Fillna in multiple columns in place in Python Pandas

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)

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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 could use apply for your columns with checking dtype whether it's numeric or not by checking dtype.kind:

res = df.apply(lambda x: x.fillna(0) if x.dtype.kind in 'biufc' else x.fillna('.'))

print(res)
     A      B     City   Name
0  1.0   0.25  Seattle   Jack
1  2.1   0.00       SF    Sue
2  0.0   0.00       LA      .
3  4.7   4.00       OC    Bob
4  5.6  12.20        .  Alice
5  6.8  14.40        .   John