df = pd.melt(df2, id_vars='person_id', var_name='col', value_name='dates')
df['col2'] = df['col'].str.split("_").str[0]
df['count'] = df.groupby(['col2'])['dates'].transform(pd.Series.count)
df = df[df['count'] != 0]
df.drop(['col2', 'count'], axis=1, inplace=True)
print(df)
person_id col dates
0 1 H1_date 2006-10-30 00:00:00
1 1 H1 2.3
2 1 H2_date 2016-10-30 00:00:00
3 1 H2 12.3
4 1 H3_date 2026-11-30 00:00:00
5 1 H3 22.3
6 1 H4_date 2106-10-30 00:00:00
7 1 H4 42.3
10 1 H6_date 2006-10-30 00:00:00
11 1 H6 2.3
12 1 H7_date NaN
13 1 H7 2.3
14 1 H8_date 2006-10-30 00:00:00
15 1 H8 NaN
在Python中的堆栈操作期间保留少量NA并删除其余NA
在Python中的堆栈操作期间保留少量NA并删除其余NA
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