# 探索 NBA 数据 我们首先安装 `Goldsberry` 包,项目源地址: [https://github.com/bradleyfay/py-Goldsberry](https://github.com/bradleyfay/py-Goldsberry) 使用 `pip` 安装: ```py pip install py-goldsberry ``` 该包的接口与 `pandas` 兼容,可以与 `pandas` 的 `DataFrame` 一起使用。 In [1]: ```py import goldsberry as gb import pandas as pd ``` 当前使用的版本号为: In [2]: ```py gb.__version__ ``` Out[2]: ```py '0.8.0.1' ``` ## 球员信息 获得 `2015-2016` 赛季运动员的名单: In [3]: ```py players = gb.PlayerList().players() players = pd.DataFrame(players) players.head() ``` Out[3]: | | DISPLAY_LAST_COMMA_FIRST | FROM_YEAR | GAMES_PLAYED_FLAG | PERSON_ID | PLAYERCODE | ROSTERSTATUS | TEAM_ABBREVIATION | TEAM_CITY | TEAM_CODE | TEAM_ID | TEAM_NAME | TO_YEAR | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | Acy, Quincy | 2012 | Y | 203112 | quincy_acy | 1 | SAC | Sacramento | kings | 1610612758 | Kings | 2015 | | 1 | Adams, Jordan | 2014 | Y | 203919 | jordan_adams | 1 | MEM | Memphis | grizzlies | 1610612763 | Grizzlies | 2015 | | 2 | Adams, Steven | 2013 | Y | 203500 | steven_adams | 1 | OKC | Oklahoma City | thunder | 1610612760 | Thunder | 2015 | | 3 | Afflalo, Arron | 2007 | Y | 201167 | arron_afflalo | 1 | NYK | New York | knicks | 1610612752 | Knicks | 2015 | | 4 | Ajinca, Alexis | 2008 | Y | 201582 | alexis_ajinca | 1 | NOP | New Orleans | pelicans | 1610612740 | Pelicans | 2015 | 球员总数为: In [4]: ```py print len(players) ``` ```py 464 ``` 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`: In [5]: ```py gsw_players = players.ix[players["TEAM_ABBREVIATION"] == "GSW"] gsw_players[["DISPLAY_LAST_COMMA_FIRST", "FROM_YEAR", "TEAM_ABBREVIATION", "TEAM_CITY", "TEAM_NAME", "PERSON_ID"]] ``` Out[5]: | | DISPLAY_LAST_COMMA_FIRST | FROM_YEAR | TEAM_ABBREVIATION | TEAM_CITY | TEAM_NAME | PERSON_ID | | --- | --- | --- | --- | --- | --- | --- | | 30 | Barbosa, Leandro | 2003 | GSW | Golden State | Warriors | 2571 | | 33 | Barnes, Harrison | 2012 | GSW | Golden State | Warriors | 203084 | | 52 | Bogut, Andrew | 2005 | GSW | Golden State | Warriors | 101106 | | 86 | Clark, Ian | 2013 | GSW | Golden State | Warriors | 203546 | | 103 | Curry, Stephen | 2009 | GSW | Golden State | Warriors | 201939 | | 135 | Ezeli, Festus | 2012 | GSW | Golden State | Warriors | 203105 | | 164 | Green, Draymond | 2012 | GSW | Golden State | Warriors | 203110 | | 209 | Iguodala, Andre | 2004 | GSW | Golden State | Warriors | 2738 | | 262 | Livingston, Shaun | 2004 | GSW | Golden State | Warriors | 2733 | | 263 | Looney, Kevon | 2015 | GSW | Golden State | Warriors | 1626172 | | 279 | McAdoo, James Michael | 2014 | GSW | Golden State | Warriors | 203949 | | 377 | Rush, Brandon | 2008 | GSW | Golden State | Warriors | 201575 | | 398 | Speights, Marreese | 2008 | GSW | Golden State | Warriors | 201578 | | 414 | Thompson, Jason | 2008 | GSW | Golden State | Warriors | 201574 | | 415 | Thompson, Klay | 2011 | GSW | Golden State | Warriors | 202691 | ## 球员比赛数据 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: In [6]: ```py kobe = players.ix[players["DISPLAY_LAST_COMMA_FIRST"].str.contains("Kobe")] kobe ``` Out[6]: | | DISPLAY_LAST_COMMA_FIRST | FROM_YEAR | GAMES_PLAYED_FLAG | PERSON_ID | PLAYERCODE | ROSTERSTATUS | TEAM_ABBREVIATION | TEAM_CITY | TEAM_CODE | TEAM_ID | TEAM_NAME | TO_YEAR | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 64 | Bryant, Kobe | 1996 | Y | 977 | kobe_bryant | 1 | LAL | Los Angeles | lakers | 1610612747 | Lakers | 2015 | 为了方便,我们将 `Kobe` 的 `ID` 放到变量中去: In [7]: ```py kobe_id = 977 ``` 我们来看本赛季 `Kobe` 的比赛记录: In [8]: ```py kobe_logs = gb.player.game_logs(kobe_id) kobe_logs = pd.DataFrame(kobe_logs.logs()) # 最近五场比赛 kobe_logs.head() ``` Out[8]: | | AST | BLK | DREB | FG3A | FG3M | FG3_PCT | FGA | FGM | FG_PCT | FTA | ... | PF | PLUS_MINUS | PTS | Player_ID | REB | SEASON_ID | STL | TOV | VIDEO_AVAILABLE | WL | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | 3 | 0 | 6 | 7 | 3 | 0.429 | 16 | 5 | 0.313 | 4 | ... | 2 | -19 | 17 | 977 | 6 | 22015 | 1 | 3 | 1 | L | | 1 | 0 | 0 | 4 | 14 | 4 | 0.286 | 25 | 6 | 0.240 | 4 | ... | 0 | -6 | 19 | 977 | 5 | 22015 | 0 | 0 | 1 | L | | 2 | 4 | 1 | 1 | 14 | 4 | 0.286 | 28 | 9 | 0.321 | 3 | ... | 4 | -2 | 25 | 977 | 2 | 22015 | 0 | 2 | 1 | L | | 3 | 2 | 0 | 9 | 11 | 4 | 0.364 | 24 | 10 | 0.417 | 4 | ... | 0 | 16 | 27 | 977 | 12 | 22015 | 2 | 1 | 1 | W | | 4 | 5 | 0 | 3 | 11 | 7 | 0.636 | 21 | 10 | 0.476 | 12 | ... | 3 | 6 | 38 | 977 | 5 | 22015 | 2 | 2 | 1 | W | 5 rows × 27 columns 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: In [9]: ```py kobe_logs.Game_ID ``` Out[9]: ```py 0 0021500795 1 0021500776 2 0021500767 3 0021500747 4 0021500734 5 0021500720 6 0021500697 7 0021500662 8 0021500653 9 0021500638 10 0021500614 11 0021500608 12 0021500592 13 0021500576 14 0021500549 15 0021500539 16 0021500476 17 0021500458 18 0021500455 19 0021500440 20 0021500435 21 0021500422 22 0021500385 23 0021500370 24 0021500349 25 0021500342 26 0021500325 27 0021500308 28 0021500301 29 0021500286 30 0021500269 31 0021500263 32 0021500253 33 0021500244 34 0021500214 35 0021500201 36 0021500188 37 0021500151 38 0021500135 39 0021500095 40 0021500077 41 0021500059 42 0021500045 43 0021500031 44 0021500017 Name: Game_ID, dtype: object ``` In [10]: ```py def show_avg_info(avg): print "得分:{:.1f}".format(avg.ix["PTS"]) print "篮板:{:.1f}".format(avg.ix["REB"]) print "助攻:{:.1f}".format(avg.ix["AST"]) print "盖帽:{:.1f}".format(avg.ix["BLK"]) print "时间:{:.1f}".format(avg.ix["MIN"]) print "抢断:{:.1f}".format(avg.ix["STL"]) print "失误:{:.1f}".format(avg.ix["TOV"]) print "犯规:{:.1f}".format(avg.ix["PF"]) print "投篮:{:.1f}%".format(avg.ix["FGM"] * 100 / avg.ix["FGA"]) print "三分:{:.1f}%".format(avg.ix["FG3M"] * 100 / avg.ix["FG3A"]) print "罚篮:{:.1f}%".format(avg.ix["FTM"] * 100 / avg.ix["FTA"]) print "后篮板:{:.1f}".format(avg.ix["DREB"]) print "前篮板:{:.1f}".format(avg.ix["OREB"]) print "正负值:{:.1f}".format(avg.ix["PLUS_MINUS"]) show_avg_info(kobe_logs.mean()) ``` ```py 得分:16.9 篮板:4.2 助攻:3.4 盖帽:0.2 时间:29.3 抢断:1.0 失误:2.2 犯规:1.9 投篮:34.9% 三分:28.0% 罚篮:80.3% 后篮板:3.5 前篮板:0.7 正负值:-7.9 ``` 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): In [11]: ```py curry_id = 201939 curry_logs = gb.player.game_logs(curry_id) curry_logs = pd.DataFrame(curry_logs.logs()) show_avg_info(curry_logs.mean()) ``` ```py 得分:29.8 篮板:5.3 助攻:6.6 盖帽:0.2 时间:33.9 抢断:2.1 失误:3.3 犯规:2.0 投篮:50.8% 三分:45.4% 罚篮:91.2% 后篮板:4.5 前篮板:0.9 正负值:15.5 ``` 当然我们也可以对比一下职业生涯的数据: In [12]: ```py kobe_career = gb.player.career_stats(kobe_id) curry_career = gb.player.career_stats(curry_id) ``` 职业生涯最高: In [13]: ```py def show_career_high(career): career_high = pd.DataFrame(career.career_high()).ix[[0,1,5]] print career_high[["GAME_DATE", "STAT", "STAT_VALUE", "VS_TEAM_CITY", "VS_TEAM_NAME"]] print "Kobe" show_career_high(kobe_career) print "Curry" show_career_high(curry_career) ``` ```py Kobe GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME 0 JAN 22 2006 PTS 81 Toronto Raptors 1 JAN 24 2010 REB 16 Toronto Raptors 5 JAN 15 2015 AST 17 Cleveland Cavaliers Curry GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME 0 FEB 27 2013 PTS 54 New York Knicks 1 DEC 28 2015 REB 14 Sacramento Kings 5 DEC 27 2013 AST 16 Phoenix Suns ``` 本赛季最高: In [14]: ```py def show_season_high(career): career_high = pd.DataFrame(career.season_high()).ix[[0,1,5]] print career_high[["GAME_DATE", "STAT", "STAT_VALUE", "VS_TEAM_CITY", "VS_TEAM_NAME"]] print "Kobe" show_season_high(kobe_career) print "Curry" show_season_high(curry_career) ``` ```py Kobe GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME 0 FEB 02 2016 PTS 38 Minnesota Timberwolves 1 FEB 04 2016 REB 12 New Orleans Pelicans 5 NOV 15 2015 AST 9 Detroit Pistons Curry GAME_DATE STAT STAT_VALUE VS_TEAM_CITY VS_TEAM_NAME 0 OCT 31 2015 PTS 53 New Orleans Pelicans 1 DEC 28 2015 REB 14 Sacramento Kings 5 JAN 25 2016 STL 5 San Antonio Spurs ``` ## 比赛信息 In [15]: ```py game_ids = gb.GameIDs() game_ids = pd.DataFrame(game_ids.game_list()) game_ids.head() ``` Out[15]: | | AST | BLK | DREB | FG3A | FG3M | FG3_PCT | FGA | FGM | FG_PCT | FTA | ... | PTS | REB | SEASON_ID | STL | TEAM_ABBREVIATION | TEAM_ID | TEAM_NAME | TOV | VIDEO_AVAILABLE | WL | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 0 | 28 | 4 | 45 | 29 | 8 | 0.276 | 124 | 56 | 0.452 | 46 | ... | 147 | 64 | 22015 | 7 | DET | 1610612765 | Detroit Pistons | 11 | 1 | W | | 1 | 30 | 2 | 36 | 23 | 9 | 0.391 | 87 | 53 | 0.609 | 34 | ... | 142 | 46 | 22015 | 9 | SAC | 1610612758 | Sacramento Kings | 15 | 1 | W | | 2 | 34 | 2 | 30 | 21 | 9 | 0.429 | 86 | 52 | 0.605 | 13 | ... | 123 | 38 | 22015 | 10 | SAS | 1610612759 | San Antonio Spurs | 13 | 1 | W | | 3 | 29 | 6 | 36 | 35 | 16 | 0.457 | 95 | 52 | 0.547 | 15 | ... | 131 | 46 | 22015 | 10 | GSW | 1610612744 | Golden State Warriors | 15 | 1 | W | | 4 | 34 | 8 | 38 | 31 | 8 | 0.258 | 104 | 52 | 0.500 | 16 | ... | 122 | 46 | 22015 | 10 | SAC | 1610612758 | Sacramento Kings | 20 | 1 | L | 5 rows × 29 columns ## 获得运动员的头像 In [16]: ```py from IPython.display import Image Image("http://stats.nba.com/media/players/230x185/"+str(kobe_id)+".png") ``` Out[16]: ![](data:image/png;base64,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) In [17]: ```py Image("http://stats.nba.com/media/players/230x185/"+str(curry_id)+".png") ``` Out[17]: 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) ## More 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: In [18]: ```py from goldsberry.player import _Player as pl_old ``` 1997 年的球员列表: In [19]: ```py players_1997 = pl_old.PlayerList(1997) players_1997 = pd.DataFrame(players_1997) ``` 乔丹的球员 ID: In [20]: ```py jordan_id = players_1997["PERSON_ID"].ix[players_1997["DISPLAY_LAST_COMMA_FIRST"].str.contains("Jordan, Michael")] jordan_id = jordan_id[jordan_id.index[0]] jordan_id ``` Out[20]: ```py 893 ``` 乔丹在 1997-1998 赛季常规赛表现: In [21]: ```py jordan_logs_1997 = pl_old.game_logs(jordan_id, season="1997") jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs()) show_avg_info(jordan_logs_1997.mean()) ``` ```py 得分:28.7 篮板:5.8 助攻:3.5 盖帽:0.5 时间:38.9 抢断:1.7 失误:2.3 犯规:1.8 投篮:46.5% 三分:23.8% 罚篮:78.4% 后篮板:4.2 前篮板:1.6 正负值:7.3 ``` 乔丹在 1997-1998 赛季季后赛表现: In [22]: ```py jordan_logs_1997 = pl_old.game_logs(jordan_id, season="1997", seasontype=2) jordan_logs_1997 = pd.DataFrame(jordan_logs_1997.logs()) show_avg_info(jordan_logs_1997.mean()) ``` ```py 得分:32.4 篮板:5.1 助攻:3.5 盖帽:0.6 时间:41.0 抢断:1.5 失误:2.1 犯规:2.2 投篮:46.2% 三分:30.2% 罚篮:81.2% 后篮板:3.5 前篮板:1.6 正负值:7.5 ``` 头像: In [23]: ```py Image("http://stats.nba.com/media/players/230x185/"+str(jordan_id)+".png") ``` Out[23]: ![](data:image/png;base64,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)