On May 9, 2015 12:54 PM, "Benjamin Root" <ben.r...@ou.edu> wrote: > > Absolutely, it should be writable. As for subclassing, that might be messy. Consider the following: > > inds = np.where(data > 5) > > In that case, I'd expect a normal, bog-standard ndarray because that is what you use for indexing (although pandas might have a good argument for having it return one of their special indexing types if "data" was a pandas array...).
Pandas doesn't subclass ndarray (anymore), so they're irrelevant to this particular discussion :-). Of course they're an argument for having a cleaner more general way of allowing non-ndarray array-like objects, but the legacy subclassing system will never be that. > Next: > > foobar = np.where(data > 5, 1, 2) > > Again, I'd expect a normal, bog-standard ndarray because the scalar elements are very simple. This question gets very complicated when considering array arguments. Consider: > > merged_data = np.where(data > 5, data, data2) > > So, what should "merged_data" be? If both "data" and "data2" are the same types, then it would be reasonable to return the same type, if possible. But what if they aren't the same? Maybe use array_priority to determine the return type? Or, perhaps it does make sense to say "sod it all" and always return an ndarray? Not sure what this has to do with Jaime's post about nonzero? There is indeed a potential question about what 3-argument where() should do with subclasses, but that's effectively a different operation entirely and to discuss it we'd need to know things like what it historically has done and why that was causing problems. -n
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