CLN: remove unnecessary return from agg_series

pandas/core/groupby/groupby.py

@@ -1269,7 +1269,7 @@ def _python_agg_general(self, func, *args, **kwargs):
 
             try:
                 # if this function is invalid for this dtype, we will ignore it.
-                result, counts = self.grouper.agg_series(obj, f)
+                result = self.grouper.agg_series(obj, f)
             except TypeError:
                 continue
 
@@ -1339,7 +1339,7 @@ def _agg_py_fallback(
         # For SeriesGroupBy we could just use self instead of sgb
 
         if self.ngroups > 0:
-            res_values, _ = self.grouper.agg_series(ser, alt)
+            res_values = self.grouper.agg_series(ser, alt)
         else:
             # equiv: res_values = self._python_agg_general(alt)
             res_values = sgb._python_apply_general(alt, ser)._values

pandas/core/groupby/ops.py

@@ -969,28 +969,28 @@ def _cython_operation(
         )
 
     @final
-    def agg_series(self, obj: Series, func: F) -> tuple[ArrayLike, np.ndarray]:
+    def agg_series(self, obj: Series, func: F) -> ArrayLike:
         # Caller is responsible for checking ngroups != 0
         assert self.ngroups != 0
 
         cast_back = True
         if len(obj) == 0:
             # SeriesGrouper would raise if we were to call _aggregate_series_fast
-            result, counts = self._aggregate_series_pure_python(obj, func)
+            result = self._aggregate_series_pure_python(obj, func)
 
         elif is_extension_array_dtype(obj.dtype):
             # _aggregate_series_fast would raise TypeError when
             #  calling libreduction.Slider
             # In the datetime64tz case it would incorrectly cast to tz-naive
             # TODO: can we get a performant workaround for EAs backed by ndarray?
-            result, counts = self._aggregate_series_pure_python(obj, func)
+            result = self._aggregate_series_pure_python(obj, func)
 
         elif obj.index._has_complex_internals:
             # Preempt TypeError in _aggregate_series_fast
-            result, counts = self._aggregate_series_pure_python(obj, func)
+            result = self._aggregate_series_pure_python(obj, func)
 
         else:
-            result, counts = self._aggregate_series_fast(obj, func)
+            result = self._aggregate_series_fast(obj, func)
             cast_back = False
 
         npvalues = lib.maybe_convert_objects(result, try_float=False)
@@ -999,11 +999,11 @@ def agg_series(self, obj: Series, func: F) -> tuple[ArrayLike, np.ndarray]:
             out = maybe_cast_pointwise_result(npvalues, obj.dtype, numeric_only=True)
         else:
             out = npvalues
-        return out, counts
+        return out
+
+    def _aggregate_series_fast(self, obj: Series, func: F) -> np.ndarray:
+        # -> np.ndarray[object]
 
-    def _aggregate_series_fast(
-        self, obj: Series, func: F
-    ) -> tuple[ArrayLike, np.ndarray]:
         # At this point we have already checked that
         #  - obj.index is not a MultiIndex
         #  - obj is backed by an ndarray, not ExtensionArray
@@ -1018,11 +1018,12 @@ def _aggregate_series_fast(
         obj = obj.take(indexer)
         ids = ids.take(indexer)
         sgrouper = libreduction.SeriesGrouper(obj, func, ids, ngroups)
-        result, counts = sgrouper.get_result()
-        return result, counts
+        result, _ = sgrouper.get_result()
+        return result
 
     @final
-    def _aggregate_series_pure_python(self, obj: Series, func: F):
+    def _aggregate_series_pure_python(self, obj: Series, func: F) -> np.ndarray:
+        # -> np.ndarray[object]
         ids, _, ngroups = self.group_info
 
         counts = np.zeros(ngroups, dtype=int)
@@ -1047,7 +1048,7 @@ def _aggregate_series_pure_python(self, obj: Series, func: F):
             counts[i] = group.shape[0]
             result[i] = res
 
-        return result, counts
+        return result
 
 
 class BinGrouper(BaseGrouper):
@@ -1205,16 +1206,17 @@ def groupings(self) -> list[grouper.Grouping]:
         ping = grouper.Grouping(lev, lev, in_axis=False, level=None, name=lev.name)
         return [ping]
 
-    def _aggregate_series_fast(
-        self, obj: Series, func: F
-    ) -> tuple[ArrayLike, np.ndarray]:
+    def _aggregate_series_fast(self, obj: Series, func: F) -> np.ndarray:
+        # -> np.ndarray[object]
+
         # At this point we have already checked that
         #  - obj.index is not a MultiIndex
         #  - obj is backed by an ndarray, not ExtensionArray
         #  - ngroups != 0
         #  - len(self.bins) > 0
         sbg = libreduction.SeriesBinGrouper(obj, func, self.bins)
-        return sbg.get_result()
+        result, _ = sbg.get_result()
+        return result
 
 
 def _is_indexed_like(obj, axes, axis: int) -> bool: