issue_comments

We voted to move forward with this API: python data.groupby({ "x0": xr.BinGrouper(bins=pd.IntervalIndex.from_breaks(coords["x_vertices"])), # binning "y": xr.UniqueGrouper(labels=["a", "b", "c"]), # categorical, data.y is dask-backed "time": xr.TimeResampleGrouper(freq="MS") }, )

We won't break backwards-compatibility for da.groupby(other_data_array) but for any complicated use-cases with Grouper the user must add the by variable to the xarray object, and refer to it by name in the dictionary as above,

Here's a question.

In #7561, I implement Grouper objects that don't have any information of the variable we're grouping by. So the future API would be:

python data.groupby({ "x0": xr.BinGrouper(bins=pd.IntervalIndex.from_breaks(coords["x_vertices"])), # binning "y": xr.UniqueGrouper(labels=["a", "b", "c"]), # categorical, data.y is dask-backed "time": xr.TimeResampleGrouper(freq="MS") }, )

Does this look OK or do we want to support passing the DataArray or variable name as a by kwarg:
python xr.BinGrouper(by="x0", bins=pd.IntervalIndex.from_breaks(coords["x_vertices"]))

This syntax would support passing DataArray in by so xr.UniqueGrouper(by=data.y) for example. Is that an important usecase to support? In #7561, I create new ResolvedGrouper objects that do contain by as a DataArray always, so it's really a question of exposing that to the user.

PS: Pandas has a key kwarg for a column name. So following that would mean

python data.groupby([ xr.BinGrouper("x0", bins=pd.IntervalIndex.from_breaks(coords["x_vertices"])), # binning xr.UniqueGrouper("y", labels=["a", "b", "c"]), # categorical, data.y is dask-backed xr.TimeResampleGrouper("time", freq="MS") ], )

I also like the idea of creating specific Grouper objects for different types of selection, e.g., UniqueGrouper (the default), BinGrouper, TimeResampleGrouper, etc.

Using xr.Grouper has the advantage that you don't have to start guessing about whether or not the user wanted some complicated behaviour (especially if their input is slightly wrong somehow and you have to raise an informative error). Simple defaults would get left as is and complex use cases can be explicit and opt-in.

In https://github.com/xarray-contrib/flox/issues/191 @keewis proposes a much nicer API for multiple variables:

python data.groupby( xr.Grouper(by="x", bins=pd.IntervalIndex.from_breaks(coords["x_vertices"])), # binning xr.Grouper(by=data.y, labels=["a", "b", "c"]), # categorical, data.y is dask-backed xr.Grouper(by="time", freq="MS"), # resample )

Note pd.Grouper uses key instead of by so that's a possibility too.

I'm getting errors with multi-indexes and flox. Is this expected and related to this issue, or should I open a separate issue?

```python import numpy as np

import xarray as xr

ds = xr.Dataset( dict(a=(("z",), np.ones(10))), coords=dict(b=(("z"), np.arange(2).repeat(5)), c=(("z"), np.arange(5).repeat(2))), ).set_index(bc=["b", "c"]) grouped = ds.groupby("bc")

with xr.set_options(use_flox=False): grouped.sum() # OK

with xr.set_options(use_flox=True): grouped.sum() # Error Traceback (most recent call last): File "/Users/mattia/MyGit/test.py", line 15, in <module> grouped.sum() File "/Users/mattia/MyGit/xarray/xarray/core/_reductions.py", line 2763, in sum return self._flox_reduce( File "/Users/mattia/MyGit/xarray/xarray/core/groupby.py", line 661, in _flox_reduce result = xarray_reduce( File "/Users/mattia/mambaforge/envs/sarsen_dev/lib/python3.10/site-packages/flox/xarray.py", line 373, in xarray_reduce actual[k] = v.expand_dims(missing_group_dims) File "/Users/mattia/MyGit/xarray/xarray/core/dataset.py", line 1427, in setitem self.update({key: value}) File "/Users/mattia/MyGit/xarray/xarray/core/dataset.py", line 4432, in update merge_result = dataset_update_method(self, other) File "/Users/mattia/MyGit/xarray/xarray/core/merge.py", line 1070, in dataset_update_method return merge_core( File "/Users/mattia/MyGit/xarray/xarray/core/merge.py", line 722, in merge_core aligned = deep_align( File "/Users/mattia/MyGit/xarray/xarray/core/alignment.py", line 824, in deep_align aligned = align( File "/Users/mattia/MyGit/xarray/xarray/core/alignment.py", line 761, in align aligner.align() File "/Users/mattia/MyGit/xarray/xarray/core/alignment.py", line 550, in align self.assert_unindexed_dim_sizes_equal() File "/Users/mattia/MyGit/xarray/xarray/core/alignment.py", line 450, in assert_unindexed_dim_sizes_equal raise ValueError( ValueError: cannot reindex or align along dimension 'bc' because of conflicting dimension sizes: {10, 6} (note: an index is found along that dimension with size=10) ```