StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
text
Body
copied!<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>

Querying!

Guidance

An individual column

Larger individual text columns get their own page to allow for proper reading.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload