Pandas: Why are double brackets needed to select column after boolean indexing

Question

For a df table like below,

   A B C D
0  0 1 1 1
1  2 3 5 7
3  3 1 2 8

why are the double brackets needed for selecting specific columns after

Answer 1

Because you have no columns named 'A','C', which is what you'd be trying to do which will raise a KeyError, so you have to use an iterable to sub-select from the df.

So

df[df['A'] < 3]['A','C']

raises

KeyError: ('A', 'C')

Which is different to

In [261]:
df[df['A'] < 3][['A','C']]

Out[261]:
   A  C
0  0  1
1  2  5

This is no different to trying:

df['A','C']

hence why you need double square brackets:

df[['A','C']]

Note that the modern way is to use .ix:

In [264]:
df.ix[df['A'] < 3,['A','C']]

Out[264]:
   A  C
0  0  1
1  2  5

So that you're operating on a view rather than potentially a copy