I have a dataframe with 50 columns. I want to replace NAs with 0 in 10 columns.
What's the simplest, most readable way of doing this?
I was hoping for something like:
cols = ['a', 'b', 'c', 'd']
df[cols].fillna(0, inplace=True)
But that gives me ValueError: Must pass DataFrame with boolean values only.
I found this answer, but it's rather hard to understand.
you can use update():
In [145]: df
Out[145]:
a b c d e
0 NaN NaN NaN 3 8
1 NaN NaN NaN 8 7
2 NaN NaN NaN 2 8
3 NaN NaN NaN 7 4
4 NaN NaN NaN 4 9
5 NaN NaN NaN 1 9
6 NaN NaN NaN 7 7
7 NaN NaN NaN 6 5
8 NaN NaN NaN 0 0
9 NaN NaN NaN 9 5
In [146]: df.update(df[['a','b','c']].fillna(0))
In [147]: df
Out[147]:
a b c d e
0 0.0 0.0 0.0 3 8
1 0.0 0.0 0.0 8 7
2 0.0 0.0 0.0 2 8
3 0.0 0.0 0.0 7 4
4 0.0 0.0 0.0 4 9
5 0.0 0.0 0.0 1 9
6 0.0 0.0 0.0 7 7
7 0.0 0.0 0.0 6 5
8 0.0 0.0 0.0 0 0
9 0.0 0.0 0.0 9 5
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