I think the logical choice would be to allow a weighted average in the nan-functions, not to change `np.average`. In particular, I think it is bad API to add options for such exclusions; we have another open issue about infinitiy in the data, which leads to nan even with weight 0 (because `0*inf = nan`; see https://github.com/numpy/numpy/issues/25362); should that gets its own option?
It also seems a case that is *very* easily solved by the user with a wrapper, or, as noted in the issue, with creating a mask and using `np.ma.average`. Or, indeed, just calculating `np.nansum(data * weight, axis=...) / np.nansum(weight, axis=...)` Anyway, my 2ยข would be to think whether we actually need this addition, and, if we decide we do, to see how this would fit with the `nan*` functions. This might mean introducing `np.nanaverage` since the Array API is fairly clear that `mean()` should be unweighted. Indeed, if written such that the `NaN` skipping is done after data and weight are multiplied, that would solve https://github.com/numpy/numpy/issues/25362 too. -- Marten p.s. My other reason for hesitation is that generally when you get a NaN, something is actually wrong... > I would love some input on this issue, > https://github.com/numpy/numpy/issues/21375. > > I have created a draft PR which I believe resolves it, > https://github.com/numpy/numpy/pull/25474. Decided to go with adding > ignore_nan to np.average rather than adding weights to np.nanmean as it > seemed logical and was the path of least resistance. > Please let me know what you think! > -awestm _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
