Came across this page while looking for an answer to this problem, but didn't like the existing answers. I ended up finding something better in the DataFrame.fillna documentation, and figured I'd contribute for anyone else that happens upon this.

If you have multiple columns, but only want to replace the NaN in a subset of them, you can use:

df.fillna({'Name':'.', 'City':'.'}, inplace=True)

This also allows you to specify different replacements for each column. And if you want to go ahead and fill all remaining NaN values, you can just throw another fillna on the end:

df.fillna({'Name':'.', 'City':'.'}, inplace=True).fillna(0, inplace=True)

Edit (22 Apr 2021)

Functionality (presumably / apparently) changed since original post, and you can no longer chain 2 inplace fillna() operations. You can still chain, but now must assign that chain to the df instead of modifying in place, e.g. like so:

df = df.fillna({'Name':'.', 'City':'.'}).fillna(0)

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

It should be as simple as df = df.fillna(value=0), with whatever value you want instead of 0. I'm using 17.1.

fillna is generally for carrying an observation forward or backward. Instead, I'd use np.where... If I understand what you're asking.

import numpy as np
np.where(np.isnan(df['newcolumn1']), df['oldcolumn1'], df['newcolumn1'])

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