I have a \'df\' which have a multilevel index (STK_ID,RPT_Date)
sales cogs net_pft
STK_ID RPT_Date
To use the "str.*" methods on a column, you could reset the index, filter rows with a column "str.*" method call, and re-create the index.
In [72]: x = df.reset_index(); x[x.RPT_Date.str.endswith("0630")].set_index(['STK_ID', 'RPT_Date'])
Out[72]:
sales cogs net_pft
STK_ID RPT_Date
000876 20060630 857483000 729541000 67157200
20070630 1146245000 1050808000 113468500
20080630 1932470000 1777010000 133756300
002254 20070630 501221000 289167000 118012200
However, this approach is not particularly fast.
In [73]: timeit x = df.reset_index(); x[x.RPT_Date.str.endswith("0630")].set_index(['STK_ID', 'RPT_Date'])
1000 loops, best of 3: 1.78 ms per loop
Another approach builds on the fact that a MultiIndex object behaves much like a list of tuples.
In [75]: df.index
Out[75]:
MultiIndex
[('000876', '20060331') ('000876', '20060630') ('000876', '20060930')
('000876', '20061231') ('000876', '20070331') ('000876', '20070630')
('000876', '20070930') ('000876', '20071231') ('000876', '20080331')
('000876', '20080630') ('000876', '20080930') ('002254', '20061231')
('002254', '20070331') ('002254', '20070630') ('002254', '20070930')]
Building on that, you can create a boolean array from a MultiIndex with df.index.map() and use the result to filter the frame.
In [76]: df[df.index.map(lambda x: x[1].endswith("0630"))]
Out[76]:
sales cogs net_pft
STK_ID RPT_Date
000876 20060630 857483000 729541000 67157200
20070630 1146245000 1050808000 113468500
20080630 1932470000 1777010000 133756300
002254 20070630 501221000 289167000 118012200
This is also quite a bit faster.
In [77]: timeit df[df.index.map(lambda x: x[1].endswith("0630"))]
1000 loops, best of 3: 240 us per loop