Backport PR #48234 on branch 1.5.x (REGR: Fix regression RecursionError when replacing numeric scalar with None)

doc/source/whatsnew/v1.5.2.rst

@@ -13,7 +13,7 @@ including other versions of pandas.
 
 Fixed regressions
 ~~~~~~~~~~~~~~~~~
--
+- Fixed regression in :meth:`Series.replace` raising ``RecursionError`` with numeric dtype and when specifying ``value=None`` (:issue:`45725`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/internals/blocks.py

@@ -569,7 +569,6 @@ def replace(
         # Note: the checks we do in NDFrame.replace ensure we never get
         #  here with listlike to_replace or value, as those cases
         #  go through replace_list
-
         values = self.values
 
         if isinstance(values, Categorical):
@@ -608,7 +607,10 @@ def replace(
             return blocks
 
         elif self.ndim == 1 or self.shape[0] == 1:
-            blk = self.coerce_to_target_dtype(value)
+            if value is None:
+                blk = self.astype(np.dtype(object))
+            else:
+                blk = self.coerce_to_target_dtype(value)
             return blk.replace(
                 to_replace=to_replace,
                 value=value,

pandas/tests/frame/methods/test_replace.py

@@ -1496,6 +1496,18 @@ def test_replace_list_with_mixed_type(
         result = obj.replace(box(to_replace), value)
         tm.assert_equal(result, expected)
 
+    @pytest.mark.parametrize("val", [2, np.nan, 2.0])
+    def test_replace_value_none_dtype_numeric(self, val):
+        # GH#48231
+        df = DataFrame({"a": [1, val]})
+        result = df.replace(val, None)
+        expected = DataFrame({"a": [1, None]}, dtype=object)
+        tm.assert_frame_equal(result, expected)
+
+        df = DataFrame({"a": [1, val]})
+        result = df.replace({val: None})
+        tm.assert_frame_equal(result, expected)
+
 
 class TestDataFrameReplaceRegex:
     @pytest.mark.parametrize(

pandas/tests/series/methods/test_replace.py

@@ -667,3 +667,11 @@ def test_replace_different_int_types(self, any_int_numpy_dtype):
         result = labs.replace(map_dict)
         expected = labs.replace({0: 0, 2: 1, 1: 2})
         tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val", [2, np.nan, 2.0])
+    def test_replace_value_none_dtype_numeric(self, val):
+        # GH#48231
+        ser = pd.Series([1, val])
+        result = ser.replace(val, None)
+        expected = pd.Series([1, None], dtype=object)
+        tm.assert_series_equal(result, expected)