CLN: aggregation.transform (#36478)

Author: rhshadrach

doc/source/whatsnew/v1.2.0.rst

@@ -424,6 +424,7 @@ Reshaping
 - Bug in :func:`union_indexes` where input index names are not preserved in some cases. Affects :func:`concat` and :class:`DataFrame` constructor (:issue:`13475`)
 - Bug in func :meth:`crosstab` when using multiple columns with ``margins=True`` and ``normalize=True`` (:issue:`35144`)
 - Bug in :meth:`DataFrame.agg` with ``func={'name':<FUNC>}`` incorrectly raising ``TypeError`` when ``DataFrame.columns==['Name']`` (:issue:`36212`)
+- Bug in :meth:`Series.transform` would give incorrect results or raise when the argument ``func`` was dictionary (:issue:`35811`)
 -
 
 Sparse
@@ -446,7 +447,6 @@ Other
 - Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` incorrectly raising ``AssertionError`` instead of ``ValueError`` when invalid parameter combinations are passed (:issue:`36045`)
 - Bug in :meth:`DataFrame.replace` and :meth:`Series.replace` with numeric values and string ``to_replace`` (:issue:`34789`)
 - Fixed metadata propagation in the :class:`Series.dt` accessor (:issue:`28283`)
-- Bug in :meth:`Series.transform` would give incorrect results or raise when the argument ``func`` was dictionary (:issue:`35811`)
 - Bug in :meth:`Index.union` behaving differently depending on whether operand is a :class:`Index` or other list-like (:issue:`36384`)
 
 .. ---------------------------------------------------------------------------

pandas/core/aggregation.py

@@ -17,9 +17,17 @@
     Sequence,
     Tuple,
     Union,
+    cast,
 )
 
-from pandas._typing import AggFuncType, Axis, FrameOrSeries, Label
+from pandas._typing import (
+    AggFuncType,
+    AggFuncTypeBase,
+    Axis,
+    FrameOrSeries,
+    FrameOrSeriesUnion,
+    Label,
+)
 
 from pandas.core.dtypes.common import is_dict_like, is_list_like
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
@@ -391,7 +399,7 @@ def validate_func_kwargs(
 
 def transform(
     obj: FrameOrSeries, func: AggFuncType, axis: Axis, *args, **kwargs
-) -> FrameOrSeries:
+) -> FrameOrSeriesUnion:
     """
     Transform a DataFrame or Series
 
@@ -424,16 +432,20 @@ def transform(
         assert not is_series
         return transform(obj.T, func, 0, *args, **kwargs).T
 
-    if isinstance(func, list):
+    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 isinstance(func, dict):
+    if is_dict_like(func):
+        func = cast(Dict[Label, Union[AggFuncTypeBase, List[AggFuncTypeBase]]], 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:
@@ -451,37 +463,52 @@ def transform(
     return result
 
 
-def transform_dict_like(obj, func, *args, **kwargs):
+def transform_dict_like(
+    obj: FrameOrSeries,
+    func: Dict[Label, Union[AggFuncTypeBase, List[AggFuncTypeBase]]],
+    *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 = sorted(set(func.keys()) - set(obj.columns))
         if len(cols) > 0:
             raise SpecificationError(f"Column(s) {cols} do not exist")
 
-    if any(isinstance(v, dict) for v in func.values()):
+    # 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 = {}
+    results: Dict[Label, 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 e:
-            if str(e) == "Function did not transform":
-                raise e
+        except Exception as err:
+            if (
+                str(err) == "Function did not transform"
+                or str(err) == "No transform functions were provided"
+            ):
+                raise err
 
     # combine results
     if len(results) == 0:
         raise ValueError("Transform function failed")
     return concat(results, axis=1)
 
 
-def transform_str_or_callable(obj, func, *args, **kwargs):
+def transform_str_or_callable(
+    obj: FrameOrSeries, func: AggFuncTypeBase, *args, **kwargs
+) -> FrameOrSeriesUnion:
     """
     Compute transform in the case of a string or callable func
     """

pandas/core/frame.py

@@ -7270,7 +7270,7 @@ def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
 
     def _gotitem(
         self,
-        key: Union[str, List[str]],
+        key: Union[Label, List[Label]],
         ndim: int,
         subset: Optional[FrameOrSeriesUnion] = None,
     ) -> FrameOrSeriesUnion:

pandas/core/shared_docs.py

@@ -265,16 +265,17 @@
 
 Parameters
 ----------
-func : function, str, list or dict
+func : function, str, list-like or dict-like
     Function to use for transforming the data. If a function, must either
-    work when passed a {klass} or when passed to {klass}.apply.
+    work when passed a {klass} or when passed to {klass}.apply. If func
+    is both list-like and dict-like, dict-like behavior takes precedence.
 
     Accepted combinations are:
 
     - function
     - string function name
-    - list of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
-    - dict of axis labels -> functions, function names or list of such.
+    - list-like of functions and/or function names, e.g. ``[np.exp, 'sqrt']``
+    - dict-like of axis labels -> functions, function names or list-like of such.
 {axis}
 *args
     Positional arguments to pass to `func`.

pandas/tests/frame/apply/test_frame_transform.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 
-from pandas import DataFrame, MultiIndex
+from pandas import DataFrame, MultiIndex, Series
 import pandas._testing as tm
 from pandas.core.base import SpecificationError
 from pandas.core.groupby.base import transformation_kernels
@@ -41,9 +41,15 @@ def test_transform_groupby_kernel(axis, float_frame, op):
 
 
 @pytest.mark.parametrize(
-    "ops, names", [([np.sqrt], ["sqrt"]), ([np.abs, np.sqrt], ["absolute", "sqrt"])]
+    "ops, names",
+    [
+        ([np.sqrt], ["sqrt"]),
+        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
+        (np.array([np.sqrt]), ["sqrt"]),
+        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
+    ],
 )
-def test_transform_list(axis, float_frame, ops, names):
+def test_transform_listlike(axis, float_frame, ops, names):
     # GH 35964
     other_axis = 1 if axis in {0, "index"} else 0
     with np.errstate(all="ignore"):
@@ -56,18 +62,41 @@ def test_transform_list(axis, float_frame, ops, names):
     tm.assert_frame_equal(result, expected)
 
 
-def test_transform_dict(axis, float_frame):
+@pytest.mark.parametrize("ops", [[], np.array([])])
+def test_transform_empty_listlike(float_frame, ops):
+    with pytest.raises(ValueError, match="No transform functions were provided"):
+        float_frame.transform(ops)
+
+
+@pytest.mark.parametrize("box", [dict, Series])
+def test_transform_dictlike(axis, float_frame, box):
     # GH 35964
     if axis == 0 or axis == "index":
         e = float_frame.columns[0]
         expected = float_frame[[e]].transform(np.abs)
     else:
         e = float_frame.index[0]
         expected = float_frame.iloc[[0]].transform(np.abs)
-    result = float_frame.transform({e: np.abs}, axis=axis)
+    result = float_frame.transform(box({e: np.abs}), axis=axis)
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "ops",
+    [
+        {},
+        {"A": []},
+        {"A": [], "B": "cumsum"},
+        {"A": "cumsum", "B": []},
+        {"A": [], "B": ["cumsum"]},
+        {"A": ["cumsum"], "B": []},
+    ],
+)
+def test_transform_empty_dictlike(float_frame, ops):
+    with pytest.raises(ValueError, match="No transform functions were provided"):
+        float_frame.transform(ops)
+
+
 @pytest.mark.parametrize("use_apply", [True, False])
 def test_transform_udf(axis, float_frame, use_apply):
     # GH 35964

pandas/tests/series/apply/test_series_transform.py

@@ -34,9 +34,15 @@ def test_transform_groupby_kernel(string_series, op):
 
 
 @pytest.mark.parametrize(
-    "ops, names", [([np.sqrt], ["sqrt"]), ([np.abs, np.sqrt], ["absolute", "sqrt"])]
+    "ops, names",
+    [
+        ([np.sqrt], ["sqrt"]),
+        ([np.abs, np.sqrt], ["absolute", "sqrt"]),
+        (np.array([np.sqrt]), ["sqrt"]),
+        (np.array([np.abs, np.sqrt]), ["absolute", "sqrt"]),
+    ],
 )
-def test_transform_list(string_series, ops, names):
+def test_transform_listlike(string_series, ops, names):
     # GH 35964
     with np.errstate(all="ignore"):
         expected = concat([op(string_series) for op in ops], axis=1)
@@ -45,15 +51,38 @@ def test_transform_list(string_series, ops, names):
         tm.assert_frame_equal(result, expected)
 
 
-def test_transform_dict(string_series):
+@pytest.mark.parametrize("ops", [[], np.array([])])
+def test_transform_empty_listlike(string_series, ops):
+    with pytest.raises(ValueError, match="No transform functions were provided"):
+        string_series.transform(ops)
+
+
+@pytest.mark.parametrize("box", [dict, Series])
+def test_transform_dictlike(string_series, box):
     # GH 35964
     with np.errstate(all="ignore"):
         expected = concat([np.sqrt(string_series), np.abs(string_series)], axis=1)
     expected.columns = ["foo", "bar"]
-    result = string_series.transform({"foo": np.sqrt, "bar": np.abs})
+    result = string_series.transform(box({"foo": np.sqrt, "bar": np.abs}))
     tm.assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "ops",
+    [
+        {},
+        {"A": []},
+        {"A": [], "B": ["cumsum"]},
+        {"A": ["cumsum"], "B": []},
+        {"A": [], "B": "cumsum"},
+        {"A": "cumsum", "B": []},
+    ],
+)
+def test_transform_empty_dictlike(string_series, ops):
+    with pytest.raises(ValueError, match="No transform functions were provided"):
+        string_series.transform(ops)
+
+
 def test_transform_udf(axis, string_series):
     # GH 35964
     # via apply