Pandas应用自定义函数生成match_status列报错，求正确实现方式

问题背景 #

现有如下Pandas DataFrame：

home_team_goal  away_team_goal
id      
1   1               1
2   0               0
3   0               3
4   5               0
5   1               3

需要新增match_status列，逻辑由以下函数定义：

def match_status(home_goals, other_goals):
    if home_goals > other_goals:
        return 'WIN'
    elif home_goals < other_goals:
        return 'LOSE'
    else:
        return 'DRAW'

使用语句df_match.apply(match_status, 1, df_match['home_team_goal'], df_match['away_team_goal'])时，触发如下ValueError：

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-83-2e2115165ea9> in <module>()
----> 1 df_match.apply(match_status, 1, df_match['home_team_goal'], df_match['away_team_goal'])

3 frames

/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py in __nonzero__(self)
   1536     def __nonzero__(self):
   1537         raise ValueError(
-> 1538             f"The truth value of a {type(self).__name__} is ambiguous. "
   1539             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   1540         )

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

错误原因 #

原调用方式直接将整个Series传入match_status函数，函数内的比较操作会返回布尔类型的Series，而if语句无法判断整个布尔Series的真假，因此抛出歧义错误。

正确实现方法 #

方法一：修改函数接收行对象 #

调整函数，让它接收每行数据并提取对应列的值：

def match_status(row):
    home_goals = row['home_team_goal']
    other_goals = row['away_team_goal']
    if home_goals > other_goals:
        return 'WIN'
    elif home_goals < other_goals:
        return 'LOSE'
    else:
        return 'DRAW'

# 应用函数并新增列
df_match['match_status'] = df_match.apply(match_status, axis=1)

方法二：保留原函数，用Lambda包装传递参数 #

通过Lambda函数将每行的对应列值传入原函数：

df_match['match_status'] = df_match.apply(
    lambda row: match_status(row['home_team_goal'], row['away_team_goal']),
    axis=1
)

方法三：矢量化操作（效率更高） #

避免使用apply，直接用Pandas/Numpy的矢量化函数实现，适合大数据量场景：

import numpy as np

# 嵌套np.where实现
df_match['match_status'] = np.where(
    df_match['home_team_goal'] > df_match['away_team_goal'],
    'WIN',
    np.where(
        df_match['home_team_goal'] < df_match['away_team_goal'],
        'LOSE',
        'DRAW'
    )
)

# 或者用np.select实现（逻辑更清晰）
conditions = [
    df_match['home_team_goal'] > df_match['away_team_goal'],
    df_match['home_team_goal'] < df_match['away_team_goal'],
    df_match['home_team_goal'] == df_match['away_team_goal']
]
choices = ['WIN', 'LOSE', 'DRAW']

df_match['match_status'] = np.select(conditions, choices)

内容的提问来源于stack exchange，提问作者Indika Rajapaksha