使用
DataFrameGroupBy.idxmax
按最大值对行进行分组,然后按
Series.isin
最后,通过价值将它们结合在一起
concat
:
df['date_one'] = pd.to_datetime(df['date_one'], dayfirst=True)
df['date_two'] = pd.to_datetime(df['date_two'], dayfirst=True)
#rule1
df1 = df.loc[df.groupby('name')['date_two'].idxmax().dropna()]
#rule2
df2 = df.loc[df.groupby('name')['date_one'].idxmax().dropna()]
df2 = df2[~df2['name'].isin(df1['name'])]
#rule3
df3 = df[~df['name'].isin(df1['name'].append(df2['name']))].drop_duplicates('name')
df = pd.concat([df1, df2, df3]).sort_index()
print (df)
name date_one date_two
0 sue NaT NaT
3 john 2019-06-13 NaT
5 sally 2019-04-23 2019-04-25
7 bob 2019-05-18 2019-06-17
