Question

我有大量来自不同足球比赛的csv文件。数据看起来像下面的示例。 result列可以包含3个可能的值：

H->主队获胜（主场获得+ 3分）
A->客队获胜（客场将获得+ 3分）
D->平局（两支球队得+1分）

   HomeTeam    AwayTeam Result
0   FC_Fake  ABC_United      H
1  Team_123   FC_Berlin      A
2   FC_FAKE    TEAM_123      D

我想更新文件，以便每行包含每个团队as they are at the start of the match的积分总数（因此，尚未针对该行本身进行的比赛的结果进行更新）

我已使用以下代码更新数据框，因此该数据框包含每个团队的points_[TEAM_NAME]虚拟列。

# Teams is a python list I extracted earlier
for team in teams:
    df['points_' + team] = 0

目标是转换数据框，以使上面的示例变成下面的示例。

（同样，这些点应代表比赛开始时的情况。因此，即使FC_FAKE在第一行赢得比赛，Points_FC_FAKE列也为0）

HomeTeam | AwayTeam | Result  Points_FC_FAKE | Points_TEAM_123 | Points_FC_Berlin |  etc
-------------------------------------------------------------------------------
 FC_Fake  ABC_United    H         0                  0             0
 Team_123 FC_Berlin     A         3                  0             0
 FC_FAKE  Team_123      D         3                  0             3

我创建了以下python函数，如果该函数在数据框中的所有行上进行迭代，则应解析结果并将正确数量的点数奖励给合适的团队。

def point_updater(x):
    if x['Result'] == 'H':        
        home = x['HomeTeam']
        x.shift(-1)['points_' + home] += 3
        return x

    elif x['Result'] == 'A':        
        away = x['AwayTeam']
        x.shift(-1)['points_' + away] += 3
        return x

    elif x['Result'] == 'D':        
        home = x['AwayTeam']
        away = x['AwayTeam']
        x.shift(-1)['points_' + home] += 1
        x.shift(-1)['points_' + away] += 1
        return x

问题是当我将此功能应用于数据框时，点不变（全部保持为0）

df = df.apply(point_counter, axis=1)
df['points_FC_Fake'].value_counts()
----
0    2691

有人知道我在做什么错吗？

Answer 1

执行这些操作的方法可能更简洁，但这现在就足够了。您可以使用df.replace()将键Result映射到它们的关联值，然后使用pd.concat()和pd.DataFrame.pivot()获得所需的结果：

import pandas as pd

df = pd.DataFrame({'HomeTeam': ['FC_Fake','Team_123','FC_Fake'], 'AwayTeam': ['ABC_United','FC_Berlin','Team_123'], 'Result': ['H','A','D']})

remap = df.replace({'H': 3, 'A': 3, 'D': 1})

new = pd.concat([remap.pivot(columns='HomeTeam', values='Result'), remap.pivot(columns='AwayTeam', values='Result')], axis=1).shift(1).fillna(0).astype(int).cumsum()

final = pd.concat([df, new], axis=1)

收益：

   HomeTeam    AwayTeam Result  FC_Fake  Team_123  ABC_United  FC_Berlin  \
0   FC_Fake  ABC_United      H        0         0           0          0   
1  Team_123   FC_Berlin      A        3         0           3          0   
2   FC_Fake    Team_123      D        3         3           3          3   

   Team_123  
0         0  
1         0  
2         0

Answer 2

在某些例外情况下，我们可以使用iterrows。另外，通过在开始计算之前进行一些清理，我使您的代码更具防故障能力和通用性：

# Convert to uppercase letters 
df['HomeTeam'] = df['HomeTeam'].str.upper()
df['AwayTeam'] = df['AwayTeam'].str.upper()

# get a list off all the teams in competition
lst_teams = list(set(list(df.HomeTeam.unique()) + list(df.AwayTeam.unique())))

# Create columns for each team
for team in lst_teams:
    df[team] = 0

# Iterrate over each row and assign correct points
for idx, r in df.iterrows():
    if r['Result'] == 'H':
        df.loc[[idx], [r['HomeTeam']]] = 3
    if r['Result'] == 'A':
        df.loc[[idx], [r['AwayTeam']]] = 3
    if r['Result'] == 'D':
        df.loc[[idx], [r['AwayTeam']]] = 1
        df.loc[[idx], [r['HomeTeam']]] = 1

# Shift the rows one down, since points are only available at start of match
df.iloc[:, 3:] = df.iloc[:, 3:].cumsum().shift(1).fillna(0).astype(int)

输出

print(df)
   HomeTeam    AwayTeam Result  ABC_UNITED  TEAM_123  FC_FAKE  FC_BERLIN
0   FC_FAKE  ABC_UNITED      H           0         0        0          0
1  TEAM_123   FC_BERLIN      A           0         0        3          0
2   FC_FAKE    TEAM_123      D           0         0        3          3

Answer 3

将您的功能更改为此：

def point_updater(x):
    if x['Result'] == 'H':    
        home = x['HomeTeam']
        x['points_' + home] += 3
        return x

    elif x['Result'] == 'A':        
        away = x['AwayTeam']
        x['points_' + away] += 3
        return x

    elif x['Result'] == 'D':        
        home = x['HomeTeam']
        away = x['AwayTeam']
        x['points_' + home] += 1
        x['points_' + away] += 1
        return x

然后将其添加到代码末尾：

df = df.apply(point_updater,axis=1)
for team in teams:
    df["points_" + team]= df["points_" + team].cumsum()

熊猫-用于有条件地更新下一行中的某些列的功能

3 个答案: