issue_comments

Sounds good. As I'm writing the type-checking code I run into this question: Why would I have a xarray.core.dataset.DataVariables object as input? A DataVariables object could contain multiple DataArrays, which makes the interpretation a bit unclear. In my mind it should only be 1.) name(s) of existing index coords, or 2.) 1D DataArray(s) with dim in self.dims

What would the signature of sortby() be then? On our end we just want a more intuitive way to sort by dimension labels, so now I have sort_index(self, dims, ascending=True). sortby(), based on your description, seems like a separate method. Or any suggestion on how we can marry the two into something coherent?

A couple of things:

1.) Upon a little thinking I believe sort_values() doesn't make much sense, so I'm only working on sort_index()'. 2.) the way I handle theinplacekwarg is by ``` if inplace: self = self.isel(**{d: self.indexes[d].argsort() if ascending else self.indexes[d].argsort()[::-1] for d in dimensions}) else: return self.isel(**{d: self.indexes[d].argsort() if ascending else self.indexes[d].argsort()[::-1] for d in dimensions}) ``` But when I run ``` ds.sort_index(dims=['x', 'y'], inplace=True) ``` nothing changes. If I put apdb.set_trace()` right below the self = self*** I can evaluate self and see that it's what I want it to be. But somehow that assignment is not realized to the higher level. Any quick pointer?

On our end, we currently do the following when we need to sort by axis label (lat/lon in this case): da.reindex(indexers={'lat':sorted(da.coords['lat'].values), 'lon':sorted(da.coords['lon'].values)}) Upon first glance of the source code I think our approach goes down different code path than your .isel() approach. The most obvious difference, from a user's stand point, is probably that .reindex() returns a new object, whereas .isel() returns a view (typically). In Pandas, both sort_index() and sort_values() seem to return new objects.

We'd be happy to contribute to an xarray version of sort_index() and sort_values(). The first question is, which one would be the more robust and computationally efficient code path to take?