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/2027#issuecomment-377314912,https://api.github.com/repos/pydata/xarray/issues/2027,377314912,MDEyOklzc3VlQ29tbWVudDM3NzMxNDkxMg==,1217238,2018-03-29T17:40:30Z,2018-03-29T17:40:30Z,MEMBER,"I think the short answer why we don't support this is that with `__getitem__` on Dataset it's potentially ambiguous which dimensions you are slicing along. This is why we require you to specify the dimensions using `sel()`.

This might be clearer with integer indexing. We support indexing like `ds.vote[np.array([1, 2])]` or `ds.vote[xarray.DataArray([1, 2], dims='new_dim')]` because it's clear what the first dimension of `ds.vote` is. (Recall that the dimensions of the indexing key only determine how data in the result is arranged, not what is indexed.) But we don't support `ds[np.array([1, 2])]`, because axis-order dependent indexing on a Dataset is potentially ambiguous.

However, we *could* potentially support this as a form of ""multi-dimensional boolean indexing"" (https://github.com/pydata/xarray/issues/1887). Basically, `ds[key]` where `key` is a single indexer with boolean dtype could be interpreted as equivalent to `ds.where(key, drop=True)`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,309686915