CLN: Remove "how" return from agg

pandas/core/apply.py

@@ -147,18 +147,14 @@ def index(self) -> Index:
     def apply(self) -> FrameOrSeriesUnion:
         pass
 
-    def agg(self) -> Tuple[Optional[FrameOrSeriesUnion], Optional[bool]]:
+    def agg(self) -> Optional[FrameOrSeriesUnion]:
         """
         Provide an implementation for the aggregators.
 
         Returns
         -------
-        tuple of result, how.
-
-        Notes
-        -----
-        how can be a string describe the required post-processing, or
-        None if not required.
+        Result of aggregation, or None if agg cannot be performed by
+        this method.
         """
         obj = self.obj
         arg = self.f
@@ -171,23 +167,21 @@ def agg(self) -> Tuple[Optional[FrameOrSeriesUnion], Optional[bool]]:
 
         result = self.maybe_apply_str()
         if result is not None:
-            return result, None
+            return result
 
         if is_dict_like(arg):
-            return self.agg_dict_like(_axis), True
+            return self.agg_dict_like(_axis)
         elif is_list_like(arg):
             # we require a list, but not a 'str'
-            return self.agg_list_like(_axis=_axis), None
-        else:
-            result = None
+            return self.agg_list_like(_axis=_axis)
 
         if callable(arg):
             f = obj._get_cython_func(arg)
             if f and not args and not kwargs:
-                return getattr(obj, f)(), None
+                return getattr(obj, f)()
 
         # caller can react
-        return result, True
+        return None
 
     def agg_list_like(self, _axis: int) -> FrameOrSeriesUnion:
         """

pandas/core/frame.py

@@ -7686,7 +7686,7 @@ def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
 
         result = None
         try:
-            result, how = self._aggregate(func, axis, *args, **kwargs)
+            result = self._aggregate(func, axis, *args, **kwargs)
         except TypeError as err:
             exc = TypeError(
                 "DataFrame constructor called with "
@@ -7720,14 +7720,14 @@ def _aggregate(self, arg, axis: Axis = 0, *args, **kwargs):
             args=args,
             kwargs=kwargs,
         )
-        result, how = op.agg()
+        result = op.agg()
 
         if axis == 1:
             # NDFrame.aggregate returns a tuple, and we need to transpose
             # only result
             result = result.T if result is not None else result
 
-        return result, how
+        return result
 
     agg = aggregate
 

pandas/core/groupby/generic.py

@@ -46,6 +46,7 @@
     ensure_platform_int,
     is_bool,
     is_categorical_dtype,
+    is_dict_like,
     is_integer_dtype,
     is_interval_dtype,
     is_numeric_dtype,
@@ -962,8 +963,8 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
         func = maybe_mangle_lambdas(func)
 
         op = GroupByApply(self, func, args, kwargs)
-        result, how = op.agg()
-        if how is None:
+        result = op.agg()
+        if not is_dict_like(func) and result is not None:
             return result
 
         if result is None:
@@ -982,7 +983,7 @@ def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs)
 
                 # try to treat as if we are passing a list
                 try:
-                    result, _ = GroupByApply(
+                    result = GroupByApply(
                         self, [func], args=(), kwargs={"_axis": self.axis}
                     ).agg()
 

pandas/core/resample.py

@@ -301,7 +301,7 @@ def pipe(
     def aggregate(self, func, *args, **kwargs):
 
         self._set_binner()
-        result, how = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
             how = func
             grouper = None

pandas/core/series.py

@@ -3973,7 +3973,7 @@ def aggregate(self, func=None, axis=0, *args, **kwargs):
             func = dict(kwargs.items())
 
         op = series_apply(self, func, args=args, kwargs=kwargs)
-        result, how = op.agg()
+        result = op.agg()
         if result is None:
 
             # we can be called from an inner function which

pandas/core/window/rolling.py

@@ -510,7 +510,7 @@ def calc(x):
             return self._apply_tablewise(homogeneous_func, name)
 
     def aggregate(self, func, *args, **kwargs):
-        result, how = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
             return self.apply(func, raw=False, args=args, kwargs=kwargs)
         return result
@@ -994,7 +994,7 @@ def calc(x):
         axis="",
     )
     def aggregate(self, func, *args, **kwargs):
-        result, how = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
+        result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg()
         if result is None:
 
             # these must apply directly