BUG: Series.replace with to_replace dict and value=None, nonsensical error message

pandas/core/generic.py

@@ -6594,6 +6594,7 @@ def replace(
         regex=False,
         method=lib.no_default,
     ):
+
         if not (
             is_scalar(to_replace)
             or is_re_compilable(to_replace)
@@ -6701,6 +6702,33 @@ def replace(
 
                 # {'A': NA} -> 0
                 elif not is_list_like(value):
+
+                    items = list(to_replace.items())
+                    keys, values = zip(*items) if items else ([], [])
+                    if items and (value is None or value is not lib.no_default):
+                        # e.g ({1:5}, value = None),
+                        # ({1:5}, value = 10), ({1:5}, value="")
+
+                        col = keys[0]
+                        if col in self and isinstance(self, ABCDataFrame):
+                            # e.g Nested dict in df {"a": {1: 5}}, value=10),
+                            # or columnwise with None value {"a": 1}, value=None
+                            print("col in self")
+                            mapping = {
+                                col: (to_replace.values(), value)
+                                for col in to_replace.keys()
+                                if col in self
+                            }
+                            return self._replace_columnwise(mapping, inplace, regex)
+
+                        return self.replace(
+                            to_replace=keys,
+                            value=values,
+                            inplace=inplace,
+                            limit=limit,
+                            regex=regex,
+                        )
+
                     # Operate column-wise
                     if self.ndim == 1:
                         raise ValueError(
@@ -6748,7 +6776,6 @@ def replace(
                     regex, value, inplace=inplace, limit=limit, regex=True
                 )
             else:
-
                 # dest iterable dict-like
                 if is_dict_like(value):  # NA -> {'A' : 0, 'B' : -1}
                     # Operate column-wise

pandas/tests/series/methods/test_replace.py

@@ -557,6 +557,14 @@ def test_pandas_replace_na(self):
         exp = pd.Series(["CC", "CC", "CC-REPL", "DD", "CC", "", pd.NA], dtype="string")
         tm.assert_series_equal(result, exp)
 
+    def test_replace_dictlike_value_None(self):
+        # GH46004
+        ser = pd.Series([1, 2, 3, 4])
+        result = ser.replace(to_replace={3: 33}, value=None)
+
+        exp = pd.Series([1, 2, 33, 4])
+        tm.assert_series_equal(result, exp)
+
     @pytest.mark.parametrize(
         "dtype, input_data, to_replace, expected_data",
         [