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/1680#issuecomment-342653688,https://api.github.com/repos/pydata/xarray/issues/1680,342653688,MDEyOklzc3VlQ29tbWVudDM0MjY1MzY4OA==,806256,2017-11-07T23:04:32Z,2017-11-07T23:04:32Z,NONE,"Thank you! Replacing with underscores works just fine, I'll do that for my data for now.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,270430189
https://github.com/pydata/xarray/issues/1680#issuecomment-341481978,https://api.github.com/repos/pydata/xarray/issues/1680,341481978,MDEyOklzc3VlQ29tbWVudDM0MTQ4MTk3OA==,806256,2017-11-02T16:38:05Z,2017-11-02T16:38:05Z,NONE,Ah sorry about that. Just re-tar'd and uploaded the dereferenced files. All of the files are in the bucket itself which can be viewed with `aws s3 ls s3://olgabot-maca/xarray-coordinates-to-variables/`,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,270430189
https://github.com/pydata/xarray/issues/1680#issuecomment-341297143,https://api.github.com/repos/pydata/xarray/issues/1680,341297143,MDEyOklzc3VlQ29tbWVudDM0MTI5NzE0Mw==,806256,2017-11-02T02:09:31Z,2017-11-02T02:09:31Z,NONE,"Sorry about that, try again now. Damn permissions!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,270430189
https://github.com/pydata/xarray/issues/1638#issuecomment-341220444,https://api.github.com/repos/pydata/xarray/issues/1638,341220444,MDEyOklzc3VlQ29tbWVudDM0MTIyMDQ0NA==,806256,2017-11-01T19:51:55Z,2017-11-01T19:51:55Z,NONE,Posted the lost coordinate issue here: https://github.com/pydata/xarray/issues/1680,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,266320445
https://github.com/pydata/xarray/issues/1638#issuecomment-341198220,https://api.github.com/repos/pydata/xarray/issues/1638,341198220,MDEyOklzc3VlQ29tbWVudDM0MTE5ODIyMA==,806256,2017-11-01T18:33:24Z,2017-11-01T18:33:24Z,NONE,"Using v0.9.6 with `engine='h5netcdf'`
```
CPU times: user 1min, sys: 47.7 s, total: 1min 48s
Wall time: 2min 19s
```
Using #1648:
```
CPU times: user 1min 5s, sys: 54.9 s, total: 2min
Wall time: 2min 1s
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,266320445
https://github.com/pydata/xarray/issues/1638#issuecomment-339162189,https://api.github.com/repos/pydata/xarray/issues/1638,339162189,MDEyOklzc3VlQ29tbWVudDMzOTE2MjE4OQ==,806256,2017-10-24T23:02:34Z,2017-10-24T23:03:18Z,NONE,"Thank you for looking into this! I used the default engine to save, which looks like it was `netcdf4`. I did `pip install h5netcdf` and saved again. It took longer, ~2min instead of seconds. Loading was still 110ms and all the features are objects again! Though the coordinates --> variables thing is still happening.
```
Dimensions: (cell: 53760, gene: 23438)
Coordinates:
* cell (cell) object 'A17-B000126-3_39_F-1-1' ...
* gene (gene) object '0610005C13Rik' ...
Data variables:
Columns sorted (cell) float64 nan nan nan nan nan nan nan ...
Comments (cell) object 'nan' 'nan' 'nan' 'nan' ...
Double check (cell) float64 nan nan nan nan nan nan nan ...
EXP_ID (cell) object '170925_A00111_0066_AH3TKNDMXX' ...
Experiment ID (cell) object 'exp22' 'exp22' 'exp22' ...
FACS.instument (cell) object 'Sony SIM1' 'Sony SIM1' ...
FACS.selection (cell) object 'Multiple' 'Multiple' ...
Location (cell) object 'MACA20_3' 'MACA20_3' ...
Lysis Plate Batch (cell) object '20' '20' '20' '20' '20' ...
Number of input reads (cell) int64 1229254 730274 1075370 ...
Plate (cell) object '1' '1' '1' '1' '1' '1' '1' ...
TAXON (cell) object 'mus' 'mus' 'mus' 'mus' ...
Uniquely mapped reads number (cell) int64 1017682 634557 941828 1392029 ...
WELL_MAPPING (cell) object 'B000126' 'B000126' ...
counts (cell, gene) int64 0 0 0 0 442 0 0 0 0 0 0 ...
dNTP.batch (cell) object '457912' '457912' '457912' ...
date.prepared (cell) object '07-06-17' '07-06-17' ...
date.sorted (cell) object '170707' '170707' '170707' ...
log10 (cell, gene) float64 0.0 0.0 0.0 0.0 2.646 ...
log2 (cell, gene) float64 0.0 0.0 0.0 0.0 8.791 ...
mouse.age (cell) object '3' '3' '3' '3' '3' '3' '3' ...
mouse.id (cell) object '3_39_F' '3_39_F' '3_39_F' ...
mouse.number (cell) object '39' '39' '39' '39' '39' ...
mouse.sex (cell) object 'F' 'F' 'F' 'F' 'F' 'F' 'F' ...
nozzle.size (cell) object '100' '100' '100' '100' ...
oligodT.order.no (cell) object '6/23/17 12757296' ...
plate.type (cell) object 'Biorad HSP3901' ...
preparation.site (cell) object 'Biohub' 'Biohub' 'Biohub' ...
subtissue (cell) object 'nan' 'nan' 'nan' 'nan' ...
tissue (cell) object 'Skin' 'Skin' 'Skin' 'Skin' ...
```
Not sure if it matters, but one detail is that I created ~250 individual datasets (each sized at ~300 samples x 20,000 features) and then used `xr.concat(datasets, dim='cell')` to concatenate them because I couldn't read them all into memory at once.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,266320445
https://github.com/pydata/xarray/issues/1638#issuecomment-337418537,https://api.github.com/repos/pydata/xarray/issues/1638,337418537,MDEyOklzc3VlQ29tbWVudDMzNzQxODUzNw==,806256,2017-10-18T00:17:25Z,2017-10-18T00:17:25Z,NONE,"Also, how did all the `Coordinates` somehow get moved into `Data variables` ?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,266320445
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