Changing certain values in multiple columns of a pandas DataFrame at once

Question

Suppose I have the following DataFrame:

In [1]: df Out[1]:   apple banana cherry 0     0      3   good 1     1      4    bad 2     2      5   good 

This works as expected:

In [2]: df['apple'][df.cherry == 'bad'] = np.nan In [3]: df Out[3]:   apple banana cherry 0     0      3   good 1   NaN      4    bad 2     2      5   good 

But this doesn't:

In [2]: df[['apple', 'banana']][df.cherry == 'bad'] = np.nan In [3]: df Out[3]:   apple banana cherry 0     0      3   good 1     1      4    bad 2     2      5   good 

Why? How can I achieve the conversion of both the 'apple' and 'banana' values without having to write out two lines, as in

In [2]: df['apple'][df.cherry == 'bad'] = np.nan In [3]: df['banana'][df.cherry == 'bad'] = np.nan 

Answer 1

You should use loc and do this without chaining:

In [11]: df.loc[df.cherry == 'bad', ['apple', 'banana']] = np.nan  In [12]: df Out[12]:     apple  banana cherry 0      0       3   good 1    NaN     NaN    bad 2      2       5   good 

See the docs on returning a view vs a copy, if you chain the assignment is made to the copy (and thrown away) but if you do it in one loc then pandas cleverly realises you want to assign to the original.