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I know that it is possible to offset with the periods argument, but how would one go about return-izing daily price data that is spread throughout a month (trading days, for example)? Example data is: In [1]: df.AAPL 2009-01-02 16:00:00 90.36 2009-01-05 16:00:00 94.18 2009-01-06 16:00:00 92.62 2009-01-07 16:00:00 90.62 2009-01-08 16:00:00 92.30 2009-01-09 16:00:00 90.19 2009-01-12 16:00:00 88.28 2009-01-13 16:00:00 87.34 2009-01-14 16:00:00 84.97 2009-01-15 16:00:00 83.02 2009-01-16 16:00:00 81.98 2009-01-20 16:00:00 77.87 2009-01-21 16:00:00 82.48 2009-01-22 16:00:00 87.98 2009-01-23 16:00:00 87.98 ... 2009-12-10 16:00:00 195.59 2009-12-11 16:00:00 193.84 2009-12-14 16:00:00 196.14 2009-12-15 16:00:00 193.34 2009-12-16 16:00:00 194.20 2009-12-17 16:00:00 191.04 2009-12-18 16:00:00 194.59 2009-12-21 16:00:00 197.38 2009-12-22 16:00:00 199.50 2009-12-23 16:00:00 201.24 2009-12-24 16:00:00 208.15 2009-12-28 16:00:00 210.71 2009-12-29 16:00:00 208.21 2009-12-30 16:00:00 210.74 2009-12-31 16:00:00 209.83 Name: AAPL, Length: 252 As you can see, simply offsetting by 30 would not produce correct results, as there are gaps in the timestamp data, not every month is 30 days, etc. I know there must be an easy way to do this using pandas.

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