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  • benbovy · 1 ✖

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  • xr.doctor(): diagnostics on a Dataset / DataArray ? · 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
1305780610 https://github.com/pydata/xarray/issues/6308#issuecomment-1305780610 https://api.github.com/repos/pydata/xarray/issues/6308 IC_kwDOAMm_X85N1KGC benbovy 4160723 2022-11-07T15:28:35Z 2022-11-07T15:28:35Z MEMBER

The kind of data wrapped in an Xarray Dataset (e.g., a Numpy array, a Dask array or any other array #5648) is already something useful that xr.doctor or xr.describe may tell!

From my experience of introducing Xarray to new users, they often completely ignore what is under the hood until something or someone makes them aware, likely after they experience some weird behavior or performance issue that is hard to figure out by themselves. Xarray objects are flexible container wrappers connected to a wide range of other Python libraries, such that it is hard to give a short introduction that covers all the important aspects (lazy / non-lazy, chunked / non-chunked, etc.). For example, it may be possible that someone who has never heard of Dask nor Zarr follows an Xarray tutorial that starts by opening a chunked dataset from a zarr store. In this case the rich repr of the Xarray Dataset doesn't even help.

Rather than a performance report or a profiling tool, the proposal here (still very elusive) is to provide a helper function that returns some information and explanation in plain english (why not with some hyperlinks, pretty printing, etc.) that would help users making sense of an Xarray object and its wrapped data/metadata. Some kind of interactive documentation very specific to the actual Xarray object. Some kind of smart tool that would partially "replace" custom (though very basic) user support.

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  xr.doctor(): diagnostics on a Dataset / DataArray ? 1151751524

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