Pandas Dataframe: Replacing NaN with row average

Question

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

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

Answer 1

As commented the axis argument to fillna is NotImplemented.

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

Note: this would be critical here as you don't want to fill in your nth columns with the nth row average.

For now you'll need to iterate through:

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

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

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