，可以与 `pandas` 的 `DataFrame` 一起使用。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import goldsberry as gb\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当前使用的版本号为：" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0.8.0.1'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 球员信息" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "获得 `2015-2016` 赛季运动员的名单：" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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DISPLAY_LAST_COMMA_FIRSTFROM_YEARGAMES_PLAYED_FLAGPERSON_IDPLAYERCODEROSTERSTATUSTEAM_ABBREVIATIONTEAM_CITYTEAM_CODETEAM_IDTEAM_NAMETO_YEAR
0Acy, Quincy2012Y203112quincy_acy1SACSacramentokings1610612758Kings2015
1Adams, Jordan2014Y203919jordan_adams1MEMMemphisgrizzlies1610612763Grizzlies2015
2Adams, Steven2013Y203500steven_adams1OKCOklahoma Citythunder1610612760Thunder2015
3Afflalo, Arron2007Y201167arron_afflalo1NYKNew Yorkknicks1610612752Knicks2015
4Ajinca, Alexis2008Y201582alexis_ajinca1NOPNew Orleanspelicans1610612740Pelicans2015
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" ], "text/plain": [ " DISPLAY_LAST_COMMA_FIRST FROM_YEAR GAMES_PLAYED_FLAG PERSON_ID \\\n", "0 Acy, Quincy 2012 Y 203112 \n", "1 Adams, Jordan 2014 Y 203919 \n", "2 Adams, Steven 2013 Y 203500 \n", "3 Afflalo, Arron 2007 Y 201167 \n", "4 Ajinca, Alexis 2008 Y 201582 \n", "\n", " PLAYERCODE ROSTERSTATUS TEAM_ABBREVIATION TEAM_CITY TEAM_CODE \\\n", "0 quincy_acy 1 SAC Sacramento kings \n", "1 jordan_adams 1 MEM Memphis grizzlies \n", "2 steven_adams 1 OKC Oklahoma City thunder \n", "3 arron_afflalo 1 NYK New York knicks \n", "4 alexis_ajinca 1 NOP New Orleans pelicans \n", "\n", " TEAM_ID TEAM_NAME TO_YEAR \n", "0 1610612758 Kings 2015 \n", "1 1610612763 Grizzlies 2015 \n", "2 1610612760 Thunder 2015 \n", "3 1610612752 Knicks 2015 \n", "4 1610612740 Pelicans 2015 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "players = gb.PlayerList().players()\n", "players = pd.DataFrame(players)\n", "\n", "players.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "球员总数为：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "464\n" ] } ], "source": [ "print len(players)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过查询特定的 `TEAM_ABBREVIATION`，我们可以查看某个球队本赛季的球员，比如 `2014-2015` 赛季的总冠军金州勇士 `GSW`：" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "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", "
DISPLAY_LAST_COMMA_FIRSTFROM_YEARTEAM_ABBREVIATIONTEAM_CITYTEAM_NAMEPERSON_ID
30Barbosa, Leandro2003GSWGolden StateWarriors2571
33Barnes, Harrison2012GSWGolden StateWarriors203084
52Bogut, Andrew2005GSWGolden StateWarriors101106
86Clark, Ian2013GSWGolden StateWarriors203546
103Curry, Stephen2009GSWGolden StateWarriors201939
135Ezeli, Festus2012GSWGolden StateWarriors203105
164Green, Draymond2012GSWGolden StateWarriors203110
209Iguodala, Andre2004GSWGolden StateWarriors2738
262Livingston, Shaun2004GSWGolden StateWarriors2733
263Looney, Kevon2015GSWGolden StateWarriors1626172
279McAdoo, James Michael2014GSWGolden StateWarriors203949
377Rush, Brandon2008GSWGolden StateWarriors201575
398Speights, Marreese2008GSWGolden StateWarriors201578
414Thompson, Jason2008GSWGolden StateWarriors201574
415Thompson, Klay2011GSWGolden StateWarriors202691
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" ], "text/plain": [ " DISPLAY_LAST_COMMA_FIRST FROM_YEAR TEAM_ABBREVIATION TEAM_CITY \\\n", "30 Barbosa, Leandro 2003 GSW Golden State \n", "33 Barnes, Harrison 2012 GSW Golden State \n", "52 Bogut, Andrew 2005 GSW Golden State \n", "86 Clark, Ian 2013 GSW Golden State \n", "103 Curry, Stephen 2009 GSW Golden State \n", "135 Ezeli, Festus 2012 GSW Golden State \n", "164 Green, Draymond 2012 GSW Golden State \n", "209 Iguodala, Andre 2004 GSW Golden State \n", "262 Livingston, Shaun 2004 GSW Golden State \n", "263 Looney, Kevon 2015 GSW Golden State \n", "279 McAdoo, James Michael 2014 GSW Golden State \n", "377 Rush, Brandon 2008 GSW Golden State \n", "398 Speights, Marreese 2008 GSW Golden State \n", "414 Thompson, Jason 2008 GSW Golden State \n", "415 Thompson, Klay 2011 GSW Golden State \n", "\n", " TEAM_NAME PERSON_ID \n", "30 Warriors 2571 \n", "33 Warriors 203084 \n", "52 Warriors 101106 \n", "86 Warriors 203546 \n", "103 Warriors 201939 \n", "135 Warriors 203105 \n", "164 Warriors 203110 \n", "209 Warriors 2738 \n", "262 Warriors 2733 \n", "263 Warriors 1626172 \n", "279 Warriors 203949 \n", "377 Warriors 201575 \n", "398 Warriors 201578 \n", "414 Warriors 201574 \n", "415 Warriors 202691 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gsw_players = players.ix[players[\"TEAM_ABBREVIATION\"] == \"GSW\"]\n", "\n", "gsw_players[[\"DISPLAY_LAST_COMMA_FIRST\", \"FROM_YEAR\", \"TEAM_ABBREVIATION\", \"TEAM_CITY\", \"TEAM_NAME\", \"PERSON_ID\"]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 球员比赛数据" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过 `DISPLAY_LAST_COMMA_FIRST`，我们来查询宣布本赛季之后退役的科比布莱恩特（`Kobe, Bryant`）的信息：" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "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", "
DISPLAY_LAST_COMMA_FIRSTFROM_YEARGAMES_PLAYED_FLAGPERSON_IDPLAYERCODEROSTERSTATUSTEAM_ABBREVIATIONTEAM_CITYTEAM_CODETEAM_IDTEAM_NAMETO_YEAR
64Bryant, Kobe1996Y977kobe_bryant1LALLos Angeleslakers1610612747Lakers2015
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" ], "text/plain": [ " DISPLAY_LAST_COMMA_FIRST FROM_YEAR GAMES_PLAYED_FLAG PERSON_ID \\\n", "64 Bryant, Kobe 1996 Y 977 \n", "\n", " PLAYERCODE ROSTERSTATUS TEAM_ABBREVIATION TEAM_CITY TEAM_CODE \\\n", "64 kobe_bryant 1 LAL Los Angeles lakers \n", "\n", " TEAM_ID TEAM_NAME TO_YEAR \n", "64 1610612747 Lakers 2015 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kobe = players.ix[players[\"DISPLAY_LAST_COMMA_FIRST\"].str.contains(\"Kobe\")]\n", "\n", "kobe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "为了方便，我们将 `Kobe` 的 `ID` 放到变量中去：" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "kobe_id = 977" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们来看本赛季 `Kobe` 的比赛记录：" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ASTBLKDREBFG3AFG3MFG3_PCTFGAFGMFG_PCTFTA...PFPLUS_MINUSPTSPlayer_IDREBSEASON_IDSTLTOVVIDEO_AVAILABLEWL
0306730.4291650.3134...2-1917977622015131L
10041440.2862560.2404...0-619977522015001L
24111440.2862890.3213...4-225977222015021L
32091140.36424100.4174...016279771222015211W
45031170.63621100.47612...3638977522015221W
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5 rows × 27 columns

\n", "
" ], "text/plain": [ " AST BLK DREB FG3A FG3M FG3_PCT FGA FGM FG_PCT FTA ... PF \\\n", "0 3 0 6 7 3 0.429 16 5 0.313 4 ... 2 \n", "1 0 0 4 14 4 0.286 25 6 0.240 4 ... 0 \n", "2 4 1 1 14 4 0.286 28 9 0.321 3 ... 4 \n", "3 2 0 9 11 4 0.364 24 10 0.417 4 ... 0 \n", "4 5 0 3 11 7 0.636 21 10 0.476 12 ... 3 \n", "\n", " PLUS_MINUS PTS Player_ID REB SEASON_ID STL TOV VIDEO_AVAILABLE WL \n", "0 -19 17 977 6 22015 1 3 1 L \n", "1 -6 19 977 5 22015 0 0 1 L \n", "2 -2 25 977 2 22015 0 2 1 L \n", "3 16 27 977 12 22015 2 1 1 W \n", "4 6 38 977 5 22015 2 2 1 W \n", "\n", "[5 rows x 27 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kobe_logs = gb.player.game_logs(kobe_id)\n", "\n", "kobe_logs = pd.DataFrame(kobe_logs.logs())\n", "\n", "# 最近五场比赛\n", "kobe_logs.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "截至到全明星赛前，本赛季 `Kobe` 一共参加了 44 场比赛，其场均数据为：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0 0021500795\n", "1 0021500776\n", "2 0021500767\n", "3 0021500747\n", "4 0021500734\n", "5 0021500720\n", "6 0021500697\n", "7 0021500662\n", "8 0021500653\n", "9 0021500638\n", "10 0021500614\n", "11 0021500608\n", "12 0021500592\n", "13 0021500576\n", "14 0021500549\n", "15 0021500539\n", "16 0021500476\n", "17 0021500458\n", "18 0021500455\n", "19 0021500440\n", "20 0021500435\n", "21 0021500422\n", "22 0021500385\n", "23 0021500370\n", "24 0021500349\n", "25 0021500342\n", "26 0021500325\n", "27 0021500308\n", "28 0021500301\n", "29 0021500286\n", "30 0021500269\n", "31 0021500263\n", "32 0021500253\n", "33 0021500244\n", "34 0021500214\n", "35 0021500201\n", "36 0021500188\n", "37 0021500151\n", "38 0021500135\n", "39 0021500095\n", "40 0021500077\n", "41 0021500059\n", "42 0021500045\n", "43 0021500031\n", "44 0021500017\n", "Name: Game_ID, dtype: object" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kobe_logs.Game_ID" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "得分：16.9\n", "篮板：4.2\n", "助攻：3.4\n", "盖帽：0.2\n", "时间：29.3\n", "抢断：1.0\n", "失误：2.2\n", "犯规：1.9\n", "投篮：34.9%\n", "三分：28.0%\n", "罚篮：80.3%\n", "后篮板：3.5\n", "前篮板：0.7\n", "正负值：-7.9\n" ] } ], "source": [ "def show_avg_info(avg):\n", " print \"得分：{:.1f}\".format(avg.ix[\"PTS\"])\n", " print \"篮板：{:.1f}\".format(avg.ix[\"REB\"])\n", " print \"助攻：{:.1f}\".format(avg.ix[\"AST\"])\n", " print \"盖帽：{:.1f}\".format(avg.ix[\"BLK\"])\n", " print \"时间：{:.1f}\".format(avg.ix[\"MIN\"])\n", " print \"抢断：{:.1f}\".format(avg.ix[\"STL\"])\n", " print \"失误：{:.1f}\".format(avg.ix[\"TOV\"])\n", " print \"犯规：{:.1f}\".format(avg.ix[\"PF\"])\n", " print \"投篮：{:.1f}%\".format(avg.ix[\"FGM\"] * 100 / avg.ix[\"FGA\"])\n", " print \"三分：{:.1f}%\".format(avg.ix[\"FG3M\"] * 100 / avg.ix[\"FG3A\"])\n", " print \"罚篮：{:.1f}%\".format(avg.ix[\"FTM\"] * 100 / avg.ix[\"FTA\"])\n", " print \"后篮板：{:.1f}\".format(avg.ix[\"DREB\"])\n", " print \"前篮板：{:.1f}\".format(avg.ix[\"OREB\"])\n", " print \"正负值：{:.1f}\".format(avg.ix[\"PLUS_MINUS\"])\n", " \n", "show_avg_info(kobe_logs.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "再看一下史提芬库里的场均数据（不要问我为什么跪着看球）：" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "得分：29.8\n", "篮板：5.3\n", "助攻：6.6\n", "盖帽：0.2\n", "时间：33.9\n", "抢断：2.1\n", "失误：3.3\n", "犯规：2.0\n", "投篮：50.8%\n", "三分：45.4%\n", "罚篮：91.2%\n", "后篮板：4.5\n", "前篮板：0.9\n", "正负值：15.5\n" ] } ], "source": [ "curry_id = 201939\n", "curry_logs = gb.player.game_logs(curry_id)\n", "curry_logs = pd.DataFrame(curry_logs.logs())\n", "\n", "show_avg_info(curry_logs.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当然我们也可以对比一下职业生涯的数据：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "kobe_career = gb.player.career_stats(kobe_id)\n", "curry_career = gb.player.career_stats(curry_id)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "职业生涯最高：" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kobe\n", " GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n", "0 JAN 22 2006 PTS 81 Toronto Raptors\n", "1 JAN 24 2010 REB 16 Toronto Raptors\n", "5 JAN 15 2015 AST 17 Cleveland Cavaliers\n", "Curry\n", " GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n", "0 FEB 27 2013 PTS 54 New York Knicks\n", "1 DEC 28 2015 REB 14 Sacramento Kings\n", "5 DEC 27 2013 AST 16 Phoenix Suns\n" ] } ], "source": [ "def show_career_high(career):\n", " career_high = pd.DataFrame(career.career_high()).ix[[0,1,5]]\n", " print career_high[[\"GAME_DATE\", \"STAT\", \"STAT_VALUE\", \"VS_TEAM_CITY\", \"VS_TEAM_NAME\"]]\n", "\n", "print \"Kobe\"\n", "show_career_high(kobe_career)\n", "\n", "print \"Curry\"\n", "show_career_high(curry_career)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "本赛季最高：" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Kobe\n", " GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n", "0 FEB 02 2016 PTS 38 Minnesota Timberwolves\n", "1 FEB 04 2016 REB 12 New Orleans Pelicans\n", "5 NOV 15 2015 AST 9 Detroit Pistons\n", "Curry\n", " GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME\n", "0 OCT 31 2015 PTS 53 New Orleans Pelicans\n", "1 DEC 28 2015 REB 14 Sacramento Kings\n", "5 JAN 25 2016 STL 5 San Antonio Spurs\n" ] } ], "source": [ "def show_season_high(career):\n", " career_high = pd.DataFrame(career.season_high()).ix[[0,1,5]]\n", " print career_high[[\"GAME_DATE\", \"STAT\", \"STAT_VALUE\", \"VS_TEAM_CITY\", \"VS_TEAM_NAME\"]]\n", "\n", "print \"Kobe\"\n", "show_season_high(kobe_career)\n", "\n", "print \"Curry\"\n", "show_season_high(curry_career)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 比赛信息" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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ASTBLKDREBFG3AFG3MFG3_PCTFGAFGMFG_PCTFTA...PTSREBSEASON_IDSTLTEAM_ABBREVIATIONTEAM_IDTEAM_NAMETOVVIDEO_AVAILABLEWL
0284452980.276124560.45246...14764220157DET1610612765Detroit Pistons111W
1302362390.39187530.60934...14246220159SAC1610612758Sacramento Kings151W
2342302190.42986520.60513...123382201510SAS1610612759San Antonio Spurs131W
32963635160.45795520.54715...131462201510GSW1610612744Golden State Warriors151W
4348383180.258104520.50016...122462201510SAC1610612758Sacramento Kings201L
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5 rows × 29 columns

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" ], "text/plain": [ " AST BLK DREB FG3A FG3M FG3_PCT FGA FGM FG_PCT FTA ... PTS REB \\\n", "0 28 4 45 29 8 0.276 124 56 0.452 46 ... 147 64 \n", "1 30 2 36 23 9 0.391 87 53 0.609 34 ... 142 46 \n", "2 34 2 30 21 9 0.429 86 52 0.605 13 ... 123 38 \n", "3 29 6 36 35 16 0.457 95 52 0.547 15 ... 131 46 \n", "4 34 8 38 31 8 0.258 104 52 0.500 16 ... 122 46 \n", "\n", " SEASON_ID STL TEAM_ABBREVIATION TEAM_ID TEAM_NAME TOV \\\n", "0 22015 7 DET 1610612765 Detroit Pistons 11 \n", "1 22015 9 SAC 1610612758 Sacramento Kings 15 \n", "2 22015 10 SAS 1610612759 San Antonio Spurs 13 \n", "3 22015 10 GSW 1610612744 Golden State Warriors 15 \n", "4 22015 10 SAC 1610612758 Sacramento Kings 20 \n", "\n", " VIDEO_AVAILABLE WL \n", "0 1 W \n", "1 1 W \n", "2 1 W \n", "3 1 W \n", "4 1 L \n", "\n", "[5 rows x 29 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "game_ids = gb.GameIDs()\n", "game_ids = pd.DataFrame(game_ids.game_list())\n", "\n", "game_ids.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 获得运动员的头像" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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MIibN1t5OyJoMw80HTCmtkDjNb/4nvrv9laypah6Y4mBOkNJSLplNDA1mZeaV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\n5c8G4TdahtfBbxc1YQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "Image(\"http://stats.nba.com/media/players/230x185/\"+str(kobe_id)+\".png\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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1CLSwO3U6YynVVhO7TpB2ISSzPh8PzKGhqLXPcKKrq/vo5n1diD0np61h8e0E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\n75pOOIevo2HHnQXPLa1zp81/D1j2g3LkhLH2AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(\"http://stats.nba.com/media/players/230x185/\"+str(curry_id)+\".png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## More" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "修改了 `goldsberry\\player\\_Player.py` 代码中的错误，使之能够查询退役球员的信息，修改后的代码在本文件夹下，放到安装目录之后下面的代码均可以运行：" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from goldsberry.player import _Player as pl_old" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1997 年的球员列表：" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "players_1997 = pl_old.PlayerList(1997)\n", "\n", "players_1997 = pd.DataFrame(players_1997)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "乔丹的球员 ID：" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "893" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jordan_id = players_1997[\"PERSON_ID\"].ix[players_1997[\"DISPLAY_LAST_COMMA_FIRST\"].str.contains(\"Jordan, Michael\")]\n", "jordan_id = jordan_id[jordan_id.index[0]]\n", "jordan_id" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "乔丹在 1997-1998 赛季常规赛表现：" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "得分：28.7\n", "篮板：5.8\n", "助攻：3.5\n", "盖帽：0.5\n", "时间：38.9\n", "抢断：1.7\n", "失误：2.3\n", "犯规：1.8\n", "投篮：46.5%\n", "三分：23.8%\n", "罚篮：78.4%\n", "后篮板：4.2\n", "前篮板：1.6\n", "正负值：7.3\n" ] } ], "source": [ "jordan_logs_1997 = pl_old.game_logs(jordan_id, season=\"1997\")\n", "jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs())\n", "\n", "show_avg_info(jordan_logs_1997.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "乔丹在 1997-1998 赛季季后赛表现：" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "得分：32.4\n", "篮板：5.1\n", "助攻：3.5\n", "盖帽：0.6\n", "时间：41.0\n", "抢断：1.5\n", "失误：2.1\n", "犯规：2.2\n", "投篮：46.2%\n", "三分：30.2%\n", "罚篮：81.2%\n", "后篮板：3.5\n", "前篮板：1.6\n", "正负值：7.5\n" ] } ], "source": [ "jordan_logs_1997 = pl_old.game_logs(jordan_id, season=\"1997\", seasontype=2)\n", "jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs())\n", "\n", "show_avg_info(jordan_logs_1997.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "头像：" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TFTo0g8Js8SL4JSwtcVCd57z3gJ6Pj56P14qehZLdPT2WDsCybG+nx9PFX1zY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\n8Inz7jcquB+V9vHjx9SKNMCV9jE1FSB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"text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(\"http://stats.nba.com/media/players/230x185/\"+str(jordan_id)+\".png\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }