REF: uses_mask in group_any_all

pandas/_libs/groupby.pyx

@@ -565,11 +565,11 @@ def group_any_all(
     const uint8_t[:, :] mask,
     str val_test,
     bint skipna,
-    bint nullable,
+    uint8_t[:, ::1] result_mask,
 ) -> None:
     """
     Aggregated boolean values to show truthfulness of group elements. If the
-    input is a nullable type (nullable=True), the result will be computed
+    input is a nullable type (result_mask is not None), the result will be computed
     using Kleene logic.
 
     Parameters
@@ -587,9 +587,9 @@ def group_any_all(
         String object dictating whether to use any or all truth testing
     skipna : bool
         Flag to ignore nan values during truth testing
-    nullable : bool
-        Whether or not the input is a nullable type. If True, the
-        result will be computed using Kleene logic
+    result_mask : ndarray[bool, ndim=2], optional
+        If not None, these specify locations in the output that are NA.
+        Modified in-place.
 
     Notes
     -----
@@ -601,6 +601,7 @@ def group_any_all(
         Py_ssize_t i, j, N = len(labels), K = out.shape[1]
         intp_t lab
         int8_t flag_val, val
+        bint uses_mask = result_mask is not None
 
     if val_test == "all":
         # Because the 'all' value of an empty iterable in Python is True we can
@@ -627,12 +628,12 @@ def group_any_all(
                 if skipna and mask[i, j]:
                     continue
 
-                if nullable and mask[i, j]:
+                if uses_mask and mask[i, j]:
                     # Set the position as masked if `out[lab] != flag_val`, which
                     # would indicate True/False has not yet been seen for any/all,
                     # so by Kleene logic the result is currently unknown
                     if out[lab, j] != flag_val:
-                        out[lab, j] = -1
+                        result_mask[lab, j] = 1
                     continue
 
                 val = values[i, j]
@@ -641,6 +642,8 @@ def group_any_all(
                 # already determined
                 if val == flag_val:
                     out[lab, j] = flag_val
+                    if uses_mask:
+                        result_mask[lab, j] = 0
 
 
 # ----------------------------------------------------------------------

pandas/core/groupby/groupby.py

@@ -1658,10 +1658,10 @@ def objs_to_bool(vals: ArrayLike) -> tuple[np.ndarray, type]:
         def result_to_bool(
             result: np.ndarray,
             inference: type,
-            nullable: bool = False,
+            result_mask,
         ) -> ArrayLike:
-            if nullable:
-                return BooleanArray(result.astype(bool, copy=False), result == -1)
+            if result_mask is not None:
+                return BooleanArray(result.astype(bool, copy=False), result_mask)
             else:
                 return result.astype(inference, copy=False)
 
@@ -1939,10 +1939,8 @@ def _preprocessing(values):
                     return values._data, None
                 return values, None
 
-            def _postprocessing(
-                vals, inference, nullable: bool = False, result_mask=None
-            ) -> ArrayLike:
-                if nullable:
+            def _postprocessing(vals, inference, result_mask=None) -> ArrayLike:
+                if result_mask is not None:
                     if result_mask.ndim == 2:
                         result_mask = result_mask[:, 0]
                     return FloatingArray(np.sqrt(vals), result_mask.view(np.bool_))
@@ -3716,13 +3714,11 @@ def blk_func(values: ArrayLike) -> ArrayLike:
                     mask = mask.reshape(-1, 1)
                 func = partial(func, mask=mask)
 
-            if how != "std":
-                is_nullable = isinstance(values, BaseMaskedArray)
-                func = partial(func, nullable=is_nullable)
-
-            elif isinstance(values, BaseMaskedArray):
+            result_mask = None
+            if isinstance(values, BaseMaskedArray):
                 result_mask = np.zeros(result.shape, dtype=np.bool_)
-                func = partial(func, result_mask=result_mask)
+
+            func = partial(func, result_mask=result_mask)
 
             # Call func to modify result in place
             if how == "std":
@@ -3733,14 +3729,12 @@ def blk_func(values: ArrayLike) -> ArrayLike:
             if values.ndim == 1:
                 assert result.shape[1] == 1, result.shape
                 result = result[:, 0]
+                if result_mask is not None:
+                    assert result_mask.shape[1] == 1, result_mask.shape
+                    result_mask = result_mask[:, 0]
 
             if post_processing:
-                pp_kwargs: dict[str, bool | np.ndarray] = {}
-                pp_kwargs["nullable"] = isinstance(values, BaseMaskedArray)
-                if how == "std" and pp_kwargs["nullable"]:
-                    pp_kwargs["result_mask"] = result_mask
-
-                result = post_processing(result, inferences, **pp_kwargs)
+                result = post_processing(result, inferences, result_mask=result_mask)
 
             if how == "std" and is_datetimelike:
                 values = cast("DatetimeArray | TimedeltaArray", values)