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/1265#issuecomment-279516519,https://api.github.com/repos/pydata/xarray/issues/1265,279516519,MDEyOklzc3VlQ29tbWVudDI3OTUxNjUxOQ==,1217238,2017-02-13T20:45:14Z,2017-02-13T20:45:14Z,MEMBER,I'm definitely happy to look at a more realistic / complete example. My PhD work was actually doing quantum simulations.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-279464724,https://api.github.com/repos/pydata/xarray/issues/1265,279464724,MDEyOklzc3VlQ29tbWVudDI3OTQ2NDcyNA==,1217238,2017-02-13T17:40:02Z,2017-02-13T17:40:02Z,MEMBER,"Xarray adds labels to NumPy array, so it can't handle variable length arrays any better than NumPy.

Basically, your options are to either (a) store stored numpy arrays using dtype=object (not really recommended), (b) pad each array up to a common length with NaNs (used to mark missing values in xarray) or (c) put multiple variables in an `xarray.Dataset` and use different dimension names for the variable length dimension.

Depending on your exact use case, either (b) or (c) could be a good solution.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854