[Numpy-discussion] [ANN] Nanny, faster NaN functions
Sun Nov 21 14:30:27 CST 2010
On Sun, Nov 21, 2010 at 2:48 PM, Keith Goodman <firstname.lastname@example.org> wrote:
> On Sun, Nov 21, 2010 at 10:25 AM, Wes McKinney <email@example.com> wrote:
>> On Sat, Nov 20, 2010 at 7:24 PM, Keith Goodman <firstname.lastname@example.org> wrote:
>>> On Sat, Nov 20, 2010 at 3:54 PM, Wes McKinney <email@example.com> wrote:
>>>> Keith (and others),
>>>> What would you think about creating a library of mostly Cython-based
>>>> "domain specific functions"? So stuff like rolling statistical
>>>> moments, nan* functions like you have here, and all that-- NumPy-array
>>>> only functions that don't necessarily belong in NumPy or SciPy (but
>>>> could be included on down the road). You were already talking about
>>>> this on the statsmodels mailing list for larry. I spent a lot of time
>>>> writing a bunch of these for pandas over the last couple of years, and
>>>> I would have relatively few qualms about moving these outside of
>>>> pandas and introducing a dependency. You could do the same for larry--
>>>> then we'd all be relying on the same well-vetted and tested codebase.
>>> I've started working on moving window statistics cython functions. I
>>> plan to make it into a package called Roly (for rolling). The
>>> signatures are: mov_sum(arr, window, axis=-1) and mov_nansum(arr,
>>> window, axis=-1), etc.
>>> I think of Nanny and Roly as two separate packages. A narrow focus is
>>> good for a new package. But maybe each package could be a subpackage
>>> in a super package?
>>> Would the function signatures in Nanny (exact duplicates of the
>>> corresponding functions in Numpy and Scipy) work for pandas? I plan to
>>> use Nanny in larry. I'll try to get the structure of the Nanny package
>>> in place. But if it doesn't attract any interest after that then I may
>>> fold it into larry.
>>> NumPy-Discussion mailing list
>> Why make multiple packages? It seems like all these functions are
>> somewhat related: practical tools for real-world data analysis (where
>> observations are often missing). I suspect having everything under one
>> hood would create more interest than chopping things up-- would be
>> very useful to folks in many different disciplines (finance,
>> economics, statistics, etc.). In R, for example, NA-handling is just a
>> part of every day life. Of course in R there is a special NA value
>> which is distinct from NaN-- many folks object to the use of NaN for
>> missing values. The alternative is masked arrays, but in my case I
>> wasn't willing to sacrifice so much performance for purity's sake.
>> I could certainly use the nan* functions to replace code in pandas
>> where I've handled things in a somewhat ad hoc way.
> A package focused on NaN-aware functions sounds like a good idea. I
> think a good plan would be to start by making faster, drop-in
> replacements for the NaN functions that are already in numpy and
> scipy. That is already a lot of work. After that, one possibility is
> to add stuff like nancumsum, nanprod, etc. After that moving window
and maybe group functions after that?
If there is a lot of repetition, you could use templating. Even simple
string substitution, if it is only replacing the dtype, works pretty
well. It would at least reduce some copy-paste.
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