Using fillna method on multiple columns of a Pandas DataFrame failed

These answers are guided by the fact that OP wanted an in place edit of an existing dataframe. Usually, I overwrite the existing dataframe with a new one.

Use pandas.DataFrame.fillna with a dict

Pandas fillna allows us to pass a dictionary that specifies which columns will be filled in and with what.

So this will work

a.fillna({'a': 0, 'b': 0})

     a    b  c
0  1.0  5.0  5
1  2.0  0.0  1
2  0.0  6.0  5
3  0.0  0.0  2

With an in place edit made possible with:

a.fillna({'a': 0, 'b': 0}, inplace=True)

NOTE: I would've just done this a = a.fillna({'a': 0, 'b': 0})

We don't save text length but we could get cute using dict.fromkeys

a.fillna(dict.fromkeys(['a', 'b'], 0), inplace=True)

loc

We can use the same format as the OP but place it in the correct columns using loc

a.loc[:, ['a', 'b']] = a[['a', 'b']].fillna(0)

a

     a    b  c
0  1.0  5.0  5
1  2.0  0.0  1
2  0.0  6.0  5
3  0.0  0.0  2

pandas.DataFrame.update

Explicitly made to make in place edits with the non-null values of another dataframe

a.update(a[['a', 'b']].fillna(0))

a

     a    b  c
0  1.0  5.0  5
1  2.0  0.0  1
2  0.0  6.0  5
3  0.0  0.0  2

Iterate column by column

I really don't like this approach because it is unnecessarily verbose

for col in ['a', 'b']:
    a[col].fillna(0, inplace=True)

a

     a    b  c
0  1.0  5.0  5
1  2.0  0.0  1
2  0.0  6.0  5
3  0.0  0.0  2

fillna with a dataframe

Use the result of a[['a', 'b']].fillna(0) as the input for another fillna. In my opinion, this is silly. Just use the first option.

a.fillna(a[['a', 'b']].fillna(0), inplace=True)

a

     a    b  c
0  1.0  5.0  5
1  2.0  0.0  1
2  0.0  6.0  5
3  0.0  0.0  2

EDIT: As @piRSquared pointed out, the first solution should be

a.loc[:, ['a', 'b']] = a[['a', 'b']].fillna(0)

to fillna in selected columns

or

a.fillna(0, inplace = True)

to fillna in all the columns

Tags:

Python

Pandas

Na

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