BUG: replace raising RecursionError (#50760)

* BUG: replace raising RecursionError

* Fix other replace bug

Author: phofl

doc/source/whatsnew/v2.0.0.rst

@@ -981,6 +981,8 @@ Missing
 - Bug in :meth:`Series.map` caused incorrect result when data has NaNs and defaultdict mapping was used (:issue:`48813`)
 - Bug in :class:`NA` raising a ``TypeError`` instead of return :class:`NA` when performing a binary operation with a ``bytes`` object (:issue:`49108`)
 - Bug in :meth:`DataFrame.update` with ``overwrite=False`` raising ``TypeError`` when ``self`` has column with ``NaT`` values and column not present in ``other`` (:issue:`16713`)
+- Bug in :meth:`Series.replace` raising ``RecursionError`` when replacing value in object-dtype :class:`Series` containing ``NA`` (:issue:`47480`)
+- Bug in :meth:`Series.replace` raising ``RecursionError`` when replacing value in numeric :class:`Series` with ``NA`` (:issue:`50758`)
 
 MultiIndex
 ^^^^^^^^^^

pandas/core/internals/blocks.py

@@ -21,6 +21,7 @@
     writers,
 )
 from pandas._libs.internals import BlockPlacement
+from pandas._libs.missing import NA
 from pandas._libs.tslibs import IncompatibleFrequency
 from pandas._typing import (
     ArrayLike,
@@ -569,7 +570,7 @@ def replace(
             return blocks
 
         elif self.ndim == 1 or self.shape[0] == 1:
-            if value is None:
+            if value is None or value is NA:
                 blk = self.astype(np.dtype(object))
             else:
                 blk = self.coerce_to_target_dtype(value)

pandas/core/missing.py

@@ -32,6 +32,7 @@
 from pandas.core.dtypes.common import (
     is_array_like,
     is_numeric_v_string_like,
+    is_object_dtype,
     needs_i8_conversion,
 )
 from pandas.core.dtypes.missing import (
@@ -83,6 +84,12 @@ def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
     # _DTypeDict, Tuple[Any, Any]]]"
     values_to_mask = np.array(values_to_mask, dtype=dtype)  # type: ignore[arg-type]
 
+    potential_na = False
+    if is_object_dtype(arr):
+        # pre-compute mask to avoid comparison to NA
+        potential_na = True
+        arr_mask = ~isna(arr)
+
     na_mask = isna(values_to_mask)
     nonna = values_to_mask[~na_mask]
 
@@ -93,10 +100,15 @@ def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
             # GH#29553 prevent numpy deprecation warnings
             pass
         else:
-            new_mask = arr == x
-            if not isinstance(new_mask, np.ndarray):
-                # usually BooleanArray
-                new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
+            if potential_na:
+                new_mask = np.zeros(arr.shape, dtype=np.bool_)
+                new_mask[arr_mask] = arr[arr_mask] == x
+            else:
+                new_mask = arr == x
+
+                if not isinstance(new_mask, np.ndarray):
+                    # usually BooleanArray
+                    new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
             mask |= new_mask
 
     if na_mask.any():

pandas/tests/series/methods/test_replace.py

@@ -690,3 +690,25 @@ def test_replace_change_dtype_series(self):
         df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
         df["Test"] = df["Test"].fillna(np.nan)
         tm.assert_frame_equal(df, expected)
+
+    @pytest.mark.parametrize("dtype", ["object", "Int64"])
+    def test_replace_na_in_obj_column(self, dtype):
+        # GH#47480
+        ser = pd.Series([0, 1, pd.NA], dtype=dtype)
+        expected = pd.Series([0, 2, pd.NA], dtype=dtype)
+        result = ser.replace(to_replace=1, value=2)
+        tm.assert_series_equal(result, expected)
+
+        ser.replace(to_replace=1, value=2, inplace=True)
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize("val", [0, 0.5])
+    def test_replace_numeric_column_with_na(self, val):
+        # GH#50758
+        ser = pd.Series([val, 1])
+        expected = pd.Series([val, pd.NA])
+        result = ser.replace(to_replace=1, value=pd.NA)
+        tm.assert_series_equal(result, expected)
+
+        ser.replace(to_replace=1, value=pd.NA, inplace=True)
+        tm.assert_series_equal(ser, expected)