Convert daily pandas stock data to monthly data using first trade day of the month

Question

I have a set of calculated OHLCVA daily securities data in a pandas dataframe like this:

>>> type(data_dy)


        

Answer 1

Thank you J Bradley, your solution worked perfectly. I did have to upgrade my version of pandas from their official website though as the version installed via pip did not have CustomBusinessMonthBegin in pandas.tseries.offsets. My final code was:

#----- imports -----
import pandas as pd
from pandas.tseries.offsets import CustomBusinessMonthBegin
import pandas.io.data as web
#----- get sample data -----
df = web.get_data_yahoo('SPY', '2012-12-01', '2013-12-31')
#----- build custom calendar -----
month_index =df.index.to_period('M')
min_day_in_month_index = pd.to_datetime(df.set_index(month_index, append=True).reset_index(level=0).groupby(level=0)['Open'].min())
custom_month_starts = CustomBusinessMonthBegin(calendar = min_day_in_month_index)
#----- convert daily data to monthly data -----
ohlc_dict = {'Open':'first','High':'max','Low':'min','Close': 'last','Volume': 'sum','Adj Close': 'last'}
mthly_ohlcva = df.resample(custom_month_starts, how=ohlc_dict)

This yielded the following:

>>> mthly_ohlcva
                Volume  Adj Close    High     Low   Close    Open
Date                                                             
2012-12-03  2889875900     136.92  145.58  139.54  142.41  142.80
2013-01-01  2587140200     143.92  150.94  144.73  149.70  145.11
2013-02-01  2581459300     145.76  153.28  148.73  151.61  150.65
2013-03-01  2330972300     151.30  156.85  150.41  156.67  151.09
2013-04-01  2907035000     154.20  159.72  153.55  159.68  156.59
2013-05-01  2781596000     157.84  169.07  158.10  163.45  159.33
2013-06-03  3533321800     155.74  165.99  155.73  160.42  163.83
2013-07-01  2330904500     163.78  169.86  160.22  168.71  161.26
2013-08-01  2283131700     158.87  170.97  163.05  163.65  169.99
2013-09-02  2226749600     163.90  173.60  163.70  168.01  165.23
2013-10-01  2901739000     171.49  177.51  164.53  175.79  168.14
2013-11-01  1930952900     176.57  181.75  174.76  181.00  176.02
2013-12-02  2232775900     181.15  184.69  177.32  184.69  181.09