On Fri, Oct 31, 2014 at 11:07 AM, Benjamin Root <ben.r...@ou.edu> wrote:
> Just to throw in my two cents here. I feel that sometimes, features are > tried out first elsewhere (possibly in scipy) and then brought down into > numpy after sufficient shakedown time. So, in some cases, I wonder if the > numpy version is actually more refined than the scipy version? Of course, > there is no way to know this from the documentation, which is a problem. > Didn't scipy have nanmean() for a while before Numpy added it in version > 1.8? > That's true for several functions in scipy.stats. And we have more deprecation in scipy.stats in favor of numpy pending. part of polynomials is another case, kind of. But I don't remember any other ones in my time. (There is also a reverse extension for scipy binned_stats based on the np.histogram code.) Josef > > Ben Root > > On Fri, Oct 31, 2014 at 10:26 AM, D. Michael McFarland <dm...@dmmcf.net> > wrote: > >> Stefan van der Walt <ste...@sun.ac.za> writes: >> >> > On 2014-10-27 15:26:58, D. Michael McFarland <dm...@dmmcf.net> wrote: >> >> What I would like to ask about is the situation this illustrates, where >> >> both NumPy and SciPy provide similar functionality (sometimes >> identical, >> >> to judge by the documentation). Is there some guidance on which is to >> >> be preferred? >> > >> > I'm not sure if you've received an answer to your question so far. My >> > advice: use the SciPy functions. SciPy is often built on more extensive >> > Fortran libraries not available during NumPy compilation, and I am not >> > aware of any cases where a function in NumPy is faster or more extensive >> > than the equivalent in SciPy. >> >> The whole thread has been interesting reading (now that I've finally >> come back to it...got busy for a few days), but this is the sort of >> answer I was hoping for. Thank you. >> >> Best, >> Michael >> _______________________________________________ >> 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 > >
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