用每列的组平均值填充 NaN [重复]

我知道fillna()方法可用于在整个数据框中填充 NaN。

df.fillna(df.mean()) # fill with mean of column.

如何将平均值计算限制为 NaN 所在的组（和列）。

Exemple:

import pandas as pd 
import numpy as np 

df = pd.DataFrame({
    'a': pd.Series([1,1,1,2,2,2]),
    'b': pd.Series([1,2,np.NaN,1,np.NaN,4])
})

print df

Input

   a   b
0  1   1
1  1   2
2  1 NaN
3  2   1
4  2 NaN
5  2   4

Output（在 groupby('a') 之后并用组的平均值替换 NaN）

   a    b
0  1  1.0
1  1  2.0
2  1  1.5
3  2  1.0
4  2  2.5
5  2  4.0

IIUC 然后你可以打电话fillna http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.fillna.html结果是groupby http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.groupby.html在“a”和transform http://pandas.pydata.org/pandas-docs/stable/groupby.html#transformation on 'b':

In [44]:
df['b'] = df['b'].fillna(df.groupby('a')['b'].transform('mean'))
df

Out[44]:
   a    b
0  1  1.0
1  1  2.0
2  1  1.5
3  2  1.0
4  2  2.5
5  2  4.0

如果您有多个NaN那么我认为以下应该有效：

In [47]:
df.fillna(df.groupby('a').transform('mean'))

Out[47]:
   a    b
0  1  1.0
1  1  2.0
2  1  1.5
3  2  1.0
4  2  2.5
5  2  4.0

EDIT

In [49]:
df = pd.DataFrame({
    'a': pd.Series([1,1,1,2,2,2]),
    'b': pd.Series([1,2,np.NaN,1,np.NaN,4]),
    'c': pd.Series([1,np.NaN,np.NaN,1,np.NaN,4]),
    'd': pd.Series([np.NaN,np.NaN,np.NaN,1,np.NaN,4])
})
df

Out[49]:
   a   b   c   d
0  1   1   1 NaN
1  1   2 NaN NaN
2  1 NaN NaN NaN
3  2   1   1   1
4  2 NaN NaN NaN
5  2   4   4   4

In [50]:
df.fillna(df.groupby('a').transform('mean'))

Out[50]:
   a    b    c    d
0  1  1.0  1.0  NaN
1  1  2.0  1.0  NaN
2  1  1.5  1.0  NaN
3  2  1.0  1.0  1.0
4  2  2.5  2.5  2.5
5  2  4.0  4.0  4.0

你得到全部NaN对于“d”，因为所有值都是NaN对于团体1 for d