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/1626#issuecomment-427195935,https://api.github.com/repos/pydata/xarray/issues/1626,427195935,MDEyOklzc3VlQ29tbWVudDQyNzE5NTkzNQ==,23484003,2018-10-04T22:59:19Z,2018-10-08T15:10:54Z,NONE,"I just got bit with this as well. I was basically using tuples of indices as coordinates in order to implement a [multidimensional sparse array](https://github.com/pydata/xarray/issues/1375) . My workaround is to use plain dimension `index_dim` to index the points in the N-dimensional space that I actually populate, and to have several coordinates (say `X,Y`) that all have `index_dim` as their only dimension. It's easy enough to see what the coordinates are once you select a value along `index_dim`, but I have to go outside `xarray` to locate a populated point based on it's `X,Y`-coordinates, because I can't slice along those arrays as (A) they aren't aliased to a dimension (B) they have non-unique values. I've come up with an ugly method for selecting by `tuples` of `X,Y`-coordinates: pairs = zip(x_wanted,y_wanted) pair2index = {(dataset.x[i].item(), dataset.y[i].item()):i for i in dataset.index_dim.data} try: found_indices = [pair2index[p] for p in pairs] found = dataset.isel(index_dim=found_indices) except KeyError: print ""Coordinate {} not found in dataset."".format(p) raise ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,264582338