REF: Move transform into apply

pandas/core/aggregation.py

@@ -20,28 +20,19 @@
     Sequence,
     Tuple,
     Union,
-    cast,
 )
 
 from pandas._typing import (
     AggFuncType,
-    AggFuncTypeBase,
-    AggFuncTypeDict,
-    Axis,
     FrameOrSeries,
-    FrameOrSeriesUnion,
 )
 
 from pandas.core.dtypes.common import (
     is_dict_like,
     is_list_like,
 )
-from pandas.core.dtypes.generic import (
-    ABCDataFrame,
-    ABCSeries,
-)
+from pandas.core.dtypes.generic import ABCSeries
 
-from pandas.core.algorithms import safe_sort
 from pandas.core.base import SpecificationError
 import pandas.core.common as com
 from pandas.core.indexes.api import Index
@@ -405,134 +396,3 @@ def validate_func_kwargs(
         no_arg_message = "Must provide 'func' or named aggregation **kwargs."
         raise TypeError(no_arg_message)
     return columns, func
-
-
-def transform(
-    obj: FrameOrSeries, func: AggFuncType, axis: Axis, *args, **kwargs
-) -> FrameOrSeriesUnion:
-    """
-    Transform a DataFrame or Series
-
-    Parameters
-    ----------
-    obj : DataFrame or Series
-        Object to compute the transform on.
-    func : string, function, list, or dictionary
-        Function(s) to compute the transform with.
-    axis : {0 or 'index', 1 or 'columns'}
-        Axis along which the function is applied:
-
-        * 0 or 'index': apply function to each column.
-        * 1 or 'columns': apply function to each row.
-
-    Returns
-    -------
-    DataFrame or Series
-        Result of applying ``func`` along the given axis of the
-        Series or DataFrame.
-
-    Raises
-    ------
-    ValueError
-        If the transform function fails or does not transform.
-    """
-    is_series = obj.ndim == 1
-
-    if obj._get_axis_number(axis) == 1:
-        assert not is_series
-        return transform(obj.T, func, 0, *args, **kwargs).T
-
-    if is_list_like(func) and not is_dict_like(func):
-        func = cast(List[AggFuncTypeBase], func)
-        # Convert func equivalent dict
-        if is_series:
-            func = {com.get_callable_name(v) or v: v for v in func}
-        else:
-            func = {col: func for col in obj}
-
-    if is_dict_like(func):
-        func = cast(AggFuncTypeDict, func)
-        return transform_dict_like(obj, func, *args, **kwargs)
-
-    # func is either str or callable
-    func = cast(AggFuncTypeBase, func)
-    try:
-        result = transform_str_or_callable(obj, func, *args, **kwargs)
-    except Exception:
-        raise ValueError("Transform function failed")
-
-    # Functions that transform may return empty Series/DataFrame
-    # when the dtype is not appropriate
-    if isinstance(result, (ABCSeries, ABCDataFrame)) and result.empty and not obj.empty:
-        raise ValueError("Transform function failed")
-    if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals(
-        obj.index
-    ):
-        raise ValueError("Function did not transform")
-
-    return result
-
-
-def transform_dict_like(
-    obj: FrameOrSeries,
-    func: AggFuncTypeDict,
-    *args,
-    **kwargs,
-):
-    """
-    Compute transform in the case of a dict-like func
-    """
-    from pandas.core.reshape.concat import concat
-
-    if len(func) == 0:
-        raise ValueError("No transform functions were provided")
-
-    if obj.ndim != 1:
-        # Check for missing columns on a frame
-        cols = set(func.keys()) - set(obj.columns)
-        if len(cols) > 0:
-            cols_sorted = list(safe_sort(list(cols)))
-            raise SpecificationError(f"Column(s) {cols_sorted} do not exist")
-
-    # Can't use func.values(); wouldn't work for a Series
-    if any(is_dict_like(v) for _, v in func.items()):
-        # GH 15931 - deprecation of renaming keys
-        raise SpecificationError("nested renamer is not supported")
-
-    results: Dict[Hashable, FrameOrSeriesUnion] = {}
-    for name, how in func.items():
-        colg = obj._gotitem(name, ndim=1)
-        try:
-            results[name] = transform(colg, how, 0, *args, **kwargs)
-        except Exception as err:
-            if str(err) in {
-                "Function did not transform",
-                "No transform functions were provided",
-            }:
-                raise err
-
-    # combine results
-    if not results:
-        raise ValueError("Transform function failed")
-    return concat(results, axis=1)
-
-
-def transform_str_or_callable(
-    obj: FrameOrSeries, func: AggFuncTypeBase, *args, **kwargs
-) -> FrameOrSeriesUnion:
-    """
-    Compute transform in the case of a string or callable func
-    """
-    if isinstance(func, str):
-        return obj._try_aggregate_string_function(func, *args, **kwargs)
-
-    if not args and not kwargs:
-        f = obj._get_cython_func(func)
-        if f:
-            return getattr(obj, f)()
-
-    # Two possible ways to use a UDF - apply or call directly
-    try:
-        return obj.apply(func, args=args, **kwargs)
-    except Exception:
-        return func(obj, *args, **kwargs)

pandas/core/apply.py

@@ -6,6 +6,7 @@
     TYPE_CHECKING,
     Any,
     Dict,
+    Hashable,
     Iterator,
     List,
     Optional,
@@ -151,6 +152,7 @@ def f(x):
         else:
             f = func
 
+        self.orig_f: AggFuncType = func
         self.f: AggFuncType = f
 
     @property
@@ -197,6 +199,131 @@ def agg(self) -> Optional[FrameOrSeriesUnion]:
         # caller can react
         return None
 
+    def transform(self) -> FrameOrSeriesUnion:
+        """
+        Transform a DataFrame or Series.
+
+        Returns
+        -------
+        DataFrame or Series
+            Result of applying ``func`` along the given axis of the
+            Series or DataFrame.
+
+        Raises
+        ------
+        ValueError
+            If the transform function fails or does not transform.
+        """
+        obj = self.obj
+        func = self.orig_f
+        axis = self.axis
+        args = self.args
+        kwargs = self.kwargs
+
+        is_series = obj.ndim == 1
+
+        if obj._get_axis_number(axis) == 1:
+            assert not is_series
+            return obj.T.transform(func, 0, *args, **kwargs).T
+
+        if is_list_like(func) and not is_dict_like(func):
+            func = cast(List[AggFuncTypeBase], func)
+            # Convert func equivalent dict
+            if is_series:
+                func = {com.get_callable_name(v) or v: v for v in func}
+            else:
+                func = {col: func for col in obj}
+
+        if is_dict_like(func):
+            func = cast(AggFuncTypeDict, func)
+            return self.transform_dict_like(func)
+
+        # func is either str or callable
+        func = cast(AggFuncTypeBase, func)
+        try:
+            result = self.transform_str_or_callable(func)
+        except Exception:
+            raise ValueError("Transform function failed")
+
+        # Functions that transform may return empty Series/DataFrame
+        # when the dtype is not appropriate
+        if (
+            isinstance(result, (ABCSeries, ABCDataFrame))
+            and result.empty
+            and not obj.empty
+        ):
+            raise ValueError("Transform function failed")
+        if not isinstance(result, (ABCSeries, ABCDataFrame)) or not result.index.equals(
+            obj.index
+        ):
+            raise ValueError("Function did not transform")
+
+        return result
+
+    def transform_dict_like(self, func):
+        """
+        Compute transform in the case of a dict-like func
+        """
+        from pandas.core.reshape.concat import concat
+
+        obj = self.obj
+        args = self.args
+        kwargs = self.kwargs
+
+        if len(func) == 0:
+            raise ValueError("No transform functions were provided")
+
+        if obj.ndim != 1:
+            # Check for missing columns on a frame
+            cols = set(func.keys()) - set(obj.columns)
+            if len(cols) > 0:
+                cols_sorted = list(safe_sort(list(cols)))
+                raise SpecificationError(f"Column(s) {cols_sorted} do not exist")
+
+        # Can't use func.values(); wouldn't work for a Series
+        if any(is_dict_like(v) for _, v in func.items()):
+            # GH 15931 - deprecation of renaming keys
+            raise SpecificationError("nested renamer is not supported")
+
+        results: Dict[Hashable, FrameOrSeriesUnion] = {}
+        for name, how in func.items():
+            colg = obj._gotitem(name, ndim=1)
+            try:
+                results[name] = colg.transform(how, 0, *args, **kwargs)
+            except Exception as err:
+                if str(err) in {
+                    "Function did not transform",
+                    "No transform functions were provided",
+                }:
+                    raise err
+
+        # combine results
+        if not results:
+            raise ValueError("Transform function failed")
+        return concat(results, axis=1)
+
+    def transform_str_or_callable(self, func) -> FrameOrSeriesUnion:
+        """
+        Compute transform in the case of a string or callable func
+        """
+        obj = self.obj
+        args = self.args
+        kwargs = self.kwargs
+
+        if isinstance(func, str):
+            return obj._try_aggregate_string_function(func, *args, **kwargs)
+
+        if not args and not kwargs:
+            f = obj._get_cython_func(func)
+            if f:
+                return getattr(obj, f)()
+
+        # Two possible ways to use a UDF - apply or call directly
+        try:
+            return obj.apply(func, args=args, **kwargs)
+        except Exception:
+            return func(obj, *args, **kwargs)
+
     def agg_list_like(self, _axis: int) -> FrameOrSeriesUnion:
         """
         Compute aggregation in the case of a list-like argument.
@@ -901,6 +1028,9 @@ def __init__(
     def apply(self):
         raise NotImplementedError
 
+    def transform(self):
+        raise NotImplementedError
+
 
 class ResamplerWindowApply(Apply):
     axis = 0
@@ -924,3 +1054,6 @@ def __init__(
 
     def apply(self):
         raise NotImplementedError
+
+    def transform(self):
+        raise NotImplementedError

pandas/core/frame.py

@@ -139,7 +139,6 @@
 from pandas.core.aggregation import (
     reconstruct_func,
     relabel_result,
-    transform,
 )
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import ExtensionArray
@@ -7786,7 +7785,10 @@ def _aggregate(self, arg, axis: Axis = 0, *args, **kwargs):
     def transform(
         self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
     ) -> DataFrame:
-        result = transform(self, func, axis, *args, **kwargs)
+        from pandas.core.apply import frame_apply
+
+        op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
+        result = op.transform()
         assert isinstance(result, DataFrame)
         return result
 

pandas/core/series.py

@@ -94,7 +94,6 @@
     ops,
 )
 from pandas.core.accessor import CachedAccessor
-from pandas.core.aggregation import transform
 from pandas.core.apply import series_apply
 from pandas.core.arrays import ExtensionArray
 from pandas.core.arrays.categorical import CategoricalAccessor
@@ -4035,7 +4034,10 @@ def aggregate(self, func=None, axis=0, *args, **kwargs):
     def transform(
         self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
     ) -> FrameOrSeriesUnion:
-        return transform(self, func, axis, *args, **kwargs)
+        # Validate axis argument
+        self._get_axis_number(axis)
+        result = series_apply(self, func=func, args=args, kwargs=kwargs).transform()
+        return result
 
     def apply(
         self,