id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1236174701,I_kwDOAMm_X85Jrodt,6610,"Update GroupBy constructor for grouping by multiple variables, dask arrays",2448579,open,0,,,6,2022-05-15T03:17:54Z,2023-04-26T16:06:17Z,,MEMBER,,,,"### What is your issue?

`flox` supports grouping by multiple variables (would fix #324, #1056)  and grouping by dask variables (would fix #2852).

To enable this in GroupBy we need to update the constructor's signature to
1. Accept multiple ""by"" variables.
2. Accept ""expected group labels"" for grouping by dask variables (like `bins` for `groupby_bins` which already supports grouping by dask variables). This lets us construct the output coordinate without evaluating the dask variable.
3. We may also want to simultaneously group by a categorical variable (season) and bin by a continuous variable (air temperature). So we also need a way to indicate whether the ""expected group labels"" are ""bin edges"" or categories.

-----
The signature in flox is (may be errors!)
``` python
xarray_reduce(
    obj: Dataset | DataArray,
    *by: DataArray | str,
    func: str | Aggregation,
    expected_groups: Sequence | np.ndarray | None = None,
    isbin: bool | Sequence[bool] = False,
    ...
)
```

You would calculate that last example using flox as
```  python
xarray_reduce(
   ds,
    ""season"", ""air_temperature"", 
    expected_groups=[None, np.arange(21, 30, 1)],
    isbin=[False, True],
    ...
)
```

The use of `expected_groups` and `isbin` seems ugly to me (the names could also be better!)

-------

I propose we update [groupby's signature](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.groupby.html) to 
1. change `group: DataArray | str` to `group: DataArray | str | Iterable[str] | Iterable[DataArray]`
2. We could add a top-level `xr.Bins` object that wraps bin edges + any kwargs to be passed to `pandas.cut`. Note our current [groupby_bins](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.groupby_bins.html) signature has a bunch of kwargs passed directly to pandas.cut.
3. Finally add `groups: None | ArrayLike | xarray.Bins | Iterable[None | ArrayLike | xarray.Bins]` to pass the ""expected group labels"". 
    1. If `None`, then groups will be auto-detected from non-dask `group` arrays (if `None` for a dask `group`, then raise error).
    1. If `xarray.Bins` indicates binning by the appropriate variables
    1. If `ArrayLike` treat as categorical.
    1. `groups` is a little too similar to `group` so we should choose a better name.
    1. The ordering of `ArrayLike` would let us fix #757 (pass the seasons in the order you want them in the output)


So then that example becomes
``` python
ds.groupby(
    [""season"", ""air_temperature""], # season is numpy, air_temperature is dask
    groups=[None, xr.Bins(np.arange(21, 30, 1), closed=""right"")],
)
```

Thoughts?

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