@gewitterblitz there is a kdtree-based index example in #7041 that works with multi-dimensional coordinates. You could also have a look at https://xoak.readthedocs.io/en/latest/ (it doesn't use Xarray indexes - soon hopefully - so the current API is via Xarray accessors).

EDIT: seeing your previous https://github.com/pydata/xarray/issues/2028#issuecomment-921926536, not sure how you could use slices for label selection using those indexes as I don't think the wrapped scipy / sklearn kdtree objects support range queries. Other spatial indexes may support it (e.g., there's an example in https://github.com/martinfleis/xvec of selecting points using a shapely.box, although currently it only supports 1-d geometry coordinates).

@aberges-grd If your non-index coordinate supports it (I guess it does?), you could assign a default index to the coordinate with set_xindex and then use slices for selection like any other (dimension) coordinate backed by a pandas index.

With the last release v2022.09.0, this is now possible via .set_xindex():

```python a = a.set_xindex("currency")

a.sel(currency="EUR")

<xarray.DataArray (country: 2)>

array([20, 30])

Coordinates:

* country (country) <U7 'Germany' 'France'

* currency (country) <U3 'EUR' 'EUR'

```

Closed in #6971 (although set_xindex still needs to be documented in the User Guide).

Hi @covertg, as soon as there is public API for setting non-dimension or custom indexes it should be ready. See #6849, which is actually already implemented in the scipy22 branch (+ an example here). I plan to re-submit it as a proper PR within the few coming weeks.

xoak should work here: https://xoak.readthedocs.io/en/latest/

Here's an example with ocean model output: https://pop-tools.readthedocs.io/en/latest/examples/xoak-example.html .

If you can wait a while, this will all work better once #5692 is merged.

3925 would fix this for 1D non-dim coords. We should update the docs (ping @TomNicholas)

Still relevant

@maxim-lian Probably. Or you could make the pandas.Index explicitly, e.g., da.sel(a=da.c.to_index().get_indexer(['x', 'y'])).

We should really add DataArray.isin() (https://github.com/pydata/xarray/issues/1268).

What's the easiest way to select on multiple values? Is it really this:

```python In [63]: da = xr.DataArray(np.random.rand(3,2), dims=list('ab'), coords={'c':(('a',),list('xyz'))})

In [64]: da.sel(a=(np.isin(da.c, list('xy')))) Out[64]: <xarray.DataArray (a: 2, b: 2)> array([[0.383989, 0.174317], [0.698948, 0.815993]]) Coordinates: c (a) <U1 'x' 'y' Dimensions without coordinates: a, b ```

This has some connections to the broader indexes refactor envisioned in https://github.com/pydata/xarray/issues/1603.

we're discussed this before: https://github.com/pydata/xarray/issues/934

I agree that this would be nice to support in theory. The challenge is that we would need to create (and then possibly throw away?) a pandas.Index do to the actual indexing, or use a numpy search function like np.isin(). Neither of these are very efficient.

Conceptually, I think it makes sense to support indexing on arbitrary variables, which is simply more expensive if an index is not already set. Dimension coordinates would not be special except that they have indexes created automatically.

I agree this is harder that it should be.

Here's one way: ```python

In [28]: a.where(a.currency=='EUR', drop=True) Out[28]: <xarray.DataArray (country: 2)> array([20., 30.]) Coordinates: * country (country) <U7 'Germany' 'France' currency (country) <U3 'EUR' 'EUR' ```

I'm not sure whether .sel should work for non-IndexVariables - thoughts?