BUG: Changing way of applying numpy functions on Series

pandas/core/series.py

@@ -3872,7 +3872,7 @@ def transform(self, func, axis=0, *args, **kwargs):
         self._get_axis_number(axis)
         return super().transform(func, *args, **kwargs)
 
-    def apply(self, func, convert_dtype=True, args=(), **kwds):
+    def apply(self, func, convert_dtype=True, force_mapping=False, args=(), **kwds):
         """
         Invoke function on values of Series.
 
@@ -3886,6 +3886,9 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
         convert_dtype : bool, default True
             Try to find better dtype for elementwise function results. If
             False, leave as dtype=object.
+        force_mapping : bool, default False
+            If set to True, forces func to be called on each element separately.
+            Useful when using numpy functions.
         args : tuple
             Positional arguments passed to func after the series value.
         **kwds
@@ -3992,9 +3995,8 @@ def f(x):
             f = func
 
         with np.errstate(all="ignore"):
-            if isinstance(f, np.ufunc):
+            if not force_mapping and isinstance(f, np.ufunc):
                 return f(self)
-
             # row-wise access
             if is_extension_array_dtype(self.dtype) and hasattr(self._values, "map"):
                 # GH#23179 some EAs do not have `map`

pandas/tests/series/test_apply.py

@@ -813,3 +813,11 @@ def test_apply_to_timedelta(self):
         b = Series(list_of_strings).apply(pd.to_timedelta)  # noqa
         # Can't compare until apply on a Series gives the correct dtype
         # assert_series_equal(a, b)
+
+    def test_apply_to_numpy_func_with_force_mapping(self):
+        s = pd.Series([np.array([0.1, 0.2], dtype=float)])
+        expected = pd.Series(
+            [np.array([0.09983341664682815, 0.19866933079506122], dtype=float)]
+        )
+        result = s.apply(np.sin, force_mapping=True)
+        tm.assert_series_equal(result, expected)