html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue https://github.com/pydata/xarray/issues/3218#issuecomment-884382105,https://api.github.com/repos/pydata/xarray/issues/3218,884382105,IC_kwDOAMm_X840tpmZ,26384082,2021-07-21T18:00:51Z,2021-07-21T18:00:51Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,480786385 https://github.com/pydata/xarray/issues/3218#issuecomment-521413970,https://api.github.com/repos/pydata/xarray/issues/3218,521413970,MDEyOklzc3VlQ29tbWVudDUyMTQxMzk3MA==,923438,2019-08-14T20:52:06Z,2019-08-14T20:52:06Z,NONE,"That looks correct. Let me try and revert back to you On Wed, Aug 14, 2019, 16:44 Maximilian Roos 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 > ?) > > @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]: > array(['a', 'd', 'd', 'd', 'f', 'g'], dtype=' Coordinates: > * x (x) float64 0.0 2.5 2.6 2.7 5.0 6.2 > > — > You are receiving this because you were mentioned. > Reply to this email directly, view it on GitHub > , > or mute the thread > > . > ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,480786385 https://github.com/pydata/xarray/issues/3218#issuecomment-521411228,https://api.github.com/repos/pydata/xarray/issues/3218,521411228,MDEyOklzc3VlQ29tbWVudDUyMTQxMTIyOA==,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]: array(['a', 'd', 'd', 'd', 'f', 'g'], dtype=' 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. > > — > You are receiving this because you authored the thread. > Reply to this email directly, view it on GitHub > , > or mute the thread > > . > ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,480786385 https://github.com/pydata/xarray/issues/3218#issuecomment-521387096,https://api.github.com/repos/pydata/xarray/issues/3218,521387096,MDEyOklzc3VlQ29tbWVudDUyMTM4NzA5Ng==,1217238,2019-08-14T19:34:22Z,2019-08-14T19:34:22Z,MEMBER,How is merge_asof different from using reindex with method='pad'?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,480786385 https://github.com/pydata/xarray/issues/3218#issuecomment-521357519,https://api.github.com/repos/pydata/xarray/issues/3218,521357519,MDEyOklzc3VlQ29tbWVudDUyMTM1NzUxOQ==,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.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,480786385