Pandas dataframe fillna() only some columns in place

Question

I am trying to fill none values in a Pandas dataframe with 0's for only some subset of columns.

When I do:

import pandas as pd df = pd.DataFrame(data={'a':[1,2,3,None],'b':[4,5,None,6],'c':[None,None,7,8]}) print df df.fillna(value=0, inplace=True) print df 

The output:

     a    b    c 0  1.0  4.0  NaN 1  2.0  5.0  NaN 2  3.0  NaN  7.0 3  NaN  6.0  8.0      a    b    c 0  1.0  4.0  0.0 1  2.0  5.0  0.0 2  3.0  0.0  7.0 3  0.0  6.0  8.0 

It replaces every None with 0's. What I want to do is, only replace Nones in columns a and b, but not c.

What is the best way of doing this?

Answer 1

You can select your desired columns and do it by assignment:

df[['a', 'b']] = df[['a','b']].fillna(value=0) 

The resulting output is as expected:

     a    b    c 0  1.0  4.0  NaN 1  2.0  5.0  NaN 2  3.0  0.0  7.0 3  0.0  6.0  8.0 

Answer 2

You can using dict , fillna with different value for different column

df.fillna({'a':0,'b':0}) Out[829]:       a    b    c 0  1.0  4.0  NaN 1  2.0  5.0  NaN 2  3.0  0.0  7.0 3  0.0  6.0  8.0 

After assign it back

df=df.fillna({'a':0,'b':0}) df Out[831]:       a    b    c 0  1.0  4.0  NaN 1  2.0  5.0  NaN 2  3.0  0.0  7.0 3  0.0  6.0  8.0