[SciPy-User] Status of TimeSeries SciKit

[Keith Goodman]

On Wed, Jul 27, 2011 at 10:16 AM, Wes McKinney <wesmckinn at gmail.com> wrote:
> On Wed, Jul 27, 2011 at 12:28 PM, Andreas <lists at hilboll.de> wrote:

>> * Enable rolling means for sparse data. For example, if I have irregular
>> (in time) measurements, say, every one to six days, I would still like
>> to be able to calculate a rolling n-day-average. Missing values should
>> be ignored (speaking numpy: timeslice.compressed().mean())
>
> Either pandas or bottleneck will do this for you, so you can say something like:
>
> rolling_mean(ts, window=50, min_periods=5)
>
> and any sample with at least 5 data points in the window will compute
> a value, missing (NaN) data will be excluded. Bottleneck has move_mean
> and move_nanmean which will outperform pandas.rolling_mean a little
> bit since the Cython code is more specialized.

Another use case is when your data is irregularly spaced in time but
you still want a moving min/mean/median/whatever over a fixed time
window instead of a fixed number of data points. That might be
Andreas's use case.