issue_comments

Current proposal ("stack"), of da[key] and with a dimension of key's name (and probably no multiindex): python In [86]: da.values[key.values] Out[86]: array([0, 3, 6, 9]) # But the xarray version

The part about this new proposal that is most annoying is that the key needs a name, which we can use to name the new dimension. That's not too hard to do, but it is little annoying -- in practice you would have to write something like da[key.rename('key_name')] much of the time to make this work.

IMO, the perfect solution would be masking support. I.e. da[key] would return the same array with an additional variable da.mask == key: python In [87]: da[key] Out[87]: <xarray.DataArray (a: 3, b: 4)> array([[ 0, <NA>, <NA>, 3], [<NA>, <NA>, 6, <NA>], [<NA>, 9, <NA>, <NA>]]) dtype: int Dimensions without coordinates: a, b Then we could have something like da[key].stack(new_dim=["a", "b"], dropna=True): python In [87]: da[key].stack(new_dim=["a", "b"], dropna=True) Out[87]: <xarray.DataArray (newdim: 4)> array([0, 3, 6, 9]) coords{ "a" (newdim): [0, 0, 1, 2], "b" (newdim): [0, 3, 2, 1], } Dimensions without coordinates: newdim Here, dropna=True would allow avoiding to create the cross-product of a, b.

Also, that would avoid all those unnecessary float casts for free.

Since https://github.com/pydata/xarray/issues/3206 has been implemented now: Maybe fancy boolean indexing (da[boolean_mask]) could return a sparse array as well.