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  • johnomotani · 3 ✖

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  • Inconsistency in whether index is created with new dimension coordinate? · 3 ✖

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  • CONTRIBUTOR · 3 ✖
id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
1245036513 https://github.com/pydata/xarray/issues/4417#issuecomment-1245036513 https://api.github.com/repos/pydata/xarray/issues/4417 IC_kwDOAMm_X85KNb_h johnomotani 3958036 2022-09-13T07:54:32Z 2022-09-13T07:54:32Z CONTRIBUTOR

Thanks @benbovy - with explicit methods now to produce the result with or without index, I think we can close this now :smiley:

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  Inconsistency in whether index is created with new dimension coordinate? 698577111
691109135 https://github.com/pydata/xarray/issues/4417#issuecomment-691109135 https://api.github.com/repos/pydata/xarray/issues/4417 MDEyOklzc3VlQ29tbWVudDY5MTEwOTEzNQ== johnomotani 3958036 2020-09-11T13:54:01Z 2020-09-11T13:54:01Z CONTRIBUTOR

I think I was trying to fix this in #4108

:+1: On a quick glance it looks to me like #4108 would fix my issue here.

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  Inconsistency in whether index is created with new dimension coordinate? 698577111
691045790 https://github.com/pydata/xarray/issues/4417#issuecomment-691045790 https://api.github.com/repos/pydata/xarray/issues/4417 MDEyOklzc3VlQ29tbWVudDY5MTA0NTc5MA== johnomotani 3958036 2020-09-11T11:45:40Z 2020-09-11T11:45:40Z CONTRIBUTOR

I haven't managed to get set_index to line up the results. This: ``` import numpy as np import xarray as xr

ds = xr.Dataset() ds['a'] = ('x', np.linspace(0,1)) ds['b'] = ('x', np.linspace(3,4)) ds = ds.rename(b='x') ds = ds.set_coords('x') ds = ds.set_index(x='x')

print(ds) print('indexes', ds.indexes) `` Produces a Dataset that now has an indexx, but nox` coordinate. The 'assignment of 1D variable' version produces both a coordinate and an index.

One suggested solution in #2461 was to use swap_dims(), and the following does produce the desired result ``` import numpy as np import xarray as xr

ds = xr.Dataset() ds['a'] = ('x', np.linspace(0,1)) ds['b'] = ('x', np.linspace(3,4)) ds = ds.swap_dims({'x': 'b'}) ds = ds.rename(b='x')

print(ds) print('indexes', ds.indexes) `` But (1) having to useswap_dims()to create a coordinate seems bizarre, (2) I think it's a bit ugly to get rid of thexdimension withswap_dims(), and then have to rename the new dimension back tox, when what I wanted was to add a coordinate tox`.

I found the behaviour confusing because I wasn't aware of the index variables at all... I create a coordinate with set_coords(); then do a bunch of manipulations involving slicing pieces out of the Dataset and re-combining them with concat (at least I think this is the relevant part of my code...); finally test the result by taking slices again (with isel()) of the result and comparing them to slices of the original Dataset with xarray.testing.assert_identical(), which failed because of the missing indexes. I guess somewhere in the manipulations I did, some operation created the coordinate in a new Dataset object by assignment, at which point it generated an index for the coordinate too.

I may well be missing other context that makes this an undesirable thing to do, but for my use-case at least, I think it would make more sense if set_coords() created an index if the coordinate is a dimension coordinate (or whatever the actual criterion is for assignment of a 1d variable to create an index).

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  Inconsistency in whether index is created with new dimension coordinate? 698577111

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