On Sun, Nov 21, 2010 at 2:48 PM, Keith Goodman <kwgood...@gmail.com> wrote: > 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?
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. Josef > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion