ERR: Raise on invalid na_action in Series.map (#32790)

Author: dsaxton

doc/source/whatsnew/v1.1.0.rst

@@ -405,6 +405,7 @@ Other
 - Fixed :func:`pandas.testing.assert_series_equal` to correctly raise if left object is a different subclass with ``check_series_type=True`` (:issue:`32670`).
 - :meth:`IntegerArray.astype` now supports ``datetime64`` dtype (:issue:32538`)
 - Fixed bug in :func:`pandas.testing.assert_series_equal` where dtypes were checked for ``Interval`` and ``ExtensionArray`` operands when ``check_dtype`` was ``False`` (:issue:`32747`)
+- Bug in :meth:`Series.map` not raising on invalid ``na_action`` (:issue:`32815`)
 - Bug in :meth:`DataFrame.__dir__` caused a segfault when using unicode surrogates in a column name (:issue:`25509`)
 
 .. ---------------------------------------------------------------------------

pandas/core/base.py

@@ -1156,8 +1156,14 @@ def _map_values(self, mapper, na_action=None):
                 def map_f(values, f):
                     return lib.map_infer_mask(values, f, isna(values).view(np.uint8))
 
-            else:
+            elif na_action is None:
                 map_f = lib.map_infer
+            else:
+                msg = (
+                    "na_action must either be 'ignore' or None, "
+                    f"{na_action} was passed"
+                )
+                raise ValueError(msg)
 
         # mapper is a function
         new_values = map_f(values, mapper)

pandas/tests/series/test_apply.py

@@ -792,6 +792,13 @@ def test_map_float_to_string_precision(self):
         expected = {0: "0.3333333333333333"}
         assert result == expected
 
+    def test_map_with_invalid_na_action_raises(self):
+        # https://github.com/pandas-dev/pandas/issues/32815
+        s = pd.Series([1, 2, 3])
+        msg = "na_action must either be 'ignore' or None"
+        with pytest.raises(ValueError, match=msg):
+            s.map(lambda x: x, na_action="____")
+
     def test_apply_to_timedelta(self):
         list_of_valid_strings = ["00:00:01", "00:00:02"]
         a = pd.to_timedelta(list_of_valid_strings)