@@ -373,6 +373,61 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
 
 agg = aggregate
 
+def _agg_for_resample(
+self, func=None, *args, engine=None, engine_kwargs=None, **kwargs
+):
+relabeling = func is None
+columns = None
+if relabeling:
+columns, func = validate_func_kwargs(kwargs)
+kwargs = {}
+
+if isinstance(func, str):
+if maybe_use_numba(engine) and engine is not None:
+# Not all agg functions support numba, only propagate numba kwargs
+# if user asks for numba, and engine is not None
+# (if engine is None, the called function will handle the case where
+# numba is requested via the global option)
+kwargs["engine"] = engine
+if engine_kwargs is not None:
+kwargs["engine_kwargs"] = engine_kwargs
+return getattr(self, func)(*args, **kwargs)
+
+elif isinstance(func, abc.Iterable):
+# Catch instances of lists / tuples
+# but not the class list / tuple itself.
+func = maybe_mangle_lambdas(func)
+kwargs["engine"] = engine
+kwargs["engine_kwargs"] = engine_kwargs
+ret = self._aggregate_multiple_funcs(func, *args, **kwargs)
+if relabeling:
+# columns is not narrowed by mypy from relabeling flag
+assert columns is not None # for mypy
+ret.columns = columns
+if not self.as_index:
+ret = ret.reset_index()
+return ret
+
+else:
+if maybe_use_numba(engine):
+return self._aggregate_with_numba(
+func, *args, engine_kwargs=engine_kwargs, **kwargs
+)
+
+if self.ngroups == 0:
+# e.g. test_evaluate_with_empty_groups without any groups to
+# iterate over, we have no output on which to do dtype
+# inference. We default to using the existing dtype.
+# xref GH#51445
+obj = self._obj_with_exclusions
+return self.obj._constructor(
+[],
+name=self.obj.name,
+index=self._grouper.result_index,
+dtype=obj.dtype,
+)
+return self._python_agg_general(func, *args, **kwargs)
+
 def _python_agg_general(self, func, *args, **kwargs):
 f = lambda x: func(x, *args, **kwargs)
 
@@ -1501,6 +1556,61 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
 relabeling, func, columns, order = reconstruct_func(func, **kwargs)
 func = maybe_mangle_lambdas(func)
 
+if maybe_use_numba(engine):
+# Not all agg functions support numba, only propagate numba kwargs
+# if user asks for numba
+kwargs["engine"] = engine
+kwargs["engine_kwargs"] = engine_kwargs
+
+op = GroupByApply(self, func, args=args, kwargs=kwargs)
+result = op.agg()
+if not is_dict_like(func) and result is not None:
+# GH #52849
+if not self.as_index and is_list_like(func):
+return result.reset_index()
+else:
+return result
+elif relabeling:
+# this should be the only (non-raising) case with relabeling
+# used reordered index of columns
+result = cast(DataFrame, result)
+result = result.iloc[:, order]
+result = cast(DataFrame, result)
+# error: Incompatible types in assignment (expression has type
+# "Optional[List[str]]", variable has type
+# "Union[Union[Union[ExtensionArray, ndarray[Any, Any]],
+# Index, Series], Sequence[Any]]")
+result.columns = columns # type: ignore[assignment]
+
+if result is None:
+# Remove the kwargs we inserted
+# (already stored in engine, engine_kwargs arguments)
+if "engine" in kwargs:
+del kwargs["engine"]
+del kwargs["engine_kwargs"]
+# at this point func is not a str, list-like, dict-like,
+# or a known callable(e.g. sum)
+if maybe_use_numba(engine):
+return self._aggregate_with_numba(
+func, *args, engine_kwargs=engine_kwargs, **kwargs
+)
+# grouper specific aggregations
+result = self._python_agg_general(func, *args, **kwargs)
+
+if not self.as_index:
+result = self._insert_inaxis_grouper(result)
+result.index = default_index(len(result))
+
+return result
+
+agg = aggregate
+
+def _agg_for_resample(
+self, func=None, *args, engine=None, engine_kwargs=None, **kwargs
+):
+relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+func = maybe_mangle_lambdas(func)
+
 if maybe_use_numba(engine):
 # Not all agg functions support numba, only propagate numba kwargs
 # if user asks for numba
@@ -1577,8 +1687,6 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
 
 return result
 
-agg = aggregate
-
 def _python_agg_general(self, func, *args, **kwargs):
 f = lambda x: func(x, *args, **kwargs)
 
@@ -1589,9 +1697,11 @@ def _python_agg_general(self, func, *args, **kwargs):
 
 obj = self._obj_with_exclusions
 
-if not len(obj.columns):
+if self._grouper._is_resample and not len(obj.columns):
 # e.g. test_margins_no_values_no_cols
-return self._python_apply_general(f, self._selected_obj)
+return obj._constructor(
+index=self._grouper.result_index, columns=obj.columns
+)
 
 output: dict[int, ArrayLike] = {}
 for idx, (name, ser) in enumerate(obj.items()):

@@ -589,6 +589,7 @@ def __init__(
 self._groupings: list[grouper.Grouping] = list(groupings)
 self._sort = sort
 self.dropna = dropna
+self._is_resample = False
 
 @property
 def groupings(self) -> list[grouper.Grouping]:
@@ -940,12 +941,14 @@ def _aggregate_series_pure_python(
 
 for i, group in enumerate(splitter):
 res = func(group)
-res = extract_result(res)
 
-if not initialized:
-# We only do this validation on the first iteration
-check_result_array(res, group.dtype)
-initialized = True
+if self._is_resample:
+res = extract_result(res)
+
+if not initialized:
+# We only do this validation on the first iteration
+check_result_array(res, group.dtype)
+initialized = True
 
 result[i] = res