How to select dataframe columns using string keys when the column names are timestamps?

Question

In pandas, time series can be indexed by passing a string that is interpretable as a date. This works for a DataFrame too:

>>> dates = pd.date_range('2000-01-01', periods=8, freq='M')
>>> df = pd.DataFrame(np.random.randn(8, 4), index=dates, columns=['A', 'B', 'C', 'D'])
>>> df
                   A         B         C         D
2000-01-31  0.096115  0.069723 -1.546733 -1.661178
2000-02-29  0.256296  1.838310  0.227132  1.765269
2000-03-31  0.315862  0.167007 -1.340888  1.005260
2000-04-30  1.238728 -2.325420  1.371134 -0.373232
2000-05-31  0.639211 -0.209961 -1.006498  0.005214
2000-06-30  0.091590 -0.664554 -2.037539 -1.335070
2000-07-31  0.275373 -0.398758  0.402848  0.441035
2000-08-31  2.189259 -1.236159 -0.579680  0.878355
>>> df['2000-05']
                   A         B         C         D
2000-05-31  0.639211 -0.209961 -1.006498  0.005214

I am looking for ways to do this when the timestamps are the column names.

>>> df = df.T
>>> df['2000-05']

This yields TypeError: only integer scalar arrays can be converted to a scalar index. The same is true for

>>> df.loc[:, '2000-05']

The most immediate solution that I can think of is

>>> df.T['2000-05'].T
   2000-05-31
A    0.639211
B   -0.209961
C   -1.006498
D    0.005214

but I am wondering if there are any other good solutions. I imagine that for very large DataFrames, doing transpositions may have a performance impact that could be avoided here.

Answer 1

Well, there is always the filter option.

df = df.T
df.filter(like='2000-05')

   2000-05-31
A    1.884517
B    0.258133
C    0.809360
D   -0.069186

filter gives you greater flexibility, for example, with regular expressions:

df.filter(regex='2000-.*-30')

   2000-04-30  2000-06-30
A   -2.968870    2.064582
B   -0.844370    0.093393
C    0.027328    0.033193
D   -0.270860   -0.455323

Answer 2

Maybe you can try str , contains

df[df.index.str.contains('2000-05')].T
Out[163]: 
   2000-05-31
A    0.639211
B   -0.209961
C   -1.006498
D    0.005214