How to select DataFrame columns based on partial matching?

Question

I was struggling this afternoon to find a way of selecting few columns of my Pandas DataFrame, by checking the occurrence of a certain pattern in their name (label?).

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Answer 1

I think df.keys().tolist() is the thing you're searching for.

A tiny example:

from pandas import DataFrame as df

d = df({'somename': [1,2,3], 'othername': [4,5,6]})

names = d.keys().tolist()

for n in names:
    print n
    print type(n)

Output:

othername
type 'str'

somename
type 'str'

Then with the strings you got, you can do any string operation you want.

Answer 2

Select column by partial string, can simply be done, via:

df.filter(like='hello')  # select columns which contain the word hello

And to select rows by partial string match, you can pass axis=0 to filter:

df.filter(like='hello', axis=0) 

Answer 3

Your solution using map is very good. If you really want to use str.contains, it is possible to convert Index objects to Series (which have the str.contains method):

In [1]: df
Out[1]: 
   x  y  z
0  0  0  0
1  1  1  1
2  2  2  2
3  3  3  3
4  4  4  4
5  5  5  5
6  6  6  6
7  7  7  7
8  8  8  8
9  9  9  9

In [2]: df.columns.to_series().str.contains('x')
Out[2]: 
x     True
y    False
z    False
dtype: bool

In [3]: df[df.columns[df.columns.to_series().str.contains('x')]]
Out[3]: 
   x
0  0
1  1
2  2
3  3
4  4
5  5
6  6
7  7
8  8
9  9

UPDATE I just read your last paragraph. From the documentation, str.contains allows you to pass a regex by default (str.contains('^myregex'))