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in Data Science by (17.6k points)

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?

1 Answer

0 votes
by (41.4k points)

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

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

Output:

     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

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