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/471#issuecomment-122332344,https://api.github.com/repos/pydata/xarray/issues/471,122332344,MDEyOklzc3VlQ29tbWVudDEyMjMzMjM0NA==,1050278,2015-07-17T16:23:34Z,2015-07-17T16:23:34Z,CONTRIBUTOR,"Sounds like a good solution.  I'll work on a PR.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,94966000
https://github.com/pydata/xarray/issues/471#issuecomment-122294746,https://api.github.com/repos/pydata/xarray/issues/471,122294746,MDEyOklzc3VlQ29tbWVudDEyMjI5NDc0Ng==,1050278,2015-07-17T14:30:15Z,2015-07-17T14:30:15Z,CONTRIBUTOR,"Yes, the two values in the missing_value attribute indicate two classes of data (not collected vs below minimum detectable threshold) and these data can be access by setting mask_and_scale=False but this also results in a valid data being returned without scaling which makes it less useful.  

My question is how should xray should handle these cases?  Either replace all instances of the values in missing_value with NaN or raise a error message stating that multiple missing_values are not supported similar?  I'd be happy to create a PR implementing either case.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,94966000