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/3077#issuecomment-508785516,https://api.github.com/repos/pydata/xarray/issues/3077,508785516,MDEyOklzc3VlQ29tbWVudDUwODc4NTUxNg==,449558,2019-07-05T14:58:12Z,2019-07-05T14:58:12Z,NONE,"Thank you @shoyer, that's very useful input. It seems that xarray would fulfill our requirements and so at least is a reasonable candidate for us.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,463841931
https://github.com/pydata/xarray/issues/3077#issuecomment-508574137,https://api.github.com/repos/pydata/xarray/issues/3077,508574137,MDEyOklzc3VlQ29tbWVudDUwODU3NDEzNw==,1217238,2019-07-04T20:48:40Z,2019-07-04T20:48:40Z,MEMBER,"Xarray currently only converts NumPy arrays with very particular dtypes:
- `object` arrays will sometimes get converted to more specific dtypes (using pandas's rules)
- `datetime64` and `timedelta64` arrays get converted into ns precision

I imagine we might add special cases like this in the future for esoteric dtypes, but numeric arrays will always be guaranteed to use views, both when creating a DataArray and casting it into a NumPy array.

(Pandas not being able to guarantee this was one of my motivations for writing xarray in the first place...)","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,463841931