On Sun, Nov 21, 2010 at 10:25 AM, Wes McKinney <wesmck...@gmail.com> wrote: > On Sat, Nov 20, 2010 at 7:24 PM, Keith Goodman <kwgood...@gmail.com> wrote: >> On Sat, Nov 20, 2010 at 3:54 PM, Wes McKinney <wesmck...@gmail.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 >> NumPy-Discussion@scipy.org >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > 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 stuff? _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
