python - 返回 groupby pandas 之后的所有行(即不是减少的行数，这是组键的唯一值)

Question

教程中的以下代码产生以下结果:

代码:

import pandas as pd
import numpy as np

df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
                    'foo', 'bar', 'foo', 'foo'],
                    'B' : ['one', 'one', 'two', 'three',
                    'two', 'two', 'one', 'three'],
                    'C' : np.random.randn(8),
                    'D' : np.random.randn(8)})

print(df)

grouped = df.groupby('A').mean()
print(grouped)

结果:

     A      B         C         D
0  foo    one -0.787410 -0.857863
1  bar    one  0.140572  1.330183
2  foo    two -0.770166  2.123528
3  bar  three -0.965523  0.771663
4  foo    two  0.215037 -0.597935
5  bar    two -1.023839 -0.248445
6  foo    one -1.377515  2.041921
7  foo  three -0.314333  1.379423
            C         D
A                      
bar -0.616263  0.617800
foo -0.606877  0.817815

但是我希望看到如下所有行:

0   foo one   -0.606877   0.817815
1   bar one   -0.616263   0.617800
2   foo two   -0.606877   0.817815
3   bar three -0.616263   0.617800
4   foo two   -0.606877   0.817815
5   bar two   -0.616263   0.617800
6   foo one   -0.606877   0.817815
7   foo three -0.606877   0.817815

我也愿意使用任何其他库。我只需要使用 python3 快速高效地执行此操作

提前致谢

Answer 1

使用GroupBy.transform指定列:

cols = ['C','D']
df[cols] = df.groupby('A')[cols].transform('mean')
print(df)
     A      B         C         D
0  foo    one  0.444616 -0.232363
1  bar    one  0.173897 -0.603437
2  foo    two  0.444616 -0.232363
3  bar  three  0.173897 -0.603437
4  foo    two  0.444616 -0.232363
5  bar    two  0.173897 -0.603437
6  foo    one  0.444616 -0.232363
7  foo  three  0.444616 -0.232363