python - daily data, resample every 3 days, calculate over trailing 5 days efficiently

Question

consider the df

tidx = pd.date_range('2012-12-31', periods=11, freq='D')
df = pd.DataFrame(dict(A=np.arange(len(tidx))), tidx)
df

I want to calculate the sum over a trailing 5 days, every 3 days.

I expect something that looks like this

this was edited
what I had was incorrect. @ivan_pozdeev and @boud noticed this was a centered window and that was not my intention. Appologies for the confusion.
everyone's solutions capture much of what I was after.

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criteria

• I'm looking for smart efficient solutions that can be scaled to large data sets.

• I'll be timing solutions and also considering elegance.

• Solutions should also be generalizable for a variety of sample and look back frequencies.

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from comments

• I want a solution that generalizes to handle a look back of a specified frequency and grab anything that falls within that look back.
  • for the sample above, the look back is 5D and there may be 4 or 50 observations that fall within that look back.
• I want the timestamp to be the last observed timestamp within the look back period.

Answer 1

the df you gave us is :

             A
2012-12-31   0
2013-01-01   1
2013-01-02   2
2013-01-03   3
2013-01-04   4
2013-01-05   5
2013-01-06   6
2013-01-07   7
2013-01-08   8
2013-01-09   9
2013-01-10  10

you could create your rolling 5-day sum series and then resample it. I can't think of a more efficient way than this. overall this should be relatively time efficient.

df.rolling(5,min_periods=5).sum().dropna().resample('3D').first()
Out[36]: 
                 A
2013-01-04 10.0000
2013-01-07 25.0000
2013-01-10 40.0000