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/1086#issuecomment-259044958,https://api.github.com/repos/pydata/xarray/issues/1086,259044958,MDEyOklzc3VlQ29tbWVudDI1OTA0NDk1OA==,167164,2016-11-08T04:47:56Z,2016-11-08T04:47:56Z,NONE,"Ok, no worries. I'll try it if it gets desperate :)
Thanks for your help, shoyer!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187608079
https://github.com/pydata/xarray/issues/1086#issuecomment-259041491,https://api.github.com/repos/pydata/xarray/issues/1086,259041491,MDEyOklzc3VlQ29tbWVudDI1OTA0MTQ5MQ==,167164,2016-11-08T04:16:26Z,2016-11-08T04:16:26Z,NONE,"So it would be more efficient to concat all of the datasets (subset for the relevant variables), and then just use a single .to_dataframe() call on the entire dataset? If so, that would require quite a bit of refactoring on my part, but it could be worth it.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187608079
https://github.com/pydata/xarray/issues/1086#issuecomment-259033970,https://api.github.com/repos/pydata/xarray/issues/1086,259033970,MDEyOklzc3VlQ29tbWVudDI1OTAzMzk3MA==,167164,2016-11-08T03:14:50Z,2016-11-08T03:14:50Z,NONE,"Yeah, I'm loading each file separately with `xr.open_dataset()`, since it's not really a multi-file dataset (it's a lot of single-site datasets, some of which have different variables, and overlapping time dimensions). I don't think I can avoid loading them separately...
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187608079
https://github.com/pydata/xarray/issues/1086#issuecomment-259026069,https://api.github.com/repos/pydata/xarray/issues/1086,259026069,MDEyOklzc3VlQ29tbWVudDI1OTAyNjA2OQ==,167164,2016-11-08T02:19:01Z,2016-11-08T02:19:01Z,NONE,"Not easily - most scripts require multiple (up to 200, of which the linked one is one of the smallest, some are up to 10Mb) of these datasets in a specific directory structure, and rely on a couple of private python modules. I was just asking because I thought I might have been missing something obvious, but now I guess that isn't the case. Probably not worth spending too much time on this - if it starts becoming a real problem for me, I will try to generate something self-contained that shows the problem. Until then, maybe it's best to assume that xarray/pandas are doing the best they can given the requirements, and close this for now.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187608079
https://github.com/pydata/xarray/issues/1086#issuecomment-258774196,https://api.github.com/repos/pydata/xarray/issues/1086,258774196,MDEyOklzc3VlQ29tbWVudDI1ODc3NDE5Ng==,167164,2016-11-07T08:30:25Z,2016-11-07T08:30:25Z,NONE,"I loaded it from a netcdf file. There's an example you can play with at https://dl.dropboxusercontent.com/u/50684199/MitraEFluxnet.1.4_flux.nc
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187608079
https://github.com/pydata/xarray/issues/1086#issuecomment-258755061,https://api.github.com/repos/pydata/xarray/issues/1086,258755061,MDEyOklzc3VlQ29tbWVudDI1ODc1NTA2MQ==,167164,2016-11-07T06:12:27Z,2016-11-07T06:12:27Z,NONE,"Slightly slower (using `%timeit` in ipython)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,187608079
https://github.com/pydata/xarray/issues/1086#issuecomment-258753366,https://api.github.com/repos/pydata/xarray/issues/1086,258753366,MDEyOklzc3VlQ29tbWVudDI1ODc1MzM2Ng==,167164,2016-11-07T05:56:26Z,2016-11-07T05:56:26Z,NONE,"Squeeze is pretty much identical in efficiency. Seems very slightly better (2-5%) on smaller datasets. (I still need to add the final `[data_vars]` to get rid of the extraneous index_var columns, but that doesn't affect performance much).
I'm not calling `pandas.tslib.array_to_timedelta64`, `to_dataframe` is - the caller list is (sorry, I'm not sure of a better way to show this):
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