On Mon, Oct 24, 2011 at 8:40 AM, Charles R Harris <[email protected] > wrote:
> > > On Sun, Oct 23, 2011 at 11:23 PM, Wes McKinney <[email protected]>wrote: > >> On Sun, Oct 23, 2011 at 8:07 PM, Eric Firing <[email protected]> wrote: >> > On 10/23/2011 12:34 PM, Nathaniel Smith wrote: >> > >> >> like. And in this case I do think we can come up with an API that will >> >> make everyone happy, but that Mark's current API probably can't be >> >> incrementally evolved to become that API.) >> >> >> > >> > No one could object to coming up with an API that makes everyone happy, >> > provided that it actually gets coded up, tested, and is found to be fast >> > and maintainable. When you say the API probably can't be evolved, do >> > you mean that the underlying implementation also has to be redone? And >> > if so, who will do it, and when? >> > >> > Eric >> > _______________________________________________ >> > NumPy-Discussion mailing list >> > [email protected] >> > http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > >> >> I personally am a bit apprehensive as I am worried about the masked >> array abstraction "leaking" through to users of pandas, something >> which I simply will not accept (why I decided against using numpy.ma >> early on, that + performance problems). Basically if having an >> understanding of masked arrays is a prerequisite for using pandas, the >> whole thing is DOA to me as it undermines the usability arguments I've >> been making about switching to Python (from R) for data analysis and >> statistical computing. >> > > The missing data functionality looks far more like R than numpy.ma. > > For instance In [8]: a = arange(5, maskna=1) In [9]: a[2] = np.NA In [10]: a.mean() Out[10]: NA(dtype='float64') In [11]: a.mean(skipna=1) Out[11]: 2.0 In [12]: a = arange(5) In [13]: b = a.view(maskna=1) In [14]: a.mean() Out[14]: 2.0 In [15]: b[2] = np.NA In [16]: b.mean() Out[16]: NA(dtype='float64') In [17]: b.mean(skipna=1) Out[17]: 2.0 Chuck
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