python - 如何修改 pd.dataframe 中的列值

Question

背景:其实我想修改dataframe中的值，只保留前20名的运动，其他的应该像“其他”一样显示。它是现有列的副本，如下所示:

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

并且包含 top20 运动名称的过滤器生成如下:

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

修改过程如下: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')

但是，上面的2个方法并没有奏效。我可以像这段代码那样做的唯一方法:

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'

以上两种方式有什么问题？感谢帮助。

Answer 1

您的第一个方法永远行不通，因为您的函数不会返回一个序列，也不会返回任何行计算。

您的第二种方法不是就地，您需要分配回一个系列。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使用 pd.Series.isin 更有效地表达，通过 ~ 取反:

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

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