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<p>For this I think you can use <code>transform</code> and <code>rolling_sum</code>. Starting from your dataframe, I might do something like:</p> <pre><code>>>> df["DATE"] = pd.to_datetime(df["DATE"]) # switch to datetime to ease sorting >>> df = df.sort(["STOCK", "DATE"]) >>> rsum_columns = "DATA1", "DATA3" >>> grouped = df.groupby("STOCK")[rsum_columns] >>> new_columns = grouped.transform(lambda x: pd.rolling_sum(x, 4)) >>> df[new_columns.columns + "_TTM"] = new_columns >>> df DATE STOCK DATA1 DATA2 DATA3 DATA1_TTM DATA3_TTM 0 2012-01-01 00:00:00 ABC 0.40 0.88 0.22 NaN NaN 1 2012-04-01 00:00:00 ABC 0.50 0.49 0.13 NaN NaN 2 2012-07-01 00:00:00 ABC 0.85 0.36 0.83 NaN NaN 3 2012-10-01 00:00:00 ABC 0.28 0.12 0.39 2.03 1.57 4 2013-01-01 00:00:00 ABC 0.86 0.87 0.58 2.49 1.93 5 2013-04-01 00:00:00 ABC 0.95 0.39 0.87 2.94 2.67 6 2013-07-01 00:00:00 ABC 0.60 0.25 0.56 2.69 2.40 7 2013-10-01 00:00:00 ABC 0.15 0.28 0.69 2.56 2.70 8 2011-01-01 00:00:00 XYZ 0.94 0.40 0.50 NaN NaN 9 2011-04-01 00:00:00 XYZ 0.65 0.19 0.81 NaN NaN 10 2011-07-01 00:00:00 XYZ 0.89 0.59 0.69 NaN NaN 11 2011-10-01 00:00:00 XYZ 0.12 0.09 0.18 2.60 2.18 12 2012-01-01 00:00:00 XYZ 0.25 0.94 0.55 1.91 2.23 13 2012-04-01 00:00:00 XYZ 0.07 0.22 0.67 1.33 2.09 14 2012-07-01 00:00:00 XYZ 0.46 0.08 0.54 0.90 1.94 15 2012-10-01 00:00:00 XYZ 0.04 0.03 0.94 0.82 2.70 [16 rows x 7 columns] </code></pre> <p>I don't know what you're asking by "Also, I want to check to see if the dates fall within 1 year", so I'll leave that alone.</p>
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1. COThanks. By that part, I meant...let's say you take the last four dates and it goes 1/1/1993, 4/1/12, 7/1/12, 10/1/12 -- if there is a data error. I wouldn't want to sum those four. I would want that one to say NaN.
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