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  • max-sixty · 2 ✖

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  • merge_asof functionality · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
521411228 https://github.com/pydata/xarray/issues/3218#issuecomment-521411228 https://api.github.com/repos/pydata/xarray/issues/3218 MDEyOklzc3VlQ29tbWVudDUyMTQxMTIyOA== max-sixty 5635139 2019-08-14T20:43:57Z 2019-08-14T20:43:57Z MEMBER

How is merge_asof different from using reindex with method='pad'?

Yes this is right! Mea culpa. We can already use the pandas reindexing for the 1D case (which should cover your case @fjanoos ?)

@fjanoos can you confirm this is what you're looking for?

```python

In [4]: da=xr.DataArray(list('abcdefghil'), dims=['x'],coords=dict(x=range(10)))

In [8]: da.reindex(x=[0,2.5,2.6,2.7,5,6.2], method='nearest') Out[8]: <xarray.DataArray (x: 6)> array(['a', 'd', 'd', 'd', 'f', 'g'], dtype='<U1') Coordinates: * x (x) float64 0.0 2.5 2.6 2.7 5.0 6.2

```

 
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   merge_asof functionality 480786385
521357519 https://github.com/pydata/xarray/issues/3218#issuecomment-521357519 https://api.github.com/repos/pydata/xarray/issues/3218 MDEyOklzc3VlQ29tbWVudDUyMTM1NzUxOQ== max-sixty 5635139 2019-08-14T18:12:43Z 2019-08-14T18:12:43Z MEMBER

I think this would be good. It would need to be implemented outside of python (cython / numba / etc) given the performance requirements. I'm not sure whether we could borrow the pandas functionality and apply it to multi-dimensional arrays.

Assuming we'd need to write our own, xarray doesn't have any cython dependencies, so I think it would be best in a separate and optional package. These could go in numbagg. It's non-trivial work, so someone would have to have a strong need for it.

 
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   merge_asof functionality 480786385

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