issue_comments: 557579503

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https://github.com/pydata/xarray/issues/1603#issuecomment-557579503 https://api.github.com/repos/pydata/xarray/issues/1603 557579503 MDEyOklzc3VlQ29tbWVudDU1NzU3OTUwMw== 2067093 2019-11-22T15:34:57Z 2019-11-22T15:34:57Z NONE

Thanks @NowanIlfideme for your feedback.

Could you perhaps share a gist of code related to your use case?

The first example in this comment is similar to my use case: https://github.com/pydata/xarray/issues/3213#issuecomment-520741706 . There are several "core" dimensions, but some part of the coordinates may be hierarchical or cross-defined (e.g. country > province > city > building, but also country > province > voting district > building). We might have a full or nearly-full panel in the MultiIndex representation, but have a huge cross product (even if we keep strictly hierarchical dimensions out).

Meanwhile using a true COO sparse representation (as I understand it) will likely end up with slower operations overall, since nearly all machine learning models (think: linear regression) require a dense array input anyways.

I'll make an example of this when I find some free time, along with a contrasting one in Pandas. :)

 
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