issue_comments

Thanks again @shoyer for your reviews and comments!

Merging this PR broke the tests added in #881. I fixed that in #903.

Thanks for your second review @shoyer ! It actually helped me to better understand the indexing logic of pandas (never too late)!

I made some updates according to your comments.

I think we're getting closer to a working feature!

Noted the difference in documentation.

Hope that the whole "multi-level indexing" section is clear enough, not sure if it's proper English.

That behavior looks good enough to me too! It is indeed already quite flexible :-).

Do you think that we really need to handle data.loc['a', 0] if data is 2 or higher dimensional? We would also have to handle the case data.loc[{'one': 'a', 'two': 0}]... Having to explicitly write data.loc[('a', 0), :] or data.loc[('a', 0), ...] seems fine to me (at least for now).

```

index = pd.MultiIndex.from_product((list('abc'), range(3))) index.names = ('one', 'two') data = xr.DataArray(range(9), [('x', index)]) data <xarray.DataArray (x: 9)> array([0, 1, 2, 3, 4, 5, 6, 7, 8]) Coordinates: * x (x) object ('a', 0) ('a', 1) ('a', 2) ('b', 0) ('b', 1) ... ```

Example using a tuple of scalar/tuple/list/slice labels for each level:

```

data.sel(x=('a', (0, 1))) <xarray.DataArray (x: 2)> array([0, 1]) Coordinates: * x (x) object ('a', 0) ('a', 1) ```

Example using a list of several combinations of multi-index labels:

```

data.sel(x=[('a', 0), ('b', 1), ('c', 1)]) <xarray.DataArray (x: 3)> array([0, 4, 7]) Coordinates: * x (x) object ('a', 0) ('b', 1) ('c', 1) ```

All the following examples raise an error:

```

data.sel(x=(('a', 0), ('b', 1), ('c', 1))) data.sel(x=['a', (0, 1)]) data.sel(x=[['a', 0], ['b', 1], ['c', 1]]) ```

Note that after verification, pandas has the same behavior for all of the examples shown here.

Thanks for the review @shoyer !

I made some updates according to your comments.

A possible remaining issue is that using a tuple of tuples, lists or slices exactly behaves like pandas (it calls get_locs) while a nested list can still be used to select couples of multi-index labels (it calls get_indexer). Should we allow only one of two behaviors, or keep both (and mention it in the docs)?

I finally managed to add some tests and docs.

Two more comments: - Index replacement: if the multi-index has unnamed levels, the chosen name for the replaced index (if any) is <old_dim>_unnamed_level (old_dim is the name of the replaced multi-index dimension). Not sure whether or not it is really necessary to track the index level number. Single indexes returned by get_loc_level don't keep that information. Anyway, it is always better to set explicit names for the index levels. - Indexing on multi-index now also works with nested tuples (#850).

@shoyer I think that it is ready for review. I successfully run the tests on my local branch. Currently, CI tests seem broken for some reason I don't know.

I hope to have some time next week to work again on this PR.

@tippetts You can see in #719 a few comments about saving/reading xarray data objects with multi-index to/from netCDF. I also looking forward to see this feature implemented - actually I need it for another project that uses xarray - so maybe I'll find some time in the next couple of weeks to start a new PR on this.

Unless you see any other issues, I think that this feature doesn't need more development for now.

I'll be back next week to finish this PR (write some tests and doc).

After refactoring a bit, da.loc[{'band': 'foo'}, :] and da.loc[{'band': 'foo'}, ...] now work as expected.

da.loc[{'band': 'foo'}] still raises a KeyError. Although it is possible to catch this exception, it still remains ambiguous so it is probably better to do nothing.

I followed your suggestions.

Two more comments (not critical issues I think) : - 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. - 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.

In summary, da.loc[{'band_wavenumber': {'band': 'foo'}}] returns a DataArray with updated index and coordinate / dimension name, da.loc['foo'] returns a DataArray with the full multi-index, and da.loc[{'band': 'foo'}] raises a KeyError. Three different results for the same operation, although I think that the first one is more explicit and better.