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/5648#issuecomment-890165626,https://api.github.com/repos/pydata/xarray/issues/5648,890165626,IC_kwDOAMm_X841Dtl6,3460034,2021-07-30T21:34:32Z,2021-07-30T21:41:41Z,CONTRIBUTOR,"Count me in for the meeting!

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Here are a few suggestions about possible topics to add to the agenda (based on linked issues/discussions), if we can fit it all in:

- Canonical/minimal API of a ""duck array"" and how to detect it (though may be superseded by NEPs [30](https://numpy.org/neps/nep-0030-duck-array-protocol.html) and [47](https://numpy.org/neps/nep-0047-array-api-standard.html) among others)
- Consistency of type deferral (i.e., between construction, binary ops, `__array_ufunc__`, `__array_function__`, and array modules...for example, these are uniform in Pint, but construction and array module functions are deliberately different from the others for Dask arrays)
- API for inter-type casting and changing what types are used in a nested array (e.g. #3245 and #5568)
- How to handle unknown duck arrays
- Nested array reprs (both short and full)
- Best practices for ""carrying through"" operations belonging to wrapped types (i.e., doing Dask-related things to a Pint Quantity or xarray DataArray that contains a Dask array), even if multiple layers deep

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Also, tagging a few other array type libraries and maintainers/contributors who may be interested (please ping the relevant folks if you know them):

- unyt (@ngoldbaum)
- astropy.units (??)
- NumPy masked arrays (??)
- scipp (@SimonHeybrock, xref https://github.com/pydata/xarray/issues/3509)

(interesting side note is the first three of these are all ndarray subclasses right now...perhaps discussing the interplay between array subclassing and wrapping is in order too?)
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