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  • Invisible differences between arrays using IntervalIndex · 2 ✖

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1259615513 https://github.com/pydata/xarray/issues/4579#issuecomment-1259615513 https://api.github.com/repos/pydata/xarray/issues/4579 IC_kwDOAMm_X85LFDUZ benbovy 4160723 2022-09-27T14:45:19Z 2022-09-27T14:46:41Z MEMBER

Perhaps Xarray has been too clever so far regarding how it handles pandas objects passed directly as coordinate data? pandas.MultiIndex objects are handled in a specific way too, which is often hard to deal with.

Expanding on @max-sixty's suggestion, we could:

  • treat all coordinate data as duck arrays, i.e., in the example above handle da1 just like da2 (no more special cases for pandas objects)
  • provide an xarray.indexes.PandasIntervalIndex wrapper, which would inherit from xarray.indexes.PandasIndex with a few addtionnal options and features, e.g., like the ones @dcherian suggests in https://github.com/pydata/xarray/discussions/6783#discussioncomment-3149033
  • build an interval index from an existing coordinate using , e.g., da.set_xindex("x", PandasIntervalIndex, closed="right")
  • figure out how to assign both a coordinate and an index from an existing pandas.IntervalIndex object in a convenient but more explicit way
 
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   Invisible differences between arrays using IntervalIndex 741806260
726281700 https://github.com/pydata/xarray/issues/4579#issuecomment-726281700 https://api.github.com/repos/pydata/xarray/issues/4579 MDEyOklzc3VlQ29tbWVudDcyNjI4MTcwMA== max-sixty 5635139 2020-11-12T19:12:32Z 2020-11-12T19:12:32Z MEMBER

Thanks for the clear issue @gerritholl . I agree — it's confusing if those two look the same.

Currently, one way of discriminating them:

```python In [6]: da1.indexes['x'] Out[6]: IntervalIndex([(0.0, 0.6666666666666666], (0.6666666666666666, 1.3333333333333333], (1.3333333333333333, 2.0]], closed='right', name='x', dtype='interval[float64]')

In [7]: da2.indexes['x'] Out[7]: Index([ (0.0, 0.6666666666666666], (0.6666666666666666, 1.3333333333333333], (1.3333333333333333, 2.0]], dtype='object', name='x') ```

One option is to push the dtype — 'interval[float64] vs object — or the Index type — IntervalIndex vs Index — values into the repr of the array: python In [8]: da1 Out[8]: <xarray.DataArray (x: 3)> array([0, 1, 2]) Coordinates: * x (x) object (0.0, 0.6666666666666666] ... (1.3333333333333333, 2.0]

Could be: python * x (x) interval[float64] (0.0, 0.6666666666666666] ... (1.3333333333333333, 2.0]

What are others thoughts?

And ref https://github.com/pydata/xarray/projects/1

 
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   Invisible differences between arrays using IntervalIndex 741806260

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