REF: align transform logic flow (pandas-dev#29672)

pandas/core/groupby/generic.py

@@ -394,35 +394,39 @@ def _aggregate_named(self, func, *args, **kwargs):
     def transform(self, func, *args, **kwargs):
         func = self._get_cython_func(func) or func
 
-        if isinstance(func, str):
-            if not (func in base.transform_kernel_whitelist):
-                msg = "'{func}' is not a valid function name for transform(name)"
-                raise ValueError(msg.format(func=func))
-            if func in base.cythonized_kernels:
-                # cythonized transform or canned "agg+broadcast"
-                return getattr(self, func)(*args, **kwargs)
-            else:
-                # If func is a reduction, we need to broadcast the
-                # result to the whole group. Compute func result
-                # and deal with possible broadcasting below.
-                return self._transform_fast(
-                    lambda: getattr(self, func)(*args, **kwargs), func
-                )
+        if not isinstance(func, str):
+            return self._transform_general(func, *args, **kwargs)
+
+        elif func not in base.transform_kernel_whitelist:
+            msg = f"'{func}' is not a valid function name for transform(name)"
+            raise ValueError(msg)
+        elif func in base.cythonized_kernels:
+            # cythonized transform or canned "agg+broadcast"
+            return getattr(self, func)(*args, **kwargs)
 
-        # reg transform
+        # If func is a reduction, we need to broadcast the
+        # result to the whole group. Compute func result
+        # and deal with possible broadcasting below.
+        result = getattr(self, func)(*args, **kwargs)
+        return self._transform_fast(result, func)
+
+    def _transform_general(self, func, *args, **kwargs):
+        """
+        Transform with a non-str `func`.
+        """
         klass = self._selected_obj.__class__
+
         results = []
-        wrapper = lambda x: func(x, *args, **kwargs)
         for name, group in self:
             object.__setattr__(group, "name", name)
-            res = wrapper(group)
+            res = func(group, *args, **kwargs)
 
             if isinstance(res, (ABCDataFrame, ABCSeries)):
                 res = res._values
 
             indexer = self._get_index(name)
-            s = klass(res, indexer)
-            results.append(s)
+            ser = klass(res, indexer)
+            results.append(ser)
 
         # check for empty "results" to avoid concat ValueError
         if results:
@@ -433,7 +437,7 @@ def transform(self, func, *args, **kwargs):
             result = Series()
 
         # we will only try to coerce the result type if
-        # we have a numeric dtype, as these are *always* udfs
+        # we have a numeric dtype, as these are *always* user-defined funcs
         # the cython take a different path (and casting)
         dtype = self._selected_obj.dtype
         if is_numeric_dtype(dtype):
@@ -443,17 +447,14 @@ def transform(self, func, *args, **kwargs):
         result.index = self._selected_obj.index
         return result
 
-    def _transform_fast(self, func, func_nm) -> Series:
+    def _transform_fast(self, result, func_nm: str) -> Series:
         """
         fast version of transform, only applicable to
         builtin/cythonizable functions
         """
-        if isinstance(func, str):
-            func = getattr(self, func)
-
         ids, _, ngroup = self.grouper.group_info
         cast = self._transform_should_cast(func_nm)
-        out = algorithms.take_1d(func()._values, ids)
+        out = algorithms.take_1d(result._values, ids)
         if cast:
             out = self._try_cast(out, self.obj)
         return Series(out, index=self.obj.index, name=self.obj.name)
@@ -1333,21 +1334,21 @@ def transform(self, func, *args, **kwargs):
         # optimized transforms
         func = self._get_cython_func(func) or func
 
-        if isinstance(func, str):
-            if not (func in base.transform_kernel_whitelist):
-                msg = "'{func}' is not a valid function name for transform(name)"
-                raise ValueError(msg.format(func=func))
-            if func in base.cythonized_kernels:
-                # cythonized transformation or canned "reduction+broadcast"
-                return getattr(self, func)(*args, **kwargs)
-            else:
-                # If func is a reduction, we need to broadcast the
-                # result to the whole group. Compute func result
-                # and deal with possible broadcasting below.
-                result = getattr(self, func)(*args, **kwargs)
-        else:
+        if not isinstance(func, str):
             return self._transform_general(func, *args, **kwargs)
 
+        elif func not in base.transform_kernel_whitelist:
+            msg = f"'{func}' is not a valid function name for transform(name)"
+            raise ValueError(msg)
+        elif func in base.cythonized_kernels:
+            # cythonized transformation or canned "reduction+broadcast"
+            return getattr(self, func)(*args, **kwargs)
+
+        # If func is a reduction, we need to broadcast the
+        # result to the whole group. Compute func result
+        # and deal with possible broadcasting below.
+        result = getattr(self, func)(*args, **kwargs)
+
         # a reduction transform
         if not isinstance(result, DataFrame):
             return self._transform_general(func, *args, **kwargs)
@@ -1358,16 +1359,18 @@ def transform(self, func, *args, **kwargs):
         if not result.columns.equals(obj.columns):
             return self._transform_general(func, *args, **kwargs)
 
-        return self._transform_fast(result, obj, func)
+        return self._transform_fast(result, func)
 
-    def _transform_fast(self, result: DataFrame, obj: DataFrame, func_nm) -> DataFrame:
+    def _transform_fast(self, result: DataFrame, func_nm: str) -> DataFrame:
         """
         Fast transform path for aggregations
         """
         # if there were groups with no observations (Categorical only?)
         # try casting data to original dtype
         cast = self._transform_should_cast(func_nm)
 
+        obj = self._obj_with_exclusions
+
         # for each col, reshape to to size of original frame
         # by take operation
         ids, _, ngroup = self.grouper.group_info