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/2399#issuecomment-420444668,https://api.github.com/repos/pydata/xarray/issues/2399,420444668,MDEyOklzc3VlQ29tbWVudDQyMDQ0NDY2OA==,6815844,2018-09-11T22:16:32Z,2018-09-11T22:16:32Z,MEMBER,"Sorry that I couldn't join the discussion here.

Thanks, @horta, for giving the nice document.
We tried to use the consistent terminology in the docs, but I agree that it would be nice to have a list of the definitions.
I think it might be better to discuss in another issue. See #2410.

For `loc` and `sel` issues. 
One thing I don't agree is 

> The result of d.loc[i] is equal to d.sel(x=i). Also, it seems reasonable to expect the its result should be the same as d0.sel(x=i) for d0 given by

As xarray inherits not only from pandas but also from numpy's multi-dimensional array.
That is, we need to be very consistent with the resultant shape of indexing.
It would be confusing if a selection from different dimensional arrays becomes the same.

> I do think that handling duplicate matches with indexing is an important use-case. This comes up with nearest neighbor matching as well -- it would be useful to be able to return the full set of matches within a given distance, not just the nearest match.

I also think that what is lacking in xarray is this functionality. 
Any interest to help us for this?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,357156174