更新时间:2022-12-11 22:03:06
通过提前将col4==3
作为列进行一些预处理.然后使用aggregate
Do some preprocessing by including the col4==3
as a column ahead of time. Then use aggregate
df.assign(result_col=df.col4.eq(3).astype(int)).groupby(
['col1', 'col2']
).agg(dict(col3='size', col4='nunique', result_col='sum'))
col3 result_col col4
col1 col2
1 4 2 2 1
6 1 0 1
旧答案
old answers
g = df.groupby(['col1', 'col2'])
g.agg({'col3':'size','col4': 'nunique'}).assign(
result_col=g.col4.apply(lambda x: x.eq(3).sum()))
col3 col4 result_col
col1 col2
1 4 2 1 2
6 1 1 0
稍微重新排列
slightly rearranged
g = df.groupby(['col1', 'col2'])
final_df = g.agg({'col3':'size','col4': 'nunique'})
final_df.insert(1, 'result_col', g.col4.apply(lambda x: x.eq(3).sum()))
final_df
col3 result_col col4
col1 col2
1 4 2 2 1
6 1 0 1