BUG: Series.replace does not preserve dtype of original Series

pandas/core/internals/blocks.py

@@ -1165,6 +1165,13 @@ def coerce_to_target_dtype(self, other):
         if is_dtype_equal(self.dtype, dtype):
             return self
 
+        if self._can_hold_element(other):
+            return self
+        elif self.is_categorical:
+            # Note: this will be wrong if we ever have a tuple category
+            cat = self.values.add_categories(other)
+            return self.make_block(cat)
+
         if self.is_bool or is_object_dtype(dtype) or is_bool_dtype(dtype):
             # we don't upcast to bool
             return self.astype(object)
@@ -2592,6 +2599,26 @@ def replace(
         result.values.replace(to_replace, value, inplace=True)
         return [result]
 
+    def _can_hold_element(self, element: Any) -> bool:
+        try:
+            self.values._validate_setitem_value(element)
+            return True
+        except (TypeError, ValueError):
+            return False
+
+    def putmask(
+        self, mask, new, inplace: bool = False, axis: int = 0, transpose: bool = False
+    ) -> List["Block"]:
+        if self._can_hold_element(new):
+            return super().putmask(mask, new, inplace, axis, transpose)
+
+        # TODO: should this be inplace?
+        # TODO: use coerce_to_target_dtype?
+        cat = self.values.add_categories(new)
+        nb = self.make_block(cat)
+        assert nb._can_hold_element(new)
+        return nb.putmask(mask, new, inplace, axis, transpose)
+
 
 # -----------------------------------------------------------------
 # Constructor Helpers

pandas/tests/series/methods/test_replace.py

@@ -5,6 +5,7 @@
 
 import pandas as pd
 import pandas._testing as tm
+from pandas.core.arrays import IntervalArray
 
 
 class TestSeriesReplace:
@@ -259,7 +260,7 @@ def test_replace2(self):
     def test_replace_with_dictlike_and_string_dtype(self):
         # GH 32621
         s = pd.Series(["one", "two", np.nan], dtype="string")
-        expected = pd.Series(["1", "2", np.nan])
+        expected = pd.Series(["1", "2", np.nan], dtype="string")
         result = s.replace({"one": "1", "two": "2"})
         tm.assert_series_equal(expected, result)
 
@@ -460,3 +461,39 @@ def test_str_replace_regex_default_raises_warning(self, pattern):
         with tm.assert_produces_warning(FutureWarning, check_stacklevel=False) as w:
             s.str.replace(pattern, "")
             assert re.match(msg, str(w[0].message))
+
+    @pytest.mark.parametrize(
+        "dtype, input_data, to_replace, expected_data",
+        [
+            ("bool", [True, False], {True: False}, [False, False]),
+            ("int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
+            ("Int64", [1, 2], {1: 10, 2: 20}, [10, 20]),
+            ("float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
+            ("Float64", [1.1, 2.2], {1.1: 10.1, 2.2: 20.5}, [10.1, 20.5]),
+            ("string", ["one", "two"], {"one": "1", "two": "2"}, ["1", "2"]),
+            (
+                pd.IntervalDtype("int64"),
+                IntervalArray([pd.Interval(1, 2), pd.Interval(2, 3)]),
+                {pd.Interval(1, 2): pd.Interval(10, 20)},
+                IntervalArray([pd.Interval(10, 20), pd.Interval(2, 3)]),
+            ),
+            (
+                pd.IntervalDtype("float64"),
+                IntervalArray([pd.Interval(1.0, 2.7), pd.Interval(2.8, 3.1)]),
+                {pd.Interval(1.0, 2.7): pd.Interval(10.6, 20.8)},
+                IntervalArray([pd.Interval(10.6, 20.8), pd.Interval(2.8, 3.1)]),
+            ),
+            (
+                pd.PeriodDtype("M"),
+                [pd.Period("2020-05", freq="M")],
+                {pd.Period("2020-05", freq="M"): pd.Period("2020-06", freq="M")},
+                [pd.Period("2020-06", freq="M")],
+            ),
+        ],
+    )
+    def test_replace_dtype(self, dtype, input_data, to_replace, expected_data):
+        # GH 33484
+        s = pd.Series(input_data, dtype=dtype)
+        result = s.replace(to_replace)
+        expected = pd.Series(expected_data, dtype=dtype)
+        tm.assert_series_equal(result, expected)