DataArray.groupby drops empty coordinates #11188
Description
What happened?
When I run groupby on an empty DataArray, empty coordinates are dropped.
This is is xarray version 2024.10.0.
import numpy as np
import xarray as xr
data = xr.DataArray(np.empty((0, 2)), dims=['x', 'y'], coords={'x': [], 'y': [1, 1]})
print(data.groupby('y').sum())
<xarray.DataArray (x: 0, y: 1)> Size: 0B
array([], shape=(0, 1), dtype=float64)
Coordinates:
* y (y) int64 8B 1
Dimensions without coordinates: x
As you see, x coordinate is not longer present.
What did you expect to happen?
I expect x coordinate to be preserved.
E.g., here is the optuput of the same snippet in xarray==2024.2.0:
<xarray.DataArray (x: 0, y: 1)> Size: 0B
array([], shape=(0, 1), dtype=float64)
Coordinates:
* x (x) float64 0B
* y (y) int64 8B 1
Minimal Complete Verifiable Example
import xarray as xr
xr.show_versions()
# your reproducer code ...
import numpy as np
import xarray as xr
data = xr.DataArray(np.empty((0, 2)), dims=['x', 'y'], coords={'x': [], 'y': [1, 1]})
assert 'x' in data.groupby('y').sum().coordsSteps to reproduce
No response
MVCE confirmation
- Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- Complete example — the example is self-contained, including all data and the text of any traceback.
- Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
- New issue — a search of GitHub Issues suggests this is not a duplicate.
- Recent environment — the issue occurs with the latest version of xarray and its dependencies.
Relevant log output
Anything else we need to know?
This issue is caused by the change introduced to DataArray._replace_maybe_drop_dims function in scope of #5361.
In this case self.dims == ('x', 'y') and variable.dims == ('y', 'x') so the condition set(self.dims) == set(variable.dims) is True.
However, the next line new_sizes = dict(zip(self.dims, variable.shape, strict=True)) assumes that the dims order is the same, otherwise it does not have any sense.
Environment
Details
INSTALLED VERSIONS
commit: None
python: 3.12.7 (main, Oct 1 2024, 02:05:46) [GCC 13.3.0]
python-bits: 64
OS: Linux
OS-release: 4.18.0-553.34.1.el8_10.x86_64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.5
libnetcdf: 4.9.2
xarray: 2024.10.0
pandas: 2.2.3
numpy: 1.26.4
scipy: 1.14.1
netCDF4: 1.7.1.post2
pydap: None
h5netcdf: 1.4.0
h5py: 3.12.1
zarr: 3.1.1
cftime: 1.6.4
nc_time_axis: 1.4.1
iris: None
bottleneck: 1.4.0
dask: 2025.11.0
distributed: 2025.11.0
matplotlib: 3.9.2
cartopy: None
seaborn: 0.13.2
numbagg: 0.8.2
fsspec: 2024.3.0
cupy: 13.3.0
pint: None
sparse: 0.15.4
flox: None
numpy_groupies: None
setuptools: 75.1.1.post0
pip: 24.0
conda: None
pytest: 8.3.3
mypy: 1.11.2
IPython: 8.29.0
sphinx: 7.4.7