Question

背景：实际上，我想修改数据框中的值，仅应保留前20个运动，其他应显示为“其他”。它是现有列的副本，如下所示：

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

生成包含top20运动名称的过滤器，如下所示：

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

修改过程如下：方法1：

 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2：

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

但是，上面的2方法无效。我可以像下面的代码一样做的唯一方法：

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'

以上两种方式的问题是什么？感谢帮助。

Answer 1

您的第一个方法将永远无法工作，因为您的函数不会返回序列，也不会return进行行计算。

您的第二个方法不是就地，您需要分配回一个序列。例如：

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

您可以使用pd.Series.isin来更有效地表达最终解决方案，而通过~来否定

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

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

如何修改pd.dataframe中的列值

1 个答案: