issue_comments

@jhamman Yes, indeed. Sorry to spam this old issue. I misread this one - #757 is what'm seeing.

@chris-b1 - I think you're seeing the issue described in #757.

I am seeing something similar, but maybe this is another issue (I'm on 0.10.0rc2)?

I do get a sorted string coordinate after a groupby...

My scenario is, that I have a dataset with a coord like this:

<xarray.DataArray 'pft' (pft: 13)> array(['TeBE_tm', 'TeBE_itm', 'TeBE_itscl', 'TeBE_tscl', 'TeBS_tm', 'TeBS_itm', 'TeE_s', 'TeR_s', 'TeNE', 'BBS_itm', 'BE_s', 'BS_s', 'C3G'], dtype='|S10') Coordinates: * pft (pft) |S10 'TeBE_tm' 'TeBE_itm' 'TeBE_itscl' 'TeBE_tscl' ... Then I create a new coordinate that I use to aggregate:

``` pfts = ds.coords['pft'].values.tolist() pfts_simplified = [remove(x) for x in pfts]

ds2['pft_agg'] = xr.full_like(ds['pft'], 0) ds2['pft_agg'][:] = pfts_simplified ds2_agg = ds2.groupby('pft_agg').sum(dim='pft', skipna=False) result = ds2_agg.rename({'pft_agg': 'pft'}) ```

Then in the end I have: ``` <xarray.DataArray 'pft' (pft: 8)> array(['BBS', 'B_s', 'C3G', 'TeBE', 'TeBE_scl', 'TeBS', 'TeNE', 'Te_s'], dtype=object) Coordinates: * pft (pft) object 'BBS' 'B_s' 'C3G' 'TeBE' 'TeBE_scl' 'TeBS' 'TeNE' ...

```

Am I missing something?

Closed via https://github.com/pydata/xarray/pull/1049

So the tricky part here is that it's not obvious what is breaking here. One clue is that reducing doesn't seem to be necessary -- I can reproduce this just with applying an identity transform:

``` In [6]: identity = lambda x: x

In [7]: ds.groupby('t').apply(identity) Out[7]: <xarray.Dataset> Dimensions: (t: 10) Coordinates: * t (t) int64 0 1 2 3 4 5 6 7 8 9 Data variables: a (t) int64 0 1 2 3 4 5 6 7 8 9 c (t) int64 0 1 2 3 4 5 6 7 8 9 b (t) int64 0 1 2 3 4 5 6 7 8 9 ```

Actually, it looks like it's probably a concat bug:

``` In [17]: gb = ds.groupby('t')

In [18]: grouped = [v for _, v in gb]

In [20]: [list(g.data_vars) for g in grouped] Out[20]: [['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c'], ['a', 'b', 'c']]

In [21]: xr.concat(grouped, dim='t') Out[21]: <xarray.Dataset> Dimensions: (t: 10) Coordinates: * t (t) int64 0 1 2 3 4 5 6 7 8 9 Data variables: a (t) int64 0 1 2 3 4 5 6 7 8 9 c (t) int64 0 1 2 3 4 5 6 7 8 9 b (t) int64 0 1 2 3 4 5 6 7 8 9 ```

@shoyer I'd be happy to provide a fix if you want. Could you give a short pointer as to where the logic is implemented?

Thanks @shoyer , here's a mwe:

``` python import xarray as xr import numpy as np ds = xr.Dataset() for vn in ['a', 'b', 'c']: ds[vn] = xr.DataArray(np.arange(10), dims=['t']) ds.groupby('t').mean()

<xarray.Dataset> Dimensions: (t: 10) Coordinates: * t (t) int64 0 1 2 3 4 5 6 7 8 9 Data variables: a (t) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 c (t) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 b (t) float64 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 ```

This is probably a bug. Usually, we're pretty careful to always use OrderedDict internally for exactly this reason. Can you give a reproducible example?