issue_comments: 296394094

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html_url	issue_url	id	node_id	user	created_at	updated_at	author_association	body	reactions	performed_via_github_app	issue
https://github.com/pydata/xarray/issues/967#issuecomment-296394094	https://api.github.com/repos/pydata/xarray/issues/967	296394094	MDEyOklzc3VlQ29tbWVudDI5NjM5NDA5NA==	5572303	2017-04-22T18:57:07Z	2017-04-22T18:57:07Z	CONTRIBUTOR	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?	{ "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }		171077425