issues: 143264649

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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
143264649 MDExOlB1bGxSZXF1ZXN0NjQwNDI5ODk= 802 Multi-index indexing 4160723 closed 0     22 2016-03-24T14:39:38Z 2016-07-19T10:48:56Z 2016-07-19T01:15:42Z MEMBER   0 pydata/xarray/pulls/802

Follows #767.

This is incomplete (it still needs some tests and documentation updates), but it is working for both Dataset and DataArray objects. I also don't know if it is fully compatible with lazy indexing (Dask).

Using the example from #767:

In [4]: da.sel(band_wavenumber={'band': 'foo'}) Out[4]: <xarray.DataArray (wavenumber: 2)> array([ 0.00017, 0.00014]) Coordinates: * wavenumber (wavenumber) float64 4.05e+03 4.05e+03

As shown in this example, similarily to pandas, it automatically renames the dimension and assigns a new coordinate when the selection doesn't return a pd.MultiIndex (here it returns a pd.FloatIndex).

In some cases this behavior may be unwanted (??), so I added a drop_level keyword argument (if False it keeps the multi-index and doesn't change the dimension/coordinate names):

In [5]: da.sel(band_wavenumber={'band': 'foo'}, drop_level=False) Out[5]: <xarray.DataArray (band_wavenumber: 2)> array([ 0.00017, 0.00014]) Coordinates: * band_wavenumber (band_wavenumber) object ('foo', 4050.2) ('foo', 4050.3)

Note that it also works with DataArray.loc, but (for now) in that case it always returns the multi-index:

In [6]: da.loc[{'band_wavenumber': {'band': 'foo'}}] Out[6]: <xarray.DataArray (band_wavenumber: 2)> array([ 0.00017, 0.00014]) Coordinates: * band_wavenumber (band_wavenumber) object ('foo', 4050.2) ('foo', 4050.3)

This is however inconsistent with Dataset.sel and Dataset.loc that both apply drop_level=True by default, due to their different implementation. Two solutions: (1) make DataArray.loc apply drop_level by default, or (2) use drop_level=False by default everywhere.

 
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