issue_comments

Which behaviour can you reproduce?

python ds = xr.Dataset( data_vars={ "a": ( ("x", "y"), [[1, 11, 26], [2, 12, 22], [3, 13, 23], [4, 16,24], [5, 15, 25]], ) }, coords={"x": [1, 1, 1, 2, 2], "y": [0, 0, 1]}, ).expand_dims({"z": [0, 1, 1, 2, 2]}) a_ds = ds.groupby("y").std().a a_da = ds.a.groupby("y").std() a_ds.identical(a_da) which seems to be the same as python a_ds = ds.groupby("y").mean("y").a a_da = ds.a.groupby("y").mean("y") a_ds.identical(a_da) My impression is that this puts the dimension grouped over to the front: - before: ds.a.dims == ("z", "x", "y") - afterwards: a_ds.dims == ("y", "z", "x")

But I agree this discussion does not really belong here. So, thanks all!

I can also reproduce this with std

Edit: I'm fairly certain this is due to what I mentioned in https://github.com/pydata/xarray/pull/3527#issuecomment-553994537

but that's only because mean removes x, doesn't it?

well, it was surprising to me. It also might be problematic that it matters if we first pull out a DataArray and then compute or the other way around: ```python

ds = xr.Dataset( ... data_vars={ ... "a": ( ... ("x", "y"), ... [[1, 11, 26], [2, 12, 22], [3, 13, 23],^I[4, 16,^I24], [5, 15, 25]], ... ) ... }, ... coords={"x": [1, 1, 1, 2, 2], "y": [0, 0, 1]}, ... ) a_ds = ds.groupby("y").quantile(0).a a_da = ds.a.groupby("y").quantile(0) a_ds.identical(a_da) False a_ds.transpose().identical(a_da) True ```

sure. But as Dataset.quantile and DataArray.quantile don't have examples either, I think it might be worth to do that in a new PR?

Also, what will we do re transpose?

the difference is here, where in DataArrayGroupBy the original dimension order is restored. DatasetGroupBy does not do that, so the test requires the transpose

that would open a new category. Should I use New Features?

is Bug Fixes the correct section for this?