REF: share GroupBy.transform

pandas/core/groupby/generic.py

@@ -526,35 +526,9 @@ def _aggregate_named(self, func, *args, **kwargs):
     @Substitution(klass="Series")
     @Appender(_transform_template)
     def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
-
-        if maybe_use_numba(engine):
-            with group_selection_context(self):
-                data = self._selected_obj
-            result = self._transform_with_numba(
-                data.to_frame(), func, *args, engine_kwargs=engine_kwargs, **kwargs
-            )
-            return self.obj._constructor(
-                result.ravel(), index=data.index, name=data.name
-            )
-
-        func = com.get_cython_func(func) or func
-
-        if not isinstance(func, str):
-            return self._transform_general(func, *args, **kwargs)
-
-        elif func not in base.transform_kernel_allowlist:
-            msg = f"'{func}' is not a valid function name for transform(name)"
-            raise ValueError(msg)
-        elif func in base.cythonized_kernels or func in base.transformation_kernels:
-            # cythonized transform or canned "agg+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.
-        # Temporarily set observed for dealing with categoricals.
-        with com.temp_setattr(self, "observed", True):
-            result = getattr(self, func)(*args, **kwargs)
-        return self._wrap_transform_fast_result(result)
+        return self._transform(
+            func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
+        )
 
     def _transform_general(self, func: Callable, *args, **kwargs) -> Series:
         """
@@ -586,6 +560,9 @@ def _transform_general(self, func: Callable, *args, **kwargs) -> Series:
         result.name = self._selected_obj.name
         return result
 
+    def _can_use_transform_fast(self, result) -> bool:
+        return True
+
     def _wrap_transform_fast_result(self, result: Series) -> Series:
         """
         fast version of transform, only applicable to
@@ -1334,43 +1311,14 @@ def _transform_general(self, func, *args, **kwargs):
     @Substitution(klass="DataFrame")
     @Appender(_transform_template)
     def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+        return self._transform(
+            func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
+        )
 
-        if maybe_use_numba(engine):
-            with group_selection_context(self):
-                data = self._selected_obj
-            result = self._transform_with_numba(
-                data, func, *args, engine_kwargs=engine_kwargs, **kwargs
-            )
-            return self.obj._constructor(result, index=data.index, columns=data.columns)
-
-        # optimized transforms
-        func = com.get_cython_func(func) or func
-
-        if not isinstance(func, str):
-            return self._transform_general(func, *args, **kwargs)
-
-        elif func not in base.transform_kernel_allowlist:
-            msg = f"'{func}' is not a valid function name for transform(name)"
-            raise ValueError(msg)
-        elif func in base.cythonized_kernels or func in base.transformation_kernels:
-            # cythonized transformation or canned "reduction+broadcast"
-            return getattr(self, func)(*args, **kwargs)
-        # GH 30918
-        # Use _transform_fast only when we know func is an aggregation
-        if func in base.reduction_kernels:
-            # If func is a reduction, we need to broadcast the
-            # result to the whole group. Compute func result
-            # and deal with possible broadcasting below.
-            # Temporarily set observed for dealing with categoricals.
-            with com.temp_setattr(self, "observed", True):
-                result = getattr(self, func)(*args, **kwargs)
-
-            if isinstance(result, DataFrame) and result.columns.equals(
-                self._obj_with_exclusions.columns
-            ):
-                return self._wrap_transform_fast_result(result)
-
-        return self._transform_general(func, *args, **kwargs)
+    def _can_use_transform_fast(self, result) -> bool:
+        return isinstance(result, DataFrame) and result.columns.equals(
+            self._obj_with_exclusions.columns
+        )
 
     def _wrap_transform_fast_result(self, result: DataFrame) -> DataFrame:
         """

pandas/core/groupby/groupby.py

@@ -29,6 +29,7 @@ class providing the base-class of operations.
     Sequence,
     TypeVar,
     Union,
+    cast,
 )
 
 import numpy as np
@@ -104,7 +105,10 @@ class providing the base-class of operations.
 from pandas.core.internals.blocks import ensure_block_shape
 from pandas.core.series import Series
 from pandas.core.sorting import get_group_index_sorter
-from pandas.core.util.numba_ import NUMBA_FUNC_CACHE
+from pandas.core.util.numba_ import (
+    NUMBA_FUNC_CACHE,
+    maybe_use_numba,
+)
 
 if TYPE_CHECKING:
     from typing import Literal
@@ -1398,8 +1402,55 @@ def _cython_transform(
 
         return self._wrap_transformed_output(output)
 
-    def transform(self, func, *args, **kwargs):
-        raise AbstractMethodError(self)
+    @final
+    def _transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
+
+        if maybe_use_numba(engine):
+            # TODO: tests with self._selected_obj.ndim == 1 on DataFrameGroupBy
+            with group_selection_context(self):
+                data = self._selected_obj
+            df = data if data.ndim == 2 else data.to_frame()
+            result = self._transform_with_numba(
+                df, func, *args, engine_kwargs=engine_kwargs, **kwargs
+            )
+            if self.obj.ndim == 2:
+                return cast(DataFrame, self.obj)._constructor(
+                    result, index=data.index, columns=data.columns
+                )
+            else:
+                return cast(Series, self.obj)._constructor(
+                    result.ravel(), index=data.index, name=data.name
+                )
+
+        # optimized transforms
+        func = com.get_cython_func(func) or func
+
+        if not isinstance(func, str):
+            return self._transform_general(func, *args, **kwargs)
+
+        elif func not in base.transform_kernel_allowlist:
+            msg = f"'{func}' is not a valid function name for transform(name)"
+            raise ValueError(msg)
+        elif func in base.cythonized_kernels or func in base.transformation_kernels:
+            # cythonized transform or canned "agg+broadcast"
+            return getattr(self, func)(*args, **kwargs)
+
+        else:
+            # i.e. func in base.reduction_kernels
+
+            # GH#30918 Use _transform_fast only when we know func is an aggregation
+            # If func is a reduction, we need to broadcast the
+            # result to the whole group. Compute func result
+            # and deal with possible broadcasting below.
+            # Temporarily set observed for dealing with categoricals.
+            with com.temp_setattr(self, "observed", True):
+                result = getattr(self, func)(*args, **kwargs)
+
+            if self._can_use_transform_fast(result):
+                return self._wrap_transform_fast_result(result)
+
+            # only reached for DataFrameGroupBy
+            return self._transform_general(func, *args, **kwargs)
 
     # -----------------------------------------------------------------
     # Utilities