[Numpy-discussion] [ANN] Nanny, faster NaN functions
Sun Nov 21 13:48:16 CST 2010
On Sun, Nov 21, 2010 at 10:25 AM, Wes McKinney <firstname.lastname@example.org> wrote:
> On Sat, Nov 20, 2010 at 7:24 PM, Keith Goodman <email@example.com> wrote:
>> On Sat, Nov 20, 2010 at 3:54 PM, Wes McKinney <firstname.lastname@example.org> 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
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