Transforming financial data from postgres to pandas dataframe for use with Zipline

I suspect you are trying to set the date as the index too early. My suggestion would be to first set_index as date and company name, then you can unstack the company name and resample.

Something like this:

In [11]: df1Out[11]:   ticker_symbol  monthly_return                date0          AAPL           0.112 1992-02-28 00:00:001            GS           0.130 1981-11-30 00:00:002            GS          -0.230 1981-12-22 00:00:00df2 = df2.set_index(['date','ticker_symbol'])df3 = df2.unstack(level=1)df4 = df.resample('M')In [14]: df2Out[14]:                           monthly_returndate       ticker_symbol                1992-02-28 AAPL                    0.1121981-11-30 GS                      0.1301981-12-22 GS                     -0.230In [15]: df3Out[15]:                monthly_return      ticker_symbol            AAPL    GSdate                               1981-11-30                NaN  0.131981-12-22                NaN -0.231992-02-28              0.112   NaNIn [16]: df4Out[16]: <class 'pandas.core.frame.DataFrame'>DatetimeIndex: 124 entries, 1981-11-30 00:00:00 to 1992-02-29 00:00:00Freq: MData columns:(monthly_return, AAPL)    1  non-null values(monthly_return, GS)      2  non-null valuesdtypes: float64(2)