issue_comments: 201340940

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https://github.com/pydata/xarray/pull/802#issuecomment-201340940 https://api.github.com/repos/pydata/xarray/issues/802 201340940 MDEyOklzc3VlQ29tbWVudDIwMTM0MDk0MA== 1217238 2016-03-25T15:51:02Z 2016-03-25T15:51:02Z MEMBER

Multi-index level drop only works when a dict is provided for a dimension, i.e., da.sel(band_wavenumber='foo') still returns the full pandas.MultiIndex. This would require for indexing.convert_label_indexer to return an updated index also when label is not dict-like, which is not that straightforward I think.

Indeed. This would require an another data structure somewhere keeping track of level names -- and ideally also ensuring that they are always unique (like dimensions). This seems fine to me for now.

There is a potential conflict when providing a dictionary to DataArray.loc, it may be interpreted either as a mapping of dimensions / labels or a mapping of index levels / labels for the 1st dimension. For now, the second option is not handled, e.g., da.loc[{'band': 'foo'}] raises a KeyError.

I agree -- better to require the user to be explicit. I also don't see many use cases for specifying the coordinate value and level name but not the dimension name. What happens if you type da.loc[{'band': 'foo'}, :] or da.loc[{'band': 'foo'}, ...]? In principle these are not ambiguous, but I could only guess at what happens without looking at the code.

 
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