On Sat, Jun 25, 2011 at 12:17 PM, Wes McKinney <wesmck...@gmail.com> wrote:
> > Agree. My basic observation about numpy.ma is that it's a finely > crafted solution for a different set of problems than the ones I have. > I just don't want the same thing to happen here so I'm stuck writing > code (like I am now) that looks like > > mask = y.mask > the_sum = y.sum(axis) > the_count = mask.sum(axis) > the_sum[the_count == 0] = nan > > Yeah, you are expecting NaN behavior there, in which case, using NaNs without masks is fine. But, for a general solution, you might want to consider that masked_array provides a "recordmask" as well as a mask. Although, the documentation is very lacking, and using masked arrays with record arrays seems very clumsy, but that is certainly something that could get cleaned up. Also, as a cleaner version of your code: the_sum = y.sum(axis) the_sum.mask = np.any(y.mask, axis)
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