Description
What happened?
When grouping by a dimension with no duplicate coordinates, reduction operations fail. Reductions don't fail if there are duplicate coordinates, or if an empty tuple is provided in the reduction operation.
What did you expect to happen?
Reductions happening after a groupby operation which leads to each group being a singleton should not fail. By default it should mimic the same behaviour as when an empty tuple is passed to the reduction operation.
Minimal Complete Verifiable Example
import numpy as np
import xarray as xr
a = xr.DataArray(range(3), coords={'a': [0, 1, 1]})
b = xr.DataArray(range(3), coords={'a': [0, 1, 2]})
# Works completely fine
a.groupby('a').sum()
# What is expected from the below:
b.groupby('a').sum(tuple())
# Raises value error
b.groupby('a').sum()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
ValueError Traceback (most recent call last)
File /private/tmp/foo.py:11, in <module>
8 a.groupby('a').sum()
10 # Raises value error
---> 11 b.groupby('a').sum()
13 # What is expected from the above:
14 b.groupby('a').sum(tuple())
File ~/miniconda3/lib/python3.10/site-packages/xarray/core/_aggregations.py:6204, in DataArrayGroupByAggregations.sum(self, dim, skipna, min_count, keep_attrs, **kwargs)
6194 return self._flox_reduce(
6195 func="sum",
6196 dim=dim,
(...)
6201 **kwargs,
6202 )
6203 else:
-> 6204 return self.reduce(
6205 duck_array_ops.sum,
6206 dim=dim,
6207 skipna=skipna,
6208 min_count=min_count,
6209 keep_attrs=keep_attrs,
6210 **kwargs,
6211 )
File ~/miniconda3/lib/python3.10/site-packages/xarray/core/groupby.py:1478, in DataArrayGroupByBase.reduce(self, func, dim, axis, keep_attrs, keepdims, shortcut, **kwargs)
1468 def reduce_array(ar: DataArray) -> DataArray:
1469 return ar.reduce(
1470 func=func,
1471 dim=dim,
(...)
1475 **kwargs,
1476 )
-> 1478 check_reduce_dims(dim, self.dims)
1480 return self.map(reduce_array, shortcut=shortcut)
File ~/miniconda3/lib/python3.10/site-packages/xarray/core/groupby.py:69, in check_reduce_dims(reduce_dims, dimensions)
67 reduce_dims = [reduce_dims]
68 if any(dim not in dimensions for dim in reduce_dims):
---> 69 raise ValueError(
70 f"cannot reduce over dimensions {reduce_dims!r}. expected either '...' "
71 f"to reduce over all dimensions or one or more of {dimensions!r}."
72 )
ValueError: cannot reduce over dimensions ['a']. expected either '...' to reduce over all dimensions or one or more of ().Anything else we need to know?
No response
Environment
INSTALLED VERSIONS
commit: None
python: 3.10.5 | packaged by conda-forge | (main, Jun 14 2022, 07:03:09) [Clang 13.0.1 ]
python-bits: 64
OS: Darwin
OS-release: 23.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: ('en_GB', 'UTF-8')
libhdf5: 1.12.1
libnetcdf: None
xarray: 2023.6.0
pandas: 1.5.3
numpy: 1.23.5
scipy: 1.8.1
netCDF4: None
pydap: None
h5netcdf: None
h5py: 3.6.0
Nio: None
zarr: None
cftime: None
nc_time_axis: None
PseudoNetCDF: None
iris: None
bottleneck: 1.3.5
dask: 2022.8.0
distributed: 2022.8.0
matplotlib: 3.5.2
cartopy: None
seaborn: None
numbagg: None
fsspec: 2023.4.0
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 67.8.0
pip: 21.3.1
conda: 4.13.0
pytest: None
mypy: None
IPython: 8.4.0
sphinx: None