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  • olgabot · 1 ✖

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  • Sparse arrays · 1 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
311118338 https://github.com/pydata/xarray/issues/1375#issuecomment-311118338 https://api.github.com/repos/pydata/xarray/issues/1375 MDEyOklzc3VlQ29tbWVudDMxMTExODMzOA== olgabot 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 (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 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!

 
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   Sparse arrays 221858543

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