熊猫数据框:用行平均值替换NaN

问题描述：

我正在尝试学习熊猫，但请对以下内容感到困惑.我想用行平均值替换NaNs是一个数据框.因此，像df.fillna(df.mean(axis=1))这样的东西应该可以工作，但是由于某种原因它对我来说却失败了.我是否想念任何东西，我做错了什么?是因为其未执行；参见链接此处

I am trying to learn pandas but i have been puzzled with the following please. I want to replace NaNs is a dataframe with the row average. Hence something like df.fillna(df.mean(axis=1)) should work but for some reason it fails for me. Am I missing anything please, something I'm doing wrong? Is is because its not implemented; see link here

import pandas as pd
import numpy as np

pd.__version__
Out[44]:
'0.15.2'

In [45]:
df = pd.DataFrame()
df['c1'] = [1, 2, 3]
df['c2'] = [4, 5, 6]
df['c3'] = [7, np.nan, 9]
df

Out[45]:
    c1  c2  c3
0   1   4   7
1   2   5   NaN
2   3   6   9

In [46]:  
df.fillna(df.mean(axis=1)) 

Out[46]:
    c1  c2  c3
0   1   4   7
1   2   5   NaN
2   3   6   9

但是类似的东西看起来可以正常工作

However something like this looks to work fine

df.fillna(df.mean(axis=0)) 

Out[47]:
    c1  c2  c3
0   1   4   7
1   2   5   8
2   3   6   9

答

如前所述，fillna的轴参数为

As commented the axis argument to fillna is NotImplemented.

df.fillna(df.mean(axis=1), axis=1)

注意:这在这里非常重要，因为您不想用第n行平均值填写第n列.

现在您需要遍历:

In [11]: m = df.mean(axis=1)
         for i, col in enumerate(df):
             # using i allows for duplicate columns
             # inplace *may* not always work here, so IMO the next line is preferred
             # df.iloc[:, i].fillna(m, inplace=True)
             df.iloc[:, i] = df.iloc[:, i].fillna(m)

In [12]: df
Out[12]:
   c1  c2   c3
0   1   4  7.0
1   2   5  3.5
2   3   6  9.0

另一种方法是先填充转置然后再转置，这样可能会更有效...

An alternative is to fillna the transpose and then transpose, which may be more efficient...

df.T.fillna(df.mean(axis=1)).T

熊猫数据框:用行平均值替换NaN

相关推荐