REF: Apply.apply_multiple

pandas/core/apply.py

@@ -91,6 +91,9 @@ def frame_apply(
     elif axis == 1:
         klass = FrameColumnApply
 
+    _, func, _, _ = reconstruct_func(func, **kwargs)
+    assert func is not None
+
     return klass(
         obj,
         func,
@@ -103,11 +106,13 @@ def frame_apply(
 
 class Apply(metaclass=abc.ABCMeta):
     axis: AxisInt
+    orig_f: AggFuncType
+    f: AggFuncType
 
     def __init__(
         self,
         obj: AggObjType,
-        func,
+        func: AggFuncType,
         raw: bool,
         result_type: str | None,
         *,
@@ -127,6 +132,7 @@ def __init__(
 
         self.result_type = result_type
 
+        f: AggFuncType
         # curry if needed
         if (
             (kwargs or args)
@@ -135,13 +141,14 @@ def __init__(
         ):
 
             def f(x):
+                assert callable(func)
                 return func(x, *args, **kwargs)
 
         else:
             f = func
 
-        self.orig_f: AggFuncType = func
-        self.f: AggFuncType = f
+        self.orig_f = func
+        self.f = f
 
     @abc.abstractmethod
     def apply(self) -> DataFrame | Series:
@@ -554,7 +561,20 @@ def apply_multiple(self) -> DataFrame | Series:
         result: Series, DataFrame, or None
             Result when self.f is a list-like or dict-like, None otherwise.
         """
-        return self.obj.aggregate(self.f, self.axis, *self.args, **self.kwargs)
+        if self.axis == 1 and isinstance(self.obj, ABCDataFrame):
+            return self.obj.T.apply(self.f, 0, *self.args, **self.kwargs).T
+
+        func = self.f
+        kwargs = self.kwargs
+
+        if is_dict_like(func):
+            result = self.agg_dict_like()
+        else:
+            result = self.agg_list_like()
+
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
+
+        return result
 
     def normalize_dictlike_arg(
         self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict
@@ -1201,7 +1221,7 @@ def transform(self):
 
 def reconstruct_func(
     func: AggFuncType | None, **kwargs
-) -> tuple[bool, AggFuncType | None, list[str] | None, npt.NDArray[np.intp] | None]:
+) -> tuple[bool, AggFuncType, list[str] | None, npt.NDArray[np.intp] | None]:
     """
     This is the internal function to reconstruct func given if there is relabeling
     or not and also normalize the keyword to get new order of columns.
@@ -1256,6 +1276,7 @@ def reconstruct_func(
 
     if relabeling:
         func, columns, order = normalize_keyword_aggregation(kwargs)
+    assert func is not None
 
     return relabeling, func, columns, order
 
@@ -1445,6 +1466,26 @@ def relabel_result(
     return reordered_result_in_dict
 
 
+def reconstruct_and_relabel_result(result, func, **kwargs) -> DataFrame | Series:
+    from pandas import DataFrame
+
+    relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+
+    if relabeling:
+        # This is to keep the order to columns occurrence unchanged, and also
+        # keep the order of new columns occurrence unchanged
+
+        # For the return values of reconstruct_func, if relabeling is
+        # False, columns and order will be None.
+        assert columns is not None
+        assert order is not None
+
+        result_in_dict = relabel_result(result, func, columns, order)
+        result = DataFrame(result_in_dict, index=columns)
+
+    return result
+
+
 # TODO: Can't use, because mypy doesn't like us setting __name__
 #   error: "partial[Any]" has no attribute "__name__"
 # the type is:

pandas/core/frame.py

@@ -122,10 +122,7 @@
     roperator,
 )
 from pandas.core.accessor import CachedAccessor
-from pandas.core.apply import (
-    reconstruct_func,
-    relabel_result,
-)
+from pandas.core.apply import reconstruct_and_relabel_result
 from pandas.core.array_algos.take import take_2d_multi
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import (
@@ -9579,23 +9576,9 @@ def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
 
         axis = self._get_axis_number(axis)
 
-        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
-
         op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
         result = op.agg()
-
-        if relabeling:
-            # This is to keep the order to columns occurrence unchanged, and also
-            # keep the order of new columns occurrence unchanged
-
-            # For the return values of reconstruct_func, if relabeling is
-            # False, columns and order will be None.
-            assert columns is not None
-            assert order is not None
-
-            result_in_dict = relabel_result(result, func, columns, order)
-            result = DataFrame(result_in_dict, index=columns)
-
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
         return result
 
     agg = aggregate