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/1375#issuecomment-526356476,https://api.github.com/repos/pydata/xarray/issues/1375,526356476,MDEyOklzc3VlQ29tbWVudDUyNjM1NjQ3Ng==,923438,2019-08-29T20:52:10Z,2019-08-29T20:52:10Z,NONE,"@shoyer 
Is there documentation for using sparse arrays ? Could you point me to some example code ?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-513589352,https://api.github.com/repos/pydata/xarray/issues/1375,513589352,MDEyOklzc3VlQ29tbWVudDUxMzU4OTM1Mg==,923438,2019-07-21T21:32:23Z,2019-07-21T21:32:23Z,NONE,Wondering what the status on this is ? Is there a branch with this functionality implemented - would love to give it a spin !,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-511121578,https://api.github.com/repos/pydata/xarray/issues/1375,511121578,MDEyOklzc3VlQ29tbWVudDUxMTEyMTU3OA==,98330,2019-07-13T13:18:34Z,2019-07-13T13:18:34Z,NONE,"I haven't talked to anyone at SciPy'19 yet who was interested in sparse arrays, but I'll keep an eye out today.

And yes, this is a fun issue to work on and would be really nice to have!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-402699810,https://api.github.com/repos/pydata/xarray/issues/1375,402699810,MDEyOklzc3VlQ29tbWVudDQwMjY5OTgxMA==,1200058,2018-07-05T12:02:30Z,2018-07-05T12:02:30Z,NONE,"How should these sparse arrays get stored in NetCDF4?
I know that NetCDF4 has some conventions how to store sparse data, but do we have to implement our own conversion mechanisms for each sparse type?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-402699290,https://api.github.com/repos/pydata/xarray/issues/1375,402699290,MDEyOklzc3VlQ29tbWVudDQwMjY5OTI5MA==,1200058,2018-07-05T12:00:15Z,2018-07-05T12:00:15Z,NONE,"Would it be an option to use dask's sparse support?
http://dask.pydata.org/en/latest/array-sparse.html
This way xarray could let dask do the main work.

Currently I load everything into a dask array by hand and pass this dask array to xarray.
This works pretty good.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-395009307,https://api.github.com/repos/pydata/xarray/issues/1375,395009307,MDEyOklzc3VlQ29tbWVudDM5NTAwOTMwNw==,1200058,2018-06-06T09:39:43Z,2018-06-06T09:41:28Z,NONE,"I'd know a project which could make perfect use of xarray, if it would support sparse tensors:
https://github.com/theislab/anndata

Currently I have to work with both xarray and anndata to store counts in sparse arrays separate from other depending data which is a little bit annoying :)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-355383374,https://api.github.com/repos/pydata/xarray/issues/1375,355383374,MDEyOklzc3VlQ29tbWVudDM1NTM4MzM3NA==,4998171,2018-01-04T19:59:28Z,2018-01-04T19:59:28Z,NONE,"I'm interested to see if there have been any developments on this.  I currently have an application where I'm working with multiple dask arrays, some of which are sparse (text data).  It'd be worth my time to move my project to xarray, so I'm be interested in contributing something here if there is a need.","{""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543
https://github.com/pydata/xarray/issues/1375#issuecomment-311118338,https://api.github.com/repos/pydata/xarray/issues/1375,311118338,MDEyOklzc3VlQ29tbWVudDMxMTExODMzOA==,806256,2017-06-26T16:55:08Z,2017-06-26T16:55:08Z,NONE,"In case you're still looking for an application, [gene expression from single cells](https://github.com/olgabot/macosko2015) (see `data/00_original/GSM162679$i_P14Retina_$j.digital_expression.txt.gz`) is very sparse due to high gene dropout. The shape is `expression.shape (49300, 24760)` and it's mostly zeros or nans. A plain csv from this data was 2.5 gigs, which gzipped to 300 megs.

[Here](https://github.com/olgabot/macosko2015/blob/master/notebooks/05_combine_retina_data.ipynb) is an example of using `xarray` to combine these files but my kernel keeps dying when I do `ds.to_netcdf()` :( 

Hope this is a good example for sparse arrays!","{""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,221858543