issues: 496460488

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id node_id number title user state locked assignee milestone comments created_at updated_at closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
496460488 MDU6SXNzdWU0OTY0NjA0ODg= 3326 quantile with Dask arrays 6475152 closed 0     0 2019-09-20T17:14:59Z 2019-11-25T15:57:49Z 2019-11-25T15:57:49Z NONE      

Currently the quantile method raises an exception when it encounters a Dask array.

python if isinstance(self.data, dask_array_type): raise TypeError( "quantile does not work for arrays stored as dask " "arrays. Load the data via .compute() or .load() " "prior to calling this method." ) I think it's because taking a quantile needs to see all the data in the dimension it's quantile-ing, or blocked/approximate methods weren't on hand when the feature was added. Dask arrays where the dimension being quantile-ed was exactly one chunk in extent seem like a special case where no blocked algorithm is needed.

The problem with following the suggestion of the exception (loading the array into memory) is that "wide and shallow" arrays are too big to load into memory, yet each chunk is statistically independent if the quantile dimension is the "shallow" dimension.

I'm not necessarily proposing delegating to Dask's quantile (unless it's super easy), but wanted to explore this special case described above.

Related links:
* https://github.com/pydata/xarray/issues/2999 * https://stackoverflow.com/a/47103407/745557

Thank you!

EDIT: added stackoverflow link

 
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