python - Extract monthly categorical (dummy) variables in pandas from a time series -

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so have dataframe (df) dated data on monthly time series (end of month). looks this:

date          data 2010-01-31    625000 2010-02-28    750000 ... 2014-10-31    450000 2014-11-30    475000

i check on seasonal monthly effects.

this simple do, how can go extracting month date create categorical dummy variables use in regression?

i want this:

date        01 02 03 04 05 06 07 08 09 10 11 2010-01-31  1  0  0  0  0  0  0  0  0  0  0 2010-02-28  0  1  0  0  0  0  0  0  0  0  0 ... 2014-10-31  0  1  0  0  0  0  0  0  0  1  0   2014-11-30  0  1  0  0  0  0  0  0  0  0  1

i tried using pd.dataframe(df.index.month, index=df.index)... gives me month each date. believe need use pandas.core.reshape.get_dummies variables in 0/1 matrix format. can show me how? thanks.

this how got april:

import pandas pd import numpy np  dates = pd.date_range('20130101', periods=4, freq='ms') df = pd.dataframe(np.random.randn(4), index=dates, columns=['data'])  df.ix[dates.month==4]

the idea make dates index , boolean index selection on dataframe.

>>> df                 data 2013-01-01  0.141205 2013-02-01  0.115361 2013-03-01 -0.309521 2013-04-01 -0.236317   >>> df.ix[dates.month==4]                 data 2013-04-01 -0.236317

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