[SciPy-User] Status of TimeSeries SciKit

Wes McKinney wesmckinn@gmail....
Sun Aug 7 15:37:05 CDT 2011


On Tue, Aug 2, 2011 at 3:37 AM, Tim Michelsen
<timmichelsen@gmx-topmail.de> wrote:
>> >> I agree. I already have 50% or more of the features in
>> >> scikits.timeseries, so this gets back to my fragmentation argument
>> >> (users being stuck with a confusing choice between multiple
>> >> libraries). Let's make it happen!
>> > So what needs to be done to move things forward?
>> > Do we need to draw up a roadmap?
>> > A table with functions that respond to common use cases in natual
>> > science, computing, and economics?
>> Having a place to collect concrete use cases (like your list from the
>> prior e-mail, but with illustrative code snippets) would be good.
>> You're welcome to start doing it here:
>>
>> https://github.com/wesm/pandas/wiki
> Here goes:
> https://github.com/wesm/pandas/wiki/Time-Series-Manipulation
>
> I will fill it with my stuff.
> Shall we file feature request directly as issues?

Cool, I will start adding things when I have some time. Feel free to
file features requests as issues tagged with "Enhancement".

>> A good place to start, which I can do when I have some time, would be
>> to start moving the scikits.timeseries code into pandas. There are
>> several key components
>>
>> - Date and DateArray stuff, frequency implementations
>> - masked array time series implementations (record array and not)
>> - plotting
>> - reporting, moving window functions, etc.
>>
>> We need to evaluate Date/DateArray as they relate to numpy.datetime64
>> and see what can be done. I haven't looked closely but I'm not sure if
>> all the convenient attribute access stuff (day, month, day_of_week,
>> weekday, etc.) is available in NumPy yet. I suspect it would be
>> reasonably straightforward to wrap DateArray so it can be an Index for
>> a pandas object.
>>
>> I won't have much time for this until mid-August, but a couple days'
>> hacking should get most of the pieces into place. I guess we can just
>> keep around the masked array classes for legacy API support and for
>> feature completeness.
> I value very much the work of Pierre and Matt.
> But my difficulti with the scikit was that the code is too complex. So I was
> only able to contribute helper functions for doc fixes.
> Please, lets make it happen that this effort is not a on or 3 man show but
> results in something whcih can be maintained by the whole community.

Yes, I agree. I am painfully aware of being one of the only people
consistently working on the data structure front (judging from commit
activity at least) but I would like to get more people involved. I'm
hopeful that increasing awareness to what we're working on (e.g. I've
started blogging about pandas and related things) will draw new people
into the projects.

> Nevertheless, the timeseries scikit made my work more comfortable and
> understadable than I was able to manage with R.
>
> Regards,
> Timmie
>
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