我正在使用nba_py获取一些NBA比赛的记分牌数据。
以下是数据结构的示例：
SEASON | GAME_DATE_EST | GAME_SEQUENCE | GAME_ID | HOME_TEAM_ID | VISITOR_TEAM_ID | WINNER
0 2013 2013-10-05T00:00:00 1 11300001 12321 1610612760 V
1 2013 2013-10-05T00:00:00 2 11300002 1610612754 1610612741 V
2 2013 2013-10-05T00:00:00 3 11300003 1610612745 1610612740 V
3 2013 2013-10-05T00:00:00 4 11300004 1610612747 1610612744 H
4 2013 2013-10-06T00:00:00 1 11300005 12324 1610612755 V
您可以在此处找到部分数据：NBA Games Data。
我的目标是创建并添加以下列的原始数据：
对于家庭团队：
1. Total wins/losses for hometeam if hometeam plays at home ("HOMETEAM_HOME_WINS"/"HOMETEAM_HOME_LOSSES")
2. Total wins/losses for hometeam if hometeam is visiting ("HOMETEAM_VISITOR_WINS"/"HOMETEAM_VISITOR_LOSSES")
访客_team：
3. Total wins/losses for visitor_team if visitor_team plays at home ("VISITOR_TEAM_HOME_WINS"/"VISITOR_TEAM_HOME_LOSSES")
4. Total wins/losses for visitor_team if visitor_team is visiting ("VISITOR_TEAM_VISITOR_WINS"/"VISITOR_TEAM_VISITOR_LOSSES")
我的第一个简单方法如下：
def get_home_team_home_wins(x):
hometeam = x.HOME_TEAM_ID
season = x.SEASON
gid = x.name
season_hometeam_games = grouped_seasons_hometeams.get_group((season, hometeam))
home_games = season_hometeam_games[(season_hometeam_games.index < gid)]
if not home_games.empty:
try:
home_wins = home_games.FTR.value_counts()["H"]
except Exception as e:
home_wins = 0
else:
home_wins = 0
grouped_seasons_hometeams = df.groupby(["SEASON", "HOME_TEAM_ID"])
df["HOMETEAM_HOME_WINS"] = df.apply(lambda x: get_home_team_home_wins(x), axis=1)
另一种方法是iterating over the rows：
grouped_seasons = df.groupby("SEASON")
df["HOMETEAM_HOME_WINS"] = 0
current_season = 0
for index,row in df.iterrows():
season = row.SEASON
if season != current_season:
current_season = season
season_games = grouped_seasons.get_group(current_season)
hometeam = row.HOME_TEAM_ID
gid = row.name
games = season_games[(season_games.index < gid)]
home_games = games[(games.HOME_TEAM_ID == hometeam)]
if not home_games.empty:
try:
home_wins = home_games.FTR.value_counts()["H"]
except Exception as e:
home_wins = 0
else:
home_wins = 0
row["HOME_TEAM_HOME_WINS_4"] = home_wins
df.ix[index] = row
更新1：
如果在家中玩游戏并且如果它在家中游戏，则有以下功能可以获得家庭的胜负。一个类似于visitor_team的人。
def get_home_team_home_wins_losses(x):
hometeam = x.HOME_TEAM_ID
season = x.SEASON
gid = x.name
games = df[(df.SEASON == season) & (df.index < gid)]
home_team_home_games = games[(games.HOME_TEAM_ID == hometeam)]
# HOMETEAM plays at home
if not home_team_home_games.empty:
home_team_home_games_value_counts = home_team_home_games.FTR.value_counts()
try:
home_team_home_wins = home_team_home_games_value_counts["H"]
except Exception as e:
home_team_home_wins = 0
try:
home_team_home_losses = home_team_home_games_value_counts["V"]
except Exception as e:
home_team_home_losses = 0
else:
home_team_home_wins = 0
home_team_home_losses = 0
return [home_team_home_wins, home_team_home_losses]
def get_home_team_visitor_wins_losses(x):
hometeam = x.HOME_TEAM_ID
season = x.SEASON
gid = x.name
games = df[(df.SEASON == season) & (df.index < gid)]
home_team_visitor_games = games[(games.VISITOR_TEAM_ID == hometeam)]
# HOMETEAM visits
if not home_team_visitor_games.empty:
home_team_visitor_games_value_counts = home_team_visitor_games.FTR.value_counts()
try:
home_team_visitor_wins = home_team_visitor_games_value_counts["V"]
except Exception as e:
home_team_visitor_wins = 0
try:
home_team_visitor_losses = home_team_visitor_games_value_counts["H"]
except Exception as e:
home_team_visitor_losses = 0
else:
home_team_visitor_wins = 0
home_team_visitor_losses = 0
return [home_team_visitor_wins, home_team_visitor_losses]
df["HOME_TEAM_HOME_WINS"], df["HOME_TEAM_HOME_LOSSES"] = zip(*df.apply(lambda x: get_home_team_home_wins_losses(x), axis=1))
df["HOME_TEAM_VISITOR_WINS"], df["HOME_TEAM_VISITOR_LOSSES"] = zip(*df.apply(lambda x: get_home_team_visitor_wins_losses(x), axis=1))
df["HOME_TEAM_WINS"] = df["HOME_TEAM_HOME_WINS"] + df["HOME_TEAM_VISITOR_WINS"]
df["HOME_TEAM_LOSSES"] = df["HOME_TEAM_HOME_LOSSES"] + df["HOME_TEAM_VISITOR_LOSSES"]
上述方法效率不高。所以，我正在考虑使用groupby，但它并不是很清楚。
每当我发现更高效的内容时，我都会添加更新。
有什么想法吗？感谢。
答案 0 :(得分：0)
考虑使用transform()但首先有条件地创建HOMEWINNER和VISITWINNER整数列。使用numpy.where()进行/ else计算时，注释掉更容易阅读，您可能/可能没有作为包使用。
请注意transform()会保留所有行，但会按ID进行汇总，因此特定HOME_TEAM_ID的每条记录都应重复这些汇总列中的值。
nbadf['VISITWINNER'] = [1 if x == 'V' else 0 for x in nbadf['WINNER']]
#nbadf['VISITWINNER'] = np.where(nbadf['WINNER']=='V', 1, 0)
nbadf['HOMEWINNER'] = [1 if x == 'H' else 0 for x in nbadf['WINNER']]
#nbadf['HOMEWINNER'] = np.where(nbadf['WINNER']=='H', 1, 0)
nbadf['HOME_TEAM_WINS'] = nbadf.groupby(['HOME_TEAM_ID','SEASON'])\
['HOMEWINNER'].transform(sum)
nbadf['HOME_TEAM_LOSSES'] = nbadf.groupby(['HOME_TEAM_ID','SEASON'])\
['VISITWINNER'].transform(sum)
nbadf['VISIT_TEAM_WINS'] = nbadf.groupby(['VISITOR_TEAM_ID','SEASON'])\
['VISITWINNER'].transform(sum)
nbadf['VISIT_TEAM_LOSSES'] = nbadf.groupby(['VISITOR_TEAM_ID','SEASON'])\
['HOMEWINNER'].transform(sum)
nbadf.drop(['HOMEWINNER', 'VISITWINNER'],inplace=True,axis=1)
# SEASON ... WINNER HOME_TEAM_WINS HOME_TEAM_LOSSES VISIT_TEAM_WINS VISIT_TEAM_LOSSES
#0 2013 ... V 0 1 1 0
#1 2013 ... V 0 1 1 0
#2 2013 ... V 0 1 1 0
#3 2013 ... H 1 0 0 1
#4 2013 ... V 0 1 1 0
现在，对于以后访问的主队的实例，反之亦然，考虑将ID与子集化数据帧合并（如果需要，更改列号）。这捕获了也是访客团队的主队。因此，在mergedf上运行上方的聚合（并使用此HOMEWINNER和WINNER_x使用VISITWINNER计算相同的条件WINNER_y：
# MERGES HOME SUBSET DF AND VISITOR SUBSET DF
mergedf = pd.merge(nbadf[[0,1,2,3,4,6]], nbadf[[0,1,2,3,5,6]],
left_on=['HOME_TEAM_ID'], right_on=['VISITOR_TEAM_ID'], how='inner')
mergedf['HOMETEAM_AS_VISITOR_WINS'] = mergedf.groupby(['VISITOR_TEAM_ID','SEASON_y'])\
['VISITWINNER'].transform(sum)
mergedf['VISITORTEAM_AS_HOME_WINS'] = mergedf.groupby(['HOME_TEAM_ID','SEASON_x'])\
['HOMEWINNER'].transform(sum)