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.

OK great. To confirm, this is what it would look like:

Yes, this looks right to me.

I've been trying to conceptualize why I think the where equivalence (the original proposal) is better than the stack proposal (the latter).

Here are two reasons why I like the stack version:

1. It's more NumPy like -- boolean indexing in NumPy returns a flat array in the same way
2. It doesn't need dtype promotion to handle possibly missing values, so it will have more predictable semantics.

As a side note: one nice feature of using isel() for stacking is that it does not create a MultiIndex, which can be expensive. But there's no reason why we necessarily need to do that for stack(). I'll open a new issue to discuss adding an optional parameter.

• I'm not sure how the setitem would work; da[key] = value?

To match the semantics of NumPy, value would need to have matching dims/coords to those of da[key]. In other words, it would also need to be stacked.

• If someone wants the stack result, it's less work to do original -> where result -> stack result relative to original -> stack result -> where result; which suggests they're more composable?

I'm not quite sure this is true -- it's the difference between needing to call stack() vs unstack().

It's worth noting that there is at least one other way boolean indexing could work:

• ds[key] could work like ds.stack({key.name: key.dims}).isel({key.name: np.flatnonzero(key.data)}), except without creating a MultiIndex. Arguably this might be more useful and also more consistent with NumPy itself. It's also more similar to the operation @Hoeze wants in https://github.com/pydata/xarray/issues/5179.

We can't support both with the same syntax, so we have to make a choice here :).

See also the discussion about what drop_duplicates/unique should do over in https://github.com/pydata/xarray/pull/5089.