issue_comments: 362902669

@gajomi

Sorry for my late response and thank you for the proposal.

But aside from my previous proposal, I was thinking whether such aggregation methods (including argmin) should propagate the coordinate. For example, as you pointed out, in theory, we may be able to track x-coordinate at the argmin index after da.argmin(dim='x'). But it is not reasonable for da.mean(dim='x'). It may be reasonable for da.max(dim='x') but not for da.median(dim='x').

Such specific rules may be confusing and bring additional complexity. I think the rule we do not track coordinates after aggregations would be much simpler and easier to understand.

If we adopt the above rule, I think the argmin would give just an array of indices, ```python In [1]: import xarray as xr ...: da = xr.DataArray([[0, 3, 2], [2, 1, 4]], dims=['x', 'y'], ...: coords={'x': [1, 2], 'y': ['a', 'b', 'c']}) ...:

In [4]: da.argmin(dim='x') Out[4]: <xarray.DataArray (y: 3)> array([0, 1, 0]) Coordinates: * y (y) <U1 'a' 'b' 'c'

In [3]: da.isel(x=da.argmin(dim='x')) Out[3]: <xarray.DataArray (y: 3)> array([0, 1, 2]) Coordinates: x (y) int64 1 2 1 * y (y) <U1 'a' 'b' 'c'

```

I think your logic would be useful even we do not track the coordinate.

I would appreciate any feedback.