Return a dataclass from Grouper.factorize (pydata#8777)

* Return dataclass from factorize

* cleanup

xarray/core/groupby.py

@@ -60,9 +60,6 @@
     GroupKey = Any
     GroupIndex = Union[int, slice, list[int]]
     T_GroupIndices = list[GroupIndex]
-    T_FactorizeOut = tuple[
-        DataArray, T_GroupIndices, Union[pd.Index, "_DummyGroup"], pd.Index
-    ]
 
 
 def check_reduce_dims(reduce_dims, dimensions):
@@ -98,7 +95,7 @@ def _maybe_squeeze_indices(
 
 def unique_value_groups(
     ar, sort: bool = True
-) -> tuple[np.ndarray | pd.Index, T_GroupIndices, np.ndarray]:
+) -> tuple[np.ndarray | pd.Index, np.ndarray]:
     """Group an array by its unique values.
 
     Parameters
@@ -119,11 +116,11 @@ def unique_value_groups(
     inverse, values = pd.factorize(ar, sort=sort)
     if isinstance(values, pd.MultiIndex):
         values.names = ar.names
-    groups = _codes_to_groups(inverse, len(values))
-    return values, groups, inverse
+    return values, inverse
 
 
-def _codes_to_groups(inverse: np.ndarray, N: int) -> T_GroupIndices:
+def _codes_to_group_indices(inverse: np.ndarray, N: int) -> T_GroupIndices:
+    assert inverse.ndim == 1
     groups: T_GroupIndices = [[] for _ in range(N)]
     for n, g in enumerate(inverse):
         if g >= 0:
@@ -356,7 +353,7 @@ def can_squeeze(self) -> bool:
         return False
 
     @abstractmethod
-    def factorize(self, group) -> T_FactorizeOut:
+    def factorize(self, group) -> EncodedGroups:
         """
         Takes the group, and creates intermediates necessary for GroupBy.
         These intermediates are
@@ -378,6 +375,27 @@ class Resampler(Grouper):
     pass
 
 
+@dataclass
+class EncodedGroups:
+    """
+    Dataclass for storing intermediate values for GroupBy operation.
+    Returned by factorize method on Grouper objects.
+
+    Parameters
+    ----------
+    codes: integer codes for each group
+    full_index: pandas Index for the group coordinate
+    group_indices: optional, List of indices of array elements belonging
+                   to each group. Inferred if not provided.
+    unique_coord: Unique group values present in dataset. Inferred if not provided
+    """
+
+    codes: DataArray
+    full_index: pd.Index
+    group_indices: T_GroupIndices | None = field(default=None)
+    unique_coord: IndexVariable | _DummyGroup | None = field(default=None)
+
+
 @dataclass
 class ResolvedGrouper(Generic[T_DataWithCoords]):
     """
@@ -397,11 +415,11 @@ class ResolvedGrouper(Generic[T_DataWithCoords]):
     group: T_Group
     obj: T_DataWithCoords
 
-    # Defined by factorize:
+    # returned by factorize:
     codes: DataArray = field(init=False)
+    full_index: pd.Index = field(init=False)
     group_indices: T_GroupIndices = field(init=False)
     unique_coord: IndexVariable | _DummyGroup = field(init=False)
-    full_index: pd.Index = field(init=False)
 
     # _ensure_1d:
     group1d: T_Group = field(init=False)
@@ -445,12 +463,26 @@ def dims(self):
         return self.group1d.dims
 
     def factorize(self) -> None:
-        (
-            self.codes,
-            self.group_indices,
-            self.unique_coord,
-            self.full_index,
-        ) = self.grouper.factorize(self.group1d)
+        encoded = self.grouper.factorize(self.group1d)
+
+        self.codes = encoded.codes
+        self.full_index = encoded.full_index
+
+        if encoded.group_indices is not None:
+            self.group_indices = encoded.group_indices
+        else:
+            self.group_indices = [
+                g
+                for g in _codes_to_group_indices(self.codes.data, len(self.full_index))
+                if g
+            ]
+        if encoded.unique_coord is None:
+            unique_values = self.full_index[np.unique(encoded.codes)]
+            self.unique_coord = IndexVariable(
+                self.group.name, unique_values, attrs=self.group.attrs
+            )
+        else:
+            self.unique_coord = encoded.unique_coord
 
 
 @dataclass
@@ -477,34 +509,33 @@ def can_squeeze(self) -> bool:
         is_dimension = self.group.dims == (self.group.name,)
         return is_dimension and self.is_unique_and_monotonic
 
-    def factorize(self, group1d) -> T_FactorizeOut:
+    def factorize(self, group1d) -> EncodedGroups:
         self.group = group1d
 
         if self.can_squeeze:
             return self._factorize_dummy()
         else:
             return self._factorize_unique()
 
-    def _factorize_unique(self) -> T_FactorizeOut:
+    def _factorize_unique(self) -> EncodedGroups:
         # look through group to find the unique values
         sort = not isinstance(self.group_as_index, pd.MultiIndex)
-        unique_values, group_indices, codes_ = unique_value_groups(
-            self.group_as_index, sort=sort
-        )
-        if len(group_indices) == 0:
+        unique_values, codes_ = unique_value_groups(self.group_as_index, sort=sort)
+        if (codes_ == -1).all():
             raise ValueError(
                 "Failed to group data. Are you grouping by a variable that is all NaN?"
             )
         codes = self.group.copy(data=codes_)
-        group_indices = group_indices
         unique_coord = IndexVariable(
             self.group.name, unique_values, attrs=self.group.attrs
         )
         full_index = unique_coord
 
-        return codes, group_indices, unique_coord, full_index
+        return EncodedGroups(
+            codes=codes, full_index=full_index, unique_coord=unique_coord
+        )
 
-    def _factorize_dummy(self) -> T_FactorizeOut:
+    def _factorize_dummy(self) -> EncodedGroups:
         size = self.group.size
         # no need to factorize
         # use slices to do views instead of fancy indexing
@@ -519,8 +550,12 @@ def _factorize_dummy(self) -> T_FactorizeOut:
         full_index = IndexVariable(
             self.group.name, unique_coord.values, self.group.attrs
         )
-
-        return codes, group_indices, unique_coord, full_index
+        return EncodedGroups(
+            codes=codes,
+            group_indices=group_indices,
+            full_index=full_index,
+            unique_coord=unique_coord,
+        )
 
 
 @dataclass
@@ -536,7 +571,7 @@ def __post_init__(self) -> None:
         if duck_array_ops.isnull(self.bins).all():
             raise ValueError("All bin edges are NaN.")
 
-    def factorize(self, group) -> T_FactorizeOut:
+    def factorize(self, group) -> EncodedGroups:
         from xarray.core.dataarray import DataArray
 
         data = group.data
@@ -554,20 +589,14 @@ def factorize(self, group) -> T_FactorizeOut:
         full_index = binned.categories
         uniques = np.sort(pd.unique(binned_codes))
         unique_values = full_index[uniques[uniques != -1]]
-        group_indices = [
-            g for g in _codes_to_groups(binned_codes, len(full_index)) if g
-        ]
-
-        if len(group_indices) == 0:
-            raise ValueError(
-                f"None of the data falls within bins with edges {self.bins!r}"
-            )
 
         codes = DataArray(
             binned_codes, getattr(group, "coords", None), name=new_dim_name
         )
         unique_coord = IndexVariable(new_dim_name, pd.Index(unique_values), group.attrs)
-        return codes, group_indices, unique_coord, full_index
+        return EncodedGroups(
+            codes=codes, full_index=full_index, unique_coord=unique_coord
+        )
 
 
 @dataclass
@@ -672,7 +701,7 @@ def first_items(self) -> tuple[pd.Series, np.ndarray]:
                 _apply_loffset(self.loffset, first_items)
             return first_items, codes
 
-    def factorize(self, group) -> T_FactorizeOut:
+    def factorize(self, group) -> EncodedGroups:
         self._init_properties(group)
         full_index, first_items, codes_ = self._get_index_and_items()
         sbins = first_items.values.astype(np.int64)
@@ -684,7 +713,12 @@ def factorize(self, group) -> T_FactorizeOut:
         unique_coord = IndexVariable(group.name, first_items.index, group.attrs)
         codes = group.copy(data=codes_)
 
-        return codes, group_indices, unique_coord, full_index
+        return EncodedGroups(
+            codes=codes,
+            group_indices=group_indices,
+            full_index=full_index,
+            unique_coord=unique_coord,
+        )
 
 
 def _validate_groupby_squeeze(squeeze: bool | None) -> None: