[SciPy-User] Question about scikits.timeseries.lib.moving_funcs.mov_average

Andreas lists@hilboll...
Wed Jun 23 10:45:56 CDT 2010

Thanks a lot for your input!

> You could try to fill your missing values beforehand, w/ functions like
> backward_fill and forward_fill, then passing your series to mov_average.

Well, that's not really what I want. By doing what you suggest, I make the
assumption that the value actually changed on the day for which I have the
measurement. But each measurement is only one single point in time, so I
do not want to make this assumption.

> Or the reverse way: compress your data to get rid of the missing values,
> pass it to mov_average, reconvert it to a daily series w/
> fill_missing_dates (to get the right number of dates), then fill it w/
> backward_fill or forward_fill.

See above. Also not really what I want.

Basically, I'm looking for a simple and efficient way to do something like

   w = 11 # the window size
   s = (w-1)*.5
   for d in data.dates:
     newdata[d] = data[d-s:d+s+1].mean()



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