Introduce Grouper objects internally

xarray/core/common.py

@@ -949,7 +949,7 @@ def _resample(
         # TODO support non-string indexer after removing the old API.
 
         from xarray.core.dataarray import DataArray
-        from xarray.core.groupby import TimeResampleGrouper
+        from xarray.core.groupby import ResolvedTimeResampleGrouper, TimeResampleGrouper
         from xarray.core.resample import RESAMPLE_DIM
 
         if keep_attrs is not None:
@@ -1012,11 +1012,13 @@ def _resample(
         group = DataArray(
             dim_coord, coords=dim_coord.coords, dims=dim_coord.dims, name=RESAMPLE_DIM
         )
+
+        rgrouper = ResolvedTimeResampleGrouper(grouper, group, self)
+
         return resample_cls(
             self,
-            group=group,
+            (rgrouper,),
             dim=dim_name,
-            grouper=grouper,
             resample_dim=RESAMPLE_DIM,
             restore_coord_dims=restore_coord_dims,
         )

xarray/core/computation.py

@@ -515,15 +515,16 @@ def apply_groupby_func(func, *args):
     groupbys = [arg for arg in args if isinstance(arg, GroupBy)]
     assert groupbys, "must have at least one groupby to iterate over"
     first_groupby = groupbys[0]
-    if any(not first_groupby._group.equals(gb._group) for gb in groupbys[1:]):
+    (grouper,) = first_groupby.groupers
+    if any(not grouper.group.equals(gb.groupers[0].group) for gb in groupbys[1:]):
         raise ValueError(
             "apply_ufunc can only perform operations over "
             "multiple GroupBy objects at once if they are all "
             "grouped the same way"
         )
 
-    grouped_dim = first_groupby._group.name
-    unique_values = first_groupby._unique_coord.values
+    grouped_dim = grouper.name
+    unique_values = grouper.unique_coord.values
 
     iterators = []
     for arg in args:

xarray/core/dataarray.py

@@ -6467,21 +6467,20 @@ def groupby(
         core.groupby.DataArrayGroupBy
         pandas.DataFrame.groupby
         """
-        from xarray.core.groupby import DataArrayGroupBy
-
-        # 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):
-            raise TypeError(
-                f"`squeeze` must be True or False, but {squeeze} was supplied"
-            )
+        from xarray.core.groupby import (
+            DataArrayGroupBy,
+            ResolvedUniqueGrouper,
+            UniqueGrouper,
+            _validate_groupby_squeeze,
+        )
 
+        _validate_groupby_squeeze(squeeze)
+        rgrouper = ResolvedUniqueGrouper(UniqueGrouper(), group, self)
         return DataArrayGroupBy(
-            self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims
+            self,
+            (rgrouper,),
+            squeeze=squeeze,
+            restore_coord_dims=restore_coord_dims,
         )
 
     def groupby_bins(
@@ -6552,21 +6551,31 @@ def groupby_bins(
         ----------
         .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
         """
-        from xarray.core.groupby import DataArrayGroupBy
+        from xarray.core.groupby import (
+            BinGrouper,
+            DataArrayGroupBy,
+            ResolvedBinGrouper,
+            _validate_groupby_squeeze,
+        )
 
-        return DataArrayGroupBy(
-            self,
-            group,
-            squeeze=squeeze,
+        _validate_groupby_squeeze(squeeze)
+        grouper = BinGrouper(
             bins=bins,
-            restore_coord_dims=restore_coord_dims,
             cut_kwargs={
                 "right": right,
                 "labels": labels,
                 "precision": precision,
                 "include_lowest": include_lowest,
             },
         )
+        rgrouper = ResolvedBinGrouper(grouper, group, self)
+
+        return DataArrayGroupBy(
+            self,
+            (rgrouper,),
+            squeeze=squeeze,
+            restore_coord_dims=restore_coord_dims,
+        )
 
     def weighted(self, weights: DataArray) -> DataArrayWeighted:
         """

xarray/core/dataset.py

@@ -8950,21 +8950,21 @@ def groupby(
         Dataset.resample
         DataArray.resample
         """
-        from xarray.core.groupby import DatasetGroupBy
-
-        # 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):
-            raise TypeError(
-                f"`squeeze` must be True or False, but {squeeze} was supplied"
-            )
+        from xarray.core.groupby import (
+            DatasetGroupBy,
+            ResolvedUniqueGrouper,
+            UniqueGrouper,
+            _validate_groupby_squeeze,
+        )
+
+        _validate_groupby_squeeze(squeeze)
+        rgrouper = ResolvedUniqueGrouper(UniqueGrouper(), group, self)
 
         return DatasetGroupBy(
-            self, group, squeeze=squeeze, restore_coord_dims=restore_coord_dims
+            self,
+            (rgrouper,),
+            squeeze=squeeze,
+            restore_coord_dims=restore_coord_dims,
         )
 
     def groupby_bins(
@@ -9035,21 +9035,31 @@ def groupby_bins(
         ----------
         .. [1] http://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html
         """
-        from xarray.core.groupby import DatasetGroupBy
+        from xarray.core.groupby import (
+            BinGrouper,
+            DatasetGroupBy,
+            ResolvedBinGrouper,
+            _validate_groupby_squeeze,
+        )
 
-        return DatasetGroupBy(
-            self,
-            group,
-            squeeze=squeeze,
+        _validate_groupby_squeeze(squeeze)
+        grouper = BinGrouper(
             bins=bins,
-            restore_coord_dims=restore_coord_dims,
             cut_kwargs={
                 "right": right,
                 "labels": labels,
                 "precision": precision,
                 "include_lowest": include_lowest,
             },
         )
+        rgrouper = ResolvedBinGrouper(grouper, group, self)
+
+        return DatasetGroupBy(
+            self,
+            (rgrouper,),
+            squeeze=squeeze,
+            restore_coord_dims=restore_coord_dims,
+        )
 
     def weighted(self, weights: DataArray) -> DatasetWeighted:
         """