CLN: Enforce deprecation of Series.agg using Series.apply

doc/source/whatsnew/v3.0.0.rst

@@ -119,6 +119,7 @@ Removal of prior version deprecations/changes
 - Removed ``pandas.value_counts``, use :meth:`Series.value_counts` instead (:issue:`53493`)
 - Removed ``read_gbq`` and ``DataFrame.to_gbq``. Use ``pandas_gbq.read_gbq`` and ``pandas_gbq.to_gbq`` instead https://pandas-gbq.readthedocs.io/en/latest/api.html (:issue:`55525`)
 - Removed deprecated argument ``obj`` in :meth:`.DataFrameGroupBy.get_group` and :meth:`.SeriesGroupBy.get_group` (:issue:`53545`)
+- Removed deprecated behavior of :meth:`Series.agg` using :meth:`Series.apply` (:issue:`53325`)
 - Removed the ``ArrayManager`` (:issue:`55043`)
 - Removed the ``fastpath`` argument from the :class:`Series` constructor (:issue:`55466`)
 - Removed the ``is_boolean``, ``is_integer``, ``is_floating``, ``holds_integer``, ``is_numeric``, ``is_categorical``, ``is_object``, and ``is_interval`` attributes of :class:`Index` (:issue:`50042`)

pandas/core/apply.py

@@ -1430,22 +1430,7 @@ def agg(self):
             func = self.func
             # string, list-like, and dict-like are entirely handled in super
             assert callable(func)
-
-            # GH53325: The setup below is just to keep current behavior while emitting a
-            # deprecation message. In the future this will all be replaced with a simple
-            # `result = f(self.obj, *self.args, **self.kwargs)`.
-            try:
-                result = obj.apply(func, args=self.args, **self.kwargs)
-            except (ValueError, AttributeError, TypeError):
-                result = func(obj, *self.args, **self.kwargs)
-            else:
-                msg = (
-                    f"using {func} in {type(obj).__name__}.agg cannot aggregate and "
-                    f"has been deprecated. Use {type(obj).__name__}.transform to "
-                    f"keep behavior unchanged."
-                )
-                warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
-
+            result = func(obj, *self.args, **self.kwargs)
         return result
 
     def apply_empty_result(self) -> Series:

pandas/tests/apply/test_frame_apply.py

@@ -1688,18 +1688,14 @@ def foo2(x, b=2, c=0):
     expected = df + 7
     tm.assert_frame_equal(result, expected)
 
-    msg = "using .+ in Series.agg cannot aggregate and"
-
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.agg([foo1, foo2], 0, 3, c=4)
+    result = df.agg([foo1, foo2], 0, 3, c=4)
     expected = DataFrame(
         [[8, 8], [9, 9], [10, 10]], columns=[["x", "x"], ["foo1", "foo2"]]
     )
     tm.assert_frame_equal(result, expected)
 
     # TODO: the result below is wrong, should be fixed (GH53325)
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = df.agg({"x": foo1}, 0, 3, c=4)
+    result = df.agg({"x": foo1}, 0, 3, c=4)
     expected = DataFrame([2, 3, 4], columns=["x"])
     tm.assert_frame_equal(result, expected)
 

pandas/tests/apply/test_series_apply.py

@@ -104,12 +104,7 @@ def f(x, a=0, b=0, c=0):
         return x + a + 10 * b + 100 * c
 
     s = Series([1, 2])
-    msg = (
-        "in Series.agg cannot aggregate and has been deprecated. "
-        "Use Series.transform to keep behavior unchanged."
-    )
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = s.agg(f, 0, *args, **kwargs)
+    result = s.agg(f, 0, *args, **kwargs)
     expected = s + increment
     tm.assert_series_equal(result, expected)
 
@@ -124,13 +119,9 @@ def foo1(x, a=1, c=0):
     def foo2(x, b=2, c=0):
         return x + b + c
 
-    msg = "using .+ in Series.agg cannot aggregate and"
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        s.agg(foo1, 0, 3, c=4)
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        s.agg([foo1, foo2], 0, 3, c=4)
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        s.agg({"a": foo1, "b": foo2}, 0, 3, c=4)
+    s.agg(foo1, 0, 3, c=4)
+    s.agg([foo1, foo2], 0, 3, c=4)
+    s.agg({"a": foo1, "b": foo2}, 0, 3, c=4)
 
 
 def test_series_apply_map_box_timestamps(by_row):
@@ -410,34 +401,26 @@ def test_apply_map_evaluate_lambdas_the_same(string_series, func, by_row):
 
 def test_agg_evaluate_lambdas(string_series):
     # GH53325
-    # in the future, the result will be a Series class.
-
-    with tm.assert_produces_warning(FutureWarning):
-        result = string_series.agg(lambda x: type(x))
-    assert isinstance(result, Series) and len(result) == len(string_series)
+    result = string_series.agg(lambda x: type(x))
+    assert result is Series
 
-    with tm.assert_produces_warning(FutureWarning):
-        result = string_series.agg(type)
-    assert isinstance(result, Series) and len(result) == len(string_series)
+    result = string_series.agg(type)
+    assert result is Series
 
 
 @pytest.mark.parametrize("op_name", ["agg", "apply"])
 def test_with_nested_series(datetime_series, op_name):
-    # GH 2316
+    # GH 2316 & GH52123
     # .agg with a reducer and a transform, what to do
-    msg = "cannot aggregate"
-    warning = FutureWarning if op_name == "agg" else None
-    with tm.assert_produces_warning(warning, match=msg):
-        # GH52123
-        result = getattr(datetime_series, op_name)(
-            lambda x: Series([x, x**2], index=["x", "x^2"])
-        )
-    expected = DataFrame({"x": datetime_series, "x^2": datetime_series**2})
-    tm.assert_frame_equal(result, expected)
-
-    with tm.assert_produces_warning(FutureWarning, match=msg):
-        result = datetime_series.agg(lambda x: Series([x, x**2], index=["x", "x^2"]))
-    tm.assert_frame_equal(result, expected)
+    result = getattr(datetime_series, op_name)(
+        lambda x: Series([x, x**2], index=["x", "x^2"])
+    )
+    if op_name == "apply":
+        expected = DataFrame({"x": datetime_series, "x^2": datetime_series**2})
+        tm.assert_frame_equal(result, expected)
+    else:
+        expected = Series([datetime_series, datetime_series**2], index=["x", "x^2"])
+        tm.assert_series_equal(result, expected)
 
 
 def test_replicate_describe(string_series):