Deprecate squeeze in GroupBy.

Closes pydata#2157

Author: dcherian

doc/whats-new.rst

@@ -43,6 +43,8 @@ Breaking changes
 
 Deprecations
 ~~~~~~~~~~~~
+- The `squeeze` kwarg to GroupBy is now deprecated. (:issue:`2157`)
+  By `Deepak Cherian <https://github.com/dcherian>`_.
 
 - As part of an effort to standardize the API, we're renaming the ``dims``
   keyword arg to ``dim`` for the minority of functions which current use

xarray/core/dataarray.py

@@ -6620,7 +6620,7 @@ def interp_calendar(
     def groupby(
         self,
         group: Hashable | DataArray | IndexVariable,
-        squeeze: bool = True,
+        squeeze: bool | None = None,
         restore_coord_dims: bool = False,
     ) -> DataArrayGroupBy:
         """Returns a DataArrayGroupBy object for performing grouped operations.

xarray/core/groupby.py

@@ -73,6 +73,21 @@ def check_reduce_dims(reduce_dims, dimensions):
             )
 
 
+def _maybe_squeeze_indices(
+    indices, squeeze: bool | None, grouper: ResolvedGrouper, warn: bool
+):
+    if squeeze in [None, True] and grouper.can_squeeze:
+        if squeeze is None and warn:
+            emit_user_level_warning(
+                "The `squeeze` kwarg to GroupBy is being removed."
+                "Pass .groupby(..., squeeze=False) to silence this warning."
+            )
+        if isinstance(indices, slice):
+            assert indices.stop - indices.start == 1
+            indices = indices.start
+    return indices
+
+
 def unique_value_groups(
     ar, sort: bool = True
 ) -> tuple[np.ndarray | pd.Index, T_GroupIndices, np.ndarray]:
@@ -366,10 +381,10 @@ def dims(self):
         return self.group1d.dims
 
     @abstractmethod
-    def _factorize(self, squeeze: bool) -> T_FactorizeOut:
+    def _factorize(self) -> T_FactorizeOut:
         raise NotImplementedError
 
-    def factorize(self, squeeze: bool) -> None:
+    def factorize(self) -> None:
         # This design makes it clear  to mypy that
         # codes, group_indices, unique_coord, and full_index
         # are set by the factorize  method on the derived class.
@@ -378,7 +393,7 @@ def factorize(self, squeeze: bool) -> None:
             self.group_indices,
             self.unique_coord,
             self.full_index,
-        ) = self._factorize(squeeze)
+        ) = self._factorize()
 
     @property
     def is_unique_and_monotonic(self) -> bool:
@@ -393,15 +408,19 @@ def group_as_index(self) -> pd.Index:
             self._group_as_index = self.group1d.to_index()
         return self._group_as_index
 
+    @property
+    def can_squeeze(self):
+        is_dimension = self.group.dims == (self.group.name,)
+        return is_dimension and self.is_unique_and_monotonic
+
 
 @dataclass
 class ResolvedUniqueGrouper(ResolvedGrouper):
     grouper: UniqueGrouper
 
-    def _factorize(self, squeeze) -> T_FactorizeOut:
-        is_dimension = self.group.dims == (self.group.name,)
-        if is_dimension and self.is_unique_and_monotonic:
-            return self._factorize_dummy(squeeze)
+    def factorize(self) -> T_FactorizeOut:
+        if self.can_squeeze:
+            return self._factorize_dummy()
         else:
             return self._factorize_unique()
 
@@ -424,15 +443,12 @@ def _factorize_unique(self) -> T_FactorizeOut:
 
         return codes, group_indices, unique_coord, full_index
 
-    def _factorize_dummy(self, squeeze) -> T_FactorizeOut:
+    def _factorize_dummy(self) -> T_FactorizeOut:
         size = self.group.size
         # no need to factorize
-        if not squeeze:
-            # use slices to do views instead of fancy indexing
-            # equivalent to: group_indices = group_indices.reshape(-1, 1)
-            group_indices: T_GroupIndices = [slice(i, i + 1) for i in range(size)]
-        else:
-            group_indices = list(range(size))
+        # use slices to do views instead of fancy indexing
+        # equivalent to: group_indices = group_indices.reshape(-1, 1)
+        group_indices: T_GroupIndices = [slice(i, i + 1) for i in range(size)]
         size_range = np.arange(size)
         if isinstance(self.group, _DummyGroup):
             codes = self.group.to_dataarray().copy(data=size_range)
@@ -448,7 +464,7 @@ def _factorize_dummy(self, squeeze) -> T_FactorizeOut:
 class ResolvedBinGrouper(ResolvedGrouper):
     grouper: BinGrouper
 
-    def _factorize(self, squeeze: bool) -> T_FactorizeOut:
+    def factorize(self) -> T_FactorizeOut:
         from xarray.core.dataarray import DataArray
 
         data = self.group1d.values
@@ -546,7 +562,7 @@ def first_items(self) -> tuple[pd.Series, np.ndarray]:
                 _apply_loffset(self.grouper.loffset, first_items)
             return first_items, codes
 
-    def _factorize(self, squeeze: bool) -> T_FactorizeOut:
+    def factorize(self) -> T_FactorizeOut:
         full_index, first_items, codes_ = self._get_index_and_items()
         sbins = first_items.values.astype(np.int64)
         group_indices: T_GroupIndices = [
@@ -591,14 +607,14 @@ class TimeResampleGrouper(Grouper):
     loffset: datetime.timedelta | str | None
 
 
-def _validate_groupby_squeeze(squeeze: bool) -> None:
+def _validate_groupby_squeeze(squeeze: bool | None) -> None:
     # While we don't generally check the type of every arg, passing
     # multiple dimensions as multiple arguments is common enough, and the
     # consequences hidden enough (strings evaluate as true) to warrant
     # checking here.
     # A future version could make squeeze kwarg only, but would face
     # backward-compat issues.
-    if not isinstance(squeeze, bool):
+    if squeeze is not None and not isinstance(squeeze, bool):
         raise TypeError(f"`squeeze` must be True or False, but {squeeze} was supplied")
 
 
@@ -730,7 +746,7 @@ def __init__(
         self._original_obj = obj
 
         for grouper_ in self.groupers:
-            grouper_.factorize(squeeze)
+            grouper_._factorize()
 
         (grouper,) = self.groupers
         self._original_group = grouper.group
@@ -762,9 +778,14 @@ def sizes(self) -> Mapping[Hashable, int]:
         Dataset.sizes
         """
         if self._sizes is None:
-            self._sizes = self._obj.isel(
-                {self._group_dim: self._group_indices[0]}
-            ).sizes
+            (grouper,) = self.groupers
+            index = _maybe_squeeze_indices(
+                self._group_indices[0],
+                self._squeeze,
+                grouper,
+                warn=True,
+            )
+            self._sizes = self._obj.isel({self._group_dim: index}).sizes
 
         return self._sizes
 
@@ -798,14 +819,22 @@ def groups(self) -> dict[GroupKey, GroupIndex]:
         # provided to mimic pandas.groupby
         if self._groups is None:
             (grouper,) = self.groupers
-            self._groups = dict(zip(grouper.unique_coord.values, self._group_indices))
+            squeezed_indices = (
+                _maybe_squeeze_indices(ind, self._squeeze, grouper, warn=idx > 0)
+                for idx, ind in enumerate(self._group_indices)
+            )
+            self._groups = dict(zip(grouper.unique_coord.values, squeezed_indices))
         return self._groups
 
     def __getitem__(self, key: GroupKey) -> T_Xarray:
         """
         Get DataArray or Dataset corresponding to a particular group label.
         """
-        return self._obj.isel({self._group_dim: self.groups[key]})
+        (grouper,) = self.groupers
+        index = _maybe_squeeze_indices(
+            self.groups[key], self._squeeze, grouper, warn=True
+        )
+        return self._obj.isel({self._group_dim: index})
 
     def __len__(self) -> int:
         (grouper,) = self.groupers
@@ -826,7 +855,11 @@ def __repr__(self) -> str:
 
     def _iter_grouped(self) -> Iterator[T_Xarray]:
         """Iterate over each element in this group"""
-        for indices in self._group_indices:
+        (grouper,) = self.groupers
+        for idx, indices in enumerate(self._group_indices):
+            indices = _maybe_squeeze_indices(
+                indices, self._squeeze, grouper, warn=idx > 0
+            )
             yield self._obj.isel({self._group_dim: indices})
 
     def _infer_concat_args(self, applied_example):
@@ -1309,7 +1342,11 @@ class DataArrayGroupByBase(GroupBy["DataArray"], DataArrayGroupbyArithmetic):
     @property
     def dims(self) -> tuple[Hashable, ...]:
         if self._dims is None:
-            self._dims = self._obj.isel({self._group_dim: self._group_indices[0]}).dims
+            (grouper,) = self.groupers
+            index = _maybe_squeeze_indices(
+                self._group_indices[0], self._squeeze, grouper, warn=True
+            )
+            self._dims = self._obj.isel({self._group_dim: index}).dims
 
         return self._dims
 
@@ -1318,7 +1355,11 @@ def _iter_grouped_shortcut(self):
         metadata
         """
         var = self._obj.variable
-        for indices in self._group_indices:
+        (grouper,) = self.groupers
+        for idx, indices in enumerate(self._group_indices):
+            indices = _maybe_squeeze_indices(
+                indices, self._squeeze, grouper, warn=idx > 0
+            )
             yield var[{self._group_dim: indices}]
 
     def _concat_shortcut(self, applied, dim, positions=None):
@@ -1517,7 +1558,14 @@ class DatasetGroupByBase(GroupBy["Dataset"], DatasetGroupbyArithmetic):
     @property
     def dims(self) -> Frozen[Hashable, int]:
         if self._dims is None:
-            self._dims = self._obj.isel({self._group_dim: self._group_indices[0]}).dims
+            (grouper,) = self.groupers
+            index = _maybe_squeeze_indices(
+                self._group_indices[0],
+                self._squeeze,
+                grouper,
+                warn=True,
+            )
+            self._dims = self._obj.isel({self._group_dim: index}).dims
 
         return self._dims