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/pull/2458#issuecomment-427054269,https://api.github.com/repos/pydata/xarray/issues/2458,427054269,MDEyOklzc3VlQ29tbWVudDQyNzA1NDI2OQ==,6628425,2018-10-04T15:05:20Z,2018-10-04T15:05:20Z,MEMBER,"> Do you think there would be a benefit to implementing a TimeGrouper class based on panda's ?

My instinct would be to first pursue the simple approach that @shoyer has started here.  If it turns out that passing a `pandas.Series` rather than a `pandas.Grouper` instance in line 236 of `groupby.py` prevents us from replicating some important behavior of resample, then it might be something to think about.  

As of yet, while there are a few details that need to be added to Stephan's implementation (e.g., as he notes in the to-do comment, proper handling of the `closed`, `label`, and `base` arguments; there is some other complexity regarding how to handle gaps in the time series, etc.), I do not (yet) see any reason why these couldn't be handled with some modifications to the current approach.  The logic in `TimeGrouper` is definitely a good reference for how to handle the different arguments to resample, but if we can, I think it would be nice to avoid the complexity of defining a new `Grouper` class.  ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,365961291