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That looks correct. Let me try and revert back to you

On Wed, Aug 14, 2019, 16:44 Maximilian Roos notifications@github.com wrote:

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 https://github.com/fjanoos ?)

@fjanoos https://github.com/fjanoos can you confirm this is what you're looking for?

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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As of now, a simple workaround would be to do these tasks in pandas and switch back and forth.

A couple of years ago - before pandas had pd.merge_asof - I had implemented a version of this logic in numba when working with numpy arrays. It was blazingly fast - and if there is interest I can try to dig it up ? I would need some help making it work for xarrays and publishing it into the master branch.

On Wed, Aug 14, 2019, 14:12 Maximilian Roos notifications@github.com wrote:

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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