如何修改pd.dataframe中的列值

Question

背景： 实际上我想修改数据框中的值，只保留前20项运动，并将其余项显示为“其他”。 这是现有列的副本，如下所示：

athlete_events['Sport_modified'] = athlete_events['Sport']

生成包含前20个运动名称的筛选器如下：

top20_sport = athlete_events['Sport'].value_counts().head(20).index

修改过程如下所示： 方法一：

 def classify_sports(cols, filters):
for i in cols:
    if i in filters:
        pass
    else:
        i = 'Others'
classify_sports(athlete_events.Sport_modified, top20_sport)

方法二：

athlete_events.Sport_modified.apply(lambda x : x if x in top20_sport else 'Others')

然而，上述两种方法均未能起效。我只能像以下代码一样实现此功能：

athlete_events.loc[
(athlete_events['Sport'] !='Athletics')&
(athlete_events['Sport'] !='Gymnastics')&
(athlete_events['Sport'] !='Swimming')&
(athlete_events['Sport'] !='Shooting')&
(athlete_events['Sport'] !='Cycling')&
(athlete_events['Sport'] !='Fencing')&
(athlete_events['Sport'] !='Rowing')&
(athlete_events['Sport'] !='Cross Country Skiing')&
(athlete_events['Sport'] !='Alpine Skiing')&
(athlete_events['Sport'] !='Wrestling')&
(athlete_events['Sport'] !='Football')&
(athlete_events['Sport'] !='Sailing')&
(athlete_events['Sport'] !='Equestrianism')&
(athlete_events['Sport'] !='Canoeing')&
(athlete_events['Sport'] !='Boxing')&
(athlete_events['Sport'] !='Speed Skating')&
(athlete_events['Sport'] !='Ice Hockey')&
(athlete_events['Sport'] !='Hockey')&
(athlete_events['Sport'] !='Biathlon')&
(athlete_events['Sport'] !='Basketball')
,'Sport_modified'] = 'Others'

那两种方法存在什么问题？谢谢帮助。

- leo022

Answer 1

你的第一种方法永远不会起作用，因为你的函数没有返回一个系列，也没有对逐行计算进行任何return。

你的第二种方法不是原地操作，你需要将其重新赋值给一个系列。例如：

df['sport_modified'] = df['sport'].apply(lambda x : x if x in top20_sport else 'Others')

您的最终解决方案可以更有效地使用pd.Series.isin来表达，通过~取反：

L = ['Athletics', 'Gymnastics', ...]

df.loc[~df['sport'].isin(L), 'sport_modified'] = 'Others'

- jpp