diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py
index 499bef2b61046..3dfad7f172cda 100644
--- a/pandas/core/groupby/generic.py
+++ b/pandas/core/groupby/generic.py
@@ -15,7 +15,6 @@
     Any,
     Callable,
     Hashable,
-    Iterable,
     Literal,
     Mapping,
     NamedTuple,
@@ -1337,38 +1336,27 @@ def _python_agg_general(self, func, *args, **kwargs):
         func = com.is_builtin_func(func)
         f = lambda x: func(x, *args, **kwargs)
 
-        # iterate through "columns" ex exclusions to populate output dict
-        output: dict[base.OutputKey, ArrayLike] = {}
-
         if self.ngroups == 0:
             # e.g. test_evaluate_with_empty_groups different path gets different
             #  result dtype in empty case.
             return self._python_apply_general(f, self._selected_obj, is_agg=True)
 
-        for idx, obj in enumerate(self._iterate_slices()):
-            name = obj.name
-            result = self.grouper.agg_series(obj, f)
-            key = base.OutputKey(label=name, position=idx)
-            output[key] = result
+        obj = self._obj_with_exclusions
+        if self.axis == 1:
+            obj = obj.T
 
-        if not output:
+        if not len(obj.columns):
             # e.g. test_margins_no_values_no_cols
             return self._python_apply_general(f, self._selected_obj)
 
-        res = self._indexed_output_to_ndframe(output)
-        return self._wrap_aggregated_output(res)
-
-    def _iterate_slices(self) -> Iterable[Series]:
-        obj = self._obj_with_exclusions
-        if self.axis == 1:
-            obj = obj.T
+        output: dict[int, ArrayLike] = {}
+        for idx, (name, ser) in enumerate(obj.items()):
+            result = self.grouper.agg_series(ser, f)
+            output[idx] = result
 
-        if isinstance(obj, Series):
-            # Occurs when doing DataFrameGroupBy(...)["X"]
-            yield obj
-        else:
-            for label, values in obj.items():
-                yield values
+        res = self.obj._constructor(output)
+        res.columns = obj.columns.copy(deep=False)
+        return self._wrap_aggregated_output(res)
 
     def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
         if self.grouper.nkeys != 1:
@@ -1830,20 +1818,6 @@ def _get_data_to_aggregate(
             mgr = mgr.get_numeric_data(copy=False)
         return mgr
 
-    def _indexed_output_to_ndframe(
-        self, output: Mapping[base.OutputKey, ArrayLike]
-    ) -> DataFrame:
-        """
-        Wrap the dict result of a GroupBy aggregation into a DataFrame.
-        """
-        indexed_output = {key.position: val for key, val in output.items()}
-        columns = Index([key.label for key in output])
-        columns._set_names(self._obj_with_exclusions._get_axis(1 - self.axis).names)
-
-        result = self.obj._constructor(indexed_output)
-        result.columns = columns
-        return result
-
     def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
         return self.obj._constructor(mgr)
 
diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index 3973dc18abd59..55e14bc11246b 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -1099,11 +1099,6 @@ def _insert_inaxis_grouper(self, result: Series | DataFrame) -> DataFrame:
 
         return result
 
-    def _indexed_output_to_ndframe(
-        self, result: Mapping[base.OutputKey, ArrayLike]
-    ) -> Series | DataFrame:
-        raise AbstractMethodError(self)
-
     @final
     def _maybe_transpose_result(self, result: NDFrameT) -> NDFrameT:
         if self.axis == 1: