TYP: fix ignores in core.groupby

pandas/core/groupby/generic.py

@@ -356,15 +356,15 @@ def _aggregate_multiple_funcs(self, arg):
     # TODO: index should not be Optional - see GH 35490
     def _wrap_series_output(
         self,
-        output: Mapping[base.OutputKey, Union[Series, np.ndarray]],
+        output: Mapping[base.OutputKey, Union[Series, ArrayLike]],
         index: Optional[Index],
     ) -> FrameOrSeriesUnion:
         """
         Wraps the output of a SeriesGroupBy operation into the expected result.
 
         Parameters
         ----------
-        output : Mapping[base.OutputKey, Union[Series, np.ndarray]]
+        output : Mapping[base.OutputKey, Union[Series, np.ndarray, ExtensionArray]]
             Data to wrap.
         index : pd.Index or None
             Index to apply to the output.
@@ -421,14 +421,14 @@ def _wrap_aggregated_output(
         return self._reindex_output(result)
 
     def _wrap_transformed_output(
-        self, output: Mapping[base.OutputKey, Union[Series, np.ndarray]]
+        self, output: Mapping[base.OutputKey, Union[Series, ArrayLike]]
     ) -> Series:
         """
         Wraps the output of a SeriesGroupBy aggregation into the expected result.
 
         Parameters
         ----------
-        output : dict[base.OutputKey, Union[Series, np.ndarray]]
+        output : dict[base.OutputKey, Union[Series, np.ndarray, ExtensionArray]]
             Dict with a sole key of 0 and a value of the result values.
 
         Returns
@@ -1121,6 +1121,7 @@ def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike:
 
             if isinstance(values, Categorical) and isinstance(result, np.ndarray):
                 # If the Categorical op didn't raise, it is dtype-preserving
+                # We get here with how="first", "last", "min", "max"
                 result = type(values)._from_sequence(result.ravel(), dtype=values.dtype)
                 # Note this will have result.dtype == dtype from above
 
@@ -1197,9 +1198,7 @@ def array_func(values: ArrayLike) -> ArrayLike:
                     assert how == "ohlc"
                     raise
 
-                # error: Incompatible types in assignment (expression has type
-                # "ExtensionArray", variable has type "ndarray")
-                result = py_fallback(values)  # type: ignore[assignment]
+                result = py_fallback(values)
 
             return cast_agg_result(result, values, how)
 
@@ -1755,14 +1754,14 @@ def _wrap_aggregated_output(
         return self._reindex_output(result)
 
     def _wrap_transformed_output(
-        self, output: Mapping[base.OutputKey, Union[Series, np.ndarray]]
+        self, output: Mapping[base.OutputKey, Union[Series, ArrayLike]]
     ) -> DataFrame:
         """
         Wraps the output of DataFrameGroupBy transformations into the expected result.
 
         Parameters
         ----------
-        output : Mapping[base.OutputKey, Union[Series, np.ndarray]]
+        output : Mapping[base.OutputKey, Union[Series, np.ndarray, ExtensionArray]]
             Data to wrap.
 
         Returns

pandas/core/groupby/groupby.py

@@ -84,7 +84,6 @@ class providing the base-class of operations.
 import pandas.core.algorithms as algorithms
 from pandas.core.arrays import (
     Categorical,
-    DatetimeArray,
     ExtensionArray,
 )
 from pandas.core.base import (
@@ -1026,7 +1025,7 @@ def _cumcount_array(self, ascending: bool = True):
     def _cython_transform(
         self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
     ):
-        output: Dict[base.OutputKey, np.ndarray] = {}
+        output: Dict[base.OutputKey, ArrayLike] = {}
 
         for idx, obj in enumerate(self._iterate_slices()):
             name = obj.name
@@ -1054,7 +1053,7 @@ def _wrap_aggregated_output(
     ):
         raise AbstractMethodError(self)
 
-    def _wrap_transformed_output(self, output: Mapping[base.OutputKey, np.ndarray]):
+    def _wrap_transformed_output(self, output: Mapping[base.OutputKey, ArrayLike]):
         raise AbstractMethodError(self)
 
     def _wrap_applied_output(self, data, keys, values, not_indexed_same: bool = False):
@@ -1099,7 +1098,7 @@ def _agg_general(
     def _cython_agg_general(
         self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
     ):
-        output: Dict[base.OutputKey, Union[np.ndarray, DatetimeArray]] = {}
+        output: Dict[base.OutputKey, ArrayLike] = {}
         # Ideally we would be able to enumerate self._iterate_slices and use
         # the index from enumeration as the key of output, but ohlc in particular
         # returns a (n x 4) array. Output requires 1D ndarrays as values, so we

pandas/core/groupby/ops.py

@@ -31,6 +31,7 @@
 import pandas._libs.groupby as libgroupby
 import pandas._libs.reduction as libreduction
 from pandas._typing import (
+    ArrayLike,
     DtypeObj,
     F,
     FrameOrSeries,
@@ -525,7 +526,7 @@ def _disallow_invalid_ops(
     @final
     def _ea_wrap_cython_operation(
         self, kind: str, values, how: str, axis: int, min_count: int = -1, **kwargs
-    ) -> np.ndarray:
+    ) -> ArrayLike:
         """
         If we have an ExtensionArray, unwrap, call _cython_operation, and
         re-wrap if appropriate.
@@ -577,7 +578,7 @@ def _ea_wrap_cython_operation(
     @final
     def _cython_operation(
         self, kind: str, values, how: str, axis: int, min_count: int = -1, **kwargs
-    ) -> np.ndarray:
+    ) -> ArrayLike:
         """
         Returns the values of a cython operation.
         """
@@ -684,11 +685,11 @@ def _cython_operation(
             # e.g. if we are int64 and need to restore to datetime64/timedelta64
             # "rank" is the only member of cython_cast_blocklist we get here
             dtype = maybe_cast_result_dtype(orig_values.dtype, how)
-            # error: Incompatible types in assignment (expression has type
-            # "Union[ExtensionArray, ndarray]", variable has type "ndarray")
-            result = maybe_downcast_to_dtype(result, dtype)  # type: ignore[assignment]
+            op_result = maybe_downcast_to_dtype(result, dtype)
+        else:
+            op_result = result
 
-        return result
+        return op_result
 
     @final
     def _aggregate(
@@ -785,14 +786,10 @@ def _aggregate_series_pure_python(self, obj: Series, func: F):
             counts[label] = group.shape[0]
             result[label] = res
 
-        result = lib.maybe_convert_objects(result, try_float=False)
-        # error: Incompatible types in assignment (expression has type
-        # "Union[ExtensionArray, ndarray]", variable has type "ndarray")
-        result = maybe_cast_result(  # type: ignore[assignment]
-            result, obj, numeric_only=True
-        )
+        out = lib.maybe_convert_objects(result, try_float=False)
+        out = maybe_cast_result(out, obj, numeric_only=True)
 
-        return result, counts
+        return out, counts
 
 
 class BinGrouper(BaseGrouper):