GroupBy(multiple strings)

doc/user-guide/groupby.rst

@@ -305,6 +305,12 @@ Use grouper objects to group by multiple dimensions:
 
     from xarray.groupers import UniqueGrouper
 
+    da.groupby(["lat", "lon"]).sum()
+
+The above is sugar for using ``UniqueGrouper`` objects directly:
+
+.. ipython:: python
+
     da.groupby(lat=UniqueGrouper(), lon=UniqueGrouper()).sum()
 
 

xarray/core/dataarray.py

@@ -102,6 +102,7 @@
         Dims,
         ErrorOptions,
         ErrorOptionsWithWarn,
+        GroupInput,
         InterpOptions,
         PadModeOptions,
         PadReflectOptions,
@@ -6706,9 +6707,7 @@ def interp_calendar(
     @_deprecate_positional_args("v2024.07.0")
     def groupby(
         self,
-        group: (
-            Hashable | DataArray | IndexVariable | Mapping[Any, Grouper] | None
-        ) = None,
+        group: GroupInput = None,
         *,
         squeeze: Literal[False] = False,
         restore_coord_dims: bool = False,
@@ -6718,7 +6717,7 @@ def groupby(
 
         Parameters
         ----------
-        group : Hashable or DataArray or IndexVariable or mapping of Hashable to Grouper
+        group : str or DataArray or IndexVariable or sequence of hashable or mapping of hashable to Grouper
             Array whose unique values should be used to group this array. If a
             Hashable, must be the name of a coordinate contained in this dataarray. If a dictionary,
             must map an existing variable name to a :py:class:`Grouper` instance.
@@ -6769,6 +6768,48 @@ def groupby(
         Coordinates:
           * dayofyear  (dayofyear) int64 3kB 1 2 3 4 5 6 7 ... 361 362 363 364 365 366
 
+        >>> da = xr.DataArray(
+        ...     data=np.arange(12).reshape((4, 3)),
+        ...     dims=("x", "y"),
+        ...     coords={"x": [10, 20, 30, 40], "letters": ("x", list("abba"))},
+        ... )
+
+        Grouping by a single variable is easy
+        >>> da.groupby("letters")
+        <DataArrayGroupBy, grouped over 1 grouper(s), 2 groups in total:
+            'letters': 2 groups with labels 'a', 'b'>
+
+        Execute a reduction
+        >>> da.groupby("letters").sum()
+        <xarray.DataArray (letters: 2, y: 3)> Size: 48B
+        array([[ 9., 11., 13.],
+               [ 9., 11., 13.]])
+        Coordinates:
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+
+        Grouping by multiple variables
+        >>> da.groupby(["letters", "x"])
+        <DataArrayGroupBy, grouped over 2 grouper(s), 8 groups in total:
+            'letters': 2 groups with labels 'a', 'b'
+            'x': 4 groups with labels 10, 20, 30, 40>
+
+        Use Grouper objects to express more complicated GroupBy operations
+        >>> from xarray.groupers import BinGrouper, UniqueGrouper
+        >>>
+        >>> da.groupby(x=BinGrouper(bins=[5, 15, 25]), letters=UniqueGrouper()).sum()
+        <xarray.DataArray (x_bins: 2, letters: 2, y: 3)> Size: 96B
+        array([[[ 0.,  1.,  2.],
+                [nan, nan, nan]],
+        <BLANKLINE>
+               [[nan, nan, nan],
+                [ 3.,  4.,  5.]]])
+        Coordinates:
+          * x_bins   (x_bins) object 16B (5, 15] (15, 25]
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+
+
         See Also
         --------
         :ref:`groupby`
@@ -6790,32 +6831,12 @@ def groupby(
         """
         from xarray.core.groupby import (
             DataArrayGroupBy,
-            ResolvedGrouper,
+            _parse_group_and_groupers,
             _validate_groupby_squeeze,
         )
-        from xarray.groupers import UniqueGrouper
 
         _validate_groupby_squeeze(squeeze)
-
-        if isinstance(group, Mapping):
-            groupers = either_dict_or_kwargs(group, groupers, "groupby")  # type: ignore
-            group = None
-
-        rgroupers: tuple[ResolvedGrouper, ...]
-        if group is not None:
-            if groupers:
-                raise ValueError(
-                    "Providing a combination of `group` and **groupers is not supported."
-                )
-            rgroupers = (ResolvedGrouper(UniqueGrouper(), group, self),)
-        else:
-            if not groupers:
-                raise ValueError("Either `group` or `**groupers` must be provided.")
-            rgroupers = tuple(
-                ResolvedGrouper(grouper, group, self)
-                for group, grouper in groupers.items()
-            )
-
+        rgroupers = _parse_group_and_groupers(self, group, groupers)
         return DataArrayGroupBy(self, rgroupers, restore_coord_dims=restore_coord_dims)
 
     @_deprecate_positional_args("v2024.07.0")

xarray/core/dataset.py

@@ -154,6 +154,7 @@
         DsCompatible,
         ErrorOptions,
         ErrorOptionsWithWarn,
+        GroupInput,
         InterpOptions,
         JoinOptions,
         PadModeOptions,
@@ -10331,10 +10332,7 @@ def interp_calendar(
     @_deprecate_positional_args("v2024.07.0")
     def groupby(
         self,
-        group: (
-            Hashable | DataArray | IndexVariable | Mapping[Any, Grouper] | None
-        ) = None,
-        *,
+        group: GroupInput = None,
         squeeze: Literal[False] = False,
         restore_coord_dims: bool = False,
         **groupers: Grouper,
@@ -10343,7 +10341,7 @@ def groupby(
 
         Parameters
         ----------
-        group : Hashable or DataArray or IndexVariable or mapping of Hashable to Grouper
+        group : str or DataArray or IndexVariable or sequence of hashable or mapping of hashable to Grouper
             Array whose unique values should be used to group this array. If a
             Hashable, must be the name of a coordinate contained in this dataarray. If a dictionary,
             must map an existing variable name to a :py:class:`Grouper` instance.
@@ -10365,6 +10363,47 @@ def groupby(
             A `DatasetGroupBy` object patterned after `pandas.GroupBy` that can be
             iterated over in the form of `(unique_value, grouped_array)` pairs.
 
+        Examples
+        --------
+        >>> ds = xr.Dataset(
+        ...     {"foo": (("x", "y"), np.arange(12).reshape((4, 3)))},
+        ...     coords={"x": [10, 20, 30, 40], "letters": ("x", list("abba"))},
+        ... )
+
+        Grouping by a single variable is easy
+        >>> ds.groupby("letters")
+        <DatasetGroupBy, grouped over 1 grouper(s), 2 groups in total:
+            'letters': 2 groups with labels 'a', 'b'>
+
+        Execute a reduction
+        >>> ds.groupby("letters").sum()
+        <xarray.Dataset> Size: 64B
+        Dimensions:  (letters: 2, y: 3)
+        Coordinates:
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+        Data variables:
+            foo      (letters, y) float64 48B 9.0 11.0 13.0 9.0 11.0 13.0
+
+        Grouping by multiple variables
+        >>> ds.groupby(["letters", "x"])
+        <DatasetGroupBy, grouped over 2 grouper(s), 8 groups in total:
+            'letters': 2 groups with labels 'a', 'b'
+            'x': 4 groups with labels 10, 20, 30, 40>
+
+        Use Grouper objects to express more complicated GroupBy operations
+        >>> from xarray.groupers import BinGrouper, UniqueGrouper
+        >>>
+        >>> ds.groupby(x=BinGrouper(bins=[5, 15, 25]), letters=UniqueGrouper()).sum()
+        <xarray.Dataset> Size: 128B
+        Dimensions:  (y: 3, x_bins: 2, letters: 2)
+        Coordinates:
+          * x_bins   (x_bins) object 16B (5, 15] (15, 25]
+          * letters  (letters) object 16B 'a' 'b'
+        Dimensions without coordinates: y
+        Data variables:
+            foo      (y, x_bins, letters) float64 96B 0.0 nan nan 3.0 ... nan nan 5.0
+
         See Also
         --------
         :ref:`groupby`
@@ -10386,31 +10425,12 @@ def groupby(
         """
         from xarray.core.groupby import (
             DatasetGroupBy,
-            ResolvedGrouper,
+            _parse_group_and_groupers,
             _validate_groupby_squeeze,
         )
-        from xarray.groupers import UniqueGrouper
 
         _validate_groupby_squeeze(squeeze)
-
-        if isinstance(group, Mapping):
-            groupers = either_dict_or_kwargs(group, groupers, "groupby")  # type: ignore
-            group = None
-
-        rgroupers: tuple[ResolvedGrouper, ...]
-        if group is not None:
-            if groupers:
-                raise ValueError(
-                    "Providing a combination of `group` and **groupers is not supported."
-                )
-            rgroupers = (ResolvedGrouper(UniqueGrouper(), group, self),)
-        else:
-            if not groupers:
-                raise ValueError("Either `group` or `**groupers` must be provided.")
-            rgroupers = tuple(
-                ResolvedGrouper(grouper, group, self)
-                for group, grouper in groupers.items()
-            )
+        rgroupers = _parse_group_and_groupers(self, group, groupers)
 
         return DatasetGroupBy(self, rgroupers, restore_coord_dims=restore_coord_dims)
 

xarray/core/groupby.py

@@ -6,7 +6,7 @@
 import warnings
 from collections.abc import Callable, Hashable, Iterator, Mapping, Sequence
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any, Generic, Literal, Union
+from typing import TYPE_CHECKING, Any, Generic, Literal, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -54,7 +54,7 @@
 
     from xarray.core.dataarray import DataArray
     from xarray.core.dataset import Dataset
-    from xarray.core.types import GroupIndex, GroupIndices, GroupKey
+    from xarray.core.types import GroupIndex, GroupIndices, GroupInput, GroupKey
     from xarray.core.utils import Frozen
     from xarray.groupers import EncodedGroups, Grouper
 
@@ -319,6 +319,51 @@ def __len__(self) -> int:
         return len(self.encoded.full_index)
 
 
+def _parse_group_and_groupers(
+    obj: T_Xarray, group: GroupInput, groupers: dict[str, Grouper]
+) -> tuple[ResolvedGrouper, ...]:
+    from xarray.core.dataarray import DataArray
+    from xarray.core.variable import Variable
+    from xarray.groupers import UniqueGrouper
+
+    if group is not None and groupers:
+        raise ValueError(
+            "Providing a combination of `group` and **groupers is not supported."
+        )
+
+    if group is None and not groupers:
+        raise ValueError("Either `group` or `**groupers` must be provided.")
+
+    if isinstance(group, np.ndarray | pd.Index):
+        raise TypeError(
+            f"`group` must be a DataArray. Received {type(group).__name__!r} instead"
+        )
+
+    if isinstance(group, Mapping):
+        grouper_mapping = either_dict_or_kwargs(group, groupers, "groupby")
+        group = None
+
+    rgroupers: tuple[ResolvedGrouper, ...]
+    if isinstance(group, DataArray | Variable):
+        rgroupers = (ResolvedGrouper(UniqueGrouper(), group, obj),)
+    else:
+        if group is not None:
+            if TYPE_CHECKING:
+                assert isinstance(group, str | Sequence)
+            group_iter: Sequence[Hashable] = (
+                (group,) if isinstance(group, str) else group
+            )
+            grouper_mapping = {g: UniqueGrouper() for g in group_iter}
+        elif groupers:
+            grouper_mapping = cast("Mapping[Hashable, Grouper]", groupers)
+
+        rgroupers = tuple(
+            ResolvedGrouper(grouper, group, obj)
+            for group, grouper in grouper_mapping.items()
+        )
+    return rgroupers
+
+
 def _validate_groupby_squeeze(squeeze: Literal[False]) -> None:
     # While we don't generally check the type of every arg, passing
     # multiple dimensions as multiple arguments is common enough, and the
@@ -327,7 +372,7 @@ def _validate_groupby_squeeze(squeeze: Literal[False]) -> None:
     # A future version could make squeeze kwarg only, but would face
     # backward-compat issues.
     if squeeze is not False:
-        raise TypeError(f"`squeeze` must be False, but {squeeze} was supplied.")
+        raise TypeError(f"`squeeze` must be False, but {squeeze!r} was supplied.")
 
 
 def _resolve_group(
@@ -626,7 +671,7 @@ def __repr__(self) -> str:
         for grouper in self.groupers:
             coord = grouper.unique_coord
             labels = ", ".join(format_array_flat(coord, 30).split())
-            text += f"\n\t{grouper.name!r}: {coord.size} groups with labels {labels}"
+            text += f"\n    {grouper.name!r}: {coord.size} groups with labels {labels}"
         return text + ">"
 
     def _iter_grouped(self) -> Iterator[T_Xarray]:

xarray/core/types.py

@@ -42,8 +42,17 @@
     from xarray.core.dataset import Dataset
     from xarray.core.indexes import Index, Indexes
     from xarray.core.utils import Frozen
-    from xarray.core.variable import Variable
-    from xarray.groupers import TimeResampler
+    from xarray.core.variable import IndexVariable, Variable
+    from xarray.groupers import Grouper, TimeResampler
+
+    GroupInput: TypeAlias = (
+        str
+        | DataArray
+        | IndexVariable
+        | Sequence[Hashable]
+        | Mapping[Any, Grouper]
+        | None
+    )
 
     try:
         from dask.array import Array as DaskArray

xarray/tests/test_groupby.py

@@ -583,7 +583,7 @@ def test_groupby_repr(obj, dim) -> None:
     N = len(np.unique(obj[dim]))
     expected = f"<{obj.__class__.__name__}GroupBy"
     expected += f", grouped over 1 grouper(s), {N} groups in total:"
-    expected += f"\n\t{dim!r}: {N} groups with labels "
+    expected += f"\n    {dim!r}: {N} groups with labels "
     if dim == "x":
         expected += "1, 2, 3, 4, 5>"
     elif dim == "y":
@@ -600,7 +600,7 @@ def test_groupby_repr_datetime(obj) -> None:
     actual = repr(obj.groupby("t.month"))
     expected = f"<{obj.__class__.__name__}GroupBy"
     expected += ", grouped over 1 grouper(s), 12 groups in total:\n"
-    expected += "\t'month': 12 groups with labels "
+    expected += "    'month': 12 groups with labels "
     expected += "1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12>"
     assert actual == expected
 
@@ -2635,6 +2635,33 @@ def test_weather_data_resample(use_flox):
     assert expected.location.attrs == ds.location.attrs
 
 
+@pytest.mark.parametrize("as_dataset", [True, False])
+def test_multiple_groupers_string(as_dataset) -> None:
+    obj = DataArray(
+        np.array([1, 2, 3, 0, 2, np.nan]),
+        dims="d",
+        coords=dict(
+            labels1=("d", np.array(["a", "b", "c", "c", "b", "a"])),
+            labels2=("d", np.array(["x", "y", "z", "z", "y", "x"])),
+        ),
+        name="foo",
+    )
+
+    if as_dataset:
+        obj = obj.to_dataset()  # type: ignore
+
+    expected = obj.groupby(labels1=UniqueGrouper(), labels2=UniqueGrouper()).mean()
+    actual = obj.groupby(("labels1", "labels2")).mean()
+    assert_identical(expected, actual)
+
+    with pytest.raises(TypeError):
+        obj.groupby("labels1", "labels2")  # type: ignore
+    with pytest.raises(ValueError):
+        obj.groupby("labels1", foo="bar")  # type: ignore
+    with pytest.raises(ValueError):
+        obj.groupby("labels1", foo=UniqueGrouper())
+
+
 @pytest.mark.parametrize("use_flox", [True, False])
 def test_multiple_groupers(use_flox) -> None:
     da = DataArray(