slice using non-index coordinates

[crusaderky]

It should be relatively straightforward to allow slicing on coordinates that are not backed by an IndexVariable, or in other words coordinates that are on a dimension with a different name, as long as they are 1-dimensional (unsure about the multidimensional case).

E.g. given this array:

a = xarray.DataArray(
    [10, 20, 30],
    dims=['country'],
    coords={
        'country': ['US', 'Germany', 'France'],
        'currency': ('country', ['USD', 'EUR', 'EUR'])
    })

<xarray.DataArray (country: 3)>
array([10, 20, 30])
Coordinates:
  * country   (country) <U7 'US' 'Germany' 'France'
    currency  (country) <U3 'USD' 'EUR' 'EUR'

This is currently not possible:

a.sel(currency='EUR')


ValueError: dimensions or multi-index levels ['currency'] do not exist

It should be interpreted as a shorthand for:

a.sel(country=a.currency == 'EUR')

<xarray.DataArray (country: 2)>
array([20, 30])
Coordinates:
  * country   (country) <U7 'Germany' 'France'
    currency  (country) <U3 'EUR' 'EUR'

[max-sixty]

I agree this is harder that it should be.

Here's one way:

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?

[shoyer]

we're discussed this before: #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.

[shoyer]

This has some connections to the broader indexes refactor envisioned in #1603.

[max-sixty]

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

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

[shoyer]

@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() (#1268).

[stale]

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[crusaderky]

Still relevant