BUG: pd.replace changes dtype when using a dict to replace values even when there are no matches

doc/source/whatsnew/v2.1.0.rst

@@ -546,6 +546,7 @@ Other
 - Bug in :func:`assert_almost_equal` now throwing assertion error for two unequal sets (:issue:`51727`)
 - Bug in :func:`assert_frame_equal` checks category dtypes even when asked not to check index type (:issue:`52126`)
 - Bug in :meth:`DataFrame.reindex` with a ``fill_value`` that should be inferred with a :class:`ExtensionDtype` incorrectly inferring ``object`` dtype (:issue:`52586`)
+- Bug in :meth:`DataFrame.replace` changing column dtypes even when no replacements are made (:issue:`53539`)
 - Bug in :meth:`DataFrame.shift` and :meth:`Series.shift` when passing both "freq" and "fill_value" silently ignoring "fill_value" instead of raising ``ValueError`` (:issue:`53832`)
 - Bug in :meth:`DataFrame.shift` with ``axis=1`` on a :class:`DataFrame` with a single :class:`ExtensionDtype` column giving incorrect results (:issue:`53832`)
 - Bug in :meth:`Series.align`, :meth:`DataFrame.align`, :meth:`Series.reindex`, :meth:`DataFrame.reindex`, :meth:`Series.interpolate`, :meth:`DataFrame.interpolate`, incorrectly failing to raise with method="asfreq" (:issue:`53620`)

pandas/core/array_algos/replace.py

@@ -23,6 +23,7 @@
 if TYPE_CHECKING:
     from pandas._typing import (
         ArrayLike,
+        NDFrame,
         Scalar,
         npt,
     )
@@ -150,3 +151,20 @@ def re_replacer(s):
         values[:] = f(values)
     else:
         values[mask] = f(values[mask])
+
+
+def keep_original_dtypes(result: NDFrame, original: NDFrame) -> NDFrame:
+    """
+    Keep same data types as the original input if no replacements have been made.
+
+    Parameters
+    ----------
+    result: NDFrame
+    original: NDFrame
+    """
+    # Perform operation only if input is a dataframe, not a series
+    if hasattr(original, "columns"):
+        for col in original.columns:
+            if result[col].astype(str).equals(original[col].astype(str)):
+                result[col] = result[col].astype(original[col].dtypes)
+    return result

pandas/core/generic.py

@@ -149,7 +149,10 @@
     nanops,
     sample,
 )
-from pandas.core.array_algos.replace import should_use_regex
+from pandas.core.array_algos.replace import (
+    keep_original_dtypes,
+    should_use_regex,
+)
 from pandas.core.arrays import ExtensionArray
 from pandas.core.base import PandasObject
 from pandas.core.construction import extract_array
@@ -7594,6 +7597,12 @@ def replace(
             )
 
         inplace = validate_bool_kwarg(inplace, "inplace")
+        if inplace:
+            # When replace is called on a copy of a dataframe with inplace set to True,
+            # self gets overridden when new_data is generated. This allows for a copy
+            # of the original dataframe to be retained, so if there are no replacements
+            # the original data types can be kept and returned
+            original_df = self.copy()
         if not is_bool(regex) and to_replace is not None:
             raise ValueError("'to_replace' must be 'None' if 'regex' is not a bool")
 
@@ -7763,8 +7772,10 @@ def replace(
 
         result = self._constructor_from_mgr(new_data, axes=new_data.axes)
         if inplace:
+            result = keep_original_dtypes(result, original_df)
             return self._update_inplace(result)
         else:
+            result = keep_original_dtypes(result, self)
             return result.__finalize__(self, method="replace")
 
     @final

pandas/tests/frame/methods/test_replace.py

@@ -1515,6 +1515,12 @@ def test_replace_with_nil_na(self):
         result = ser.replace("nil", "anything else")
         tm.assert_frame_equal(expected, result)
 
+    def test_replace_no_replacements_dtypes(self):
+        # GH 53539
+        df = DataFrame({"a": [1, 2], "b": [3, 4]}, dtype="object")
+        res = df.replace({5: 0})
+        tm.assert_frame_equal(df, res)
+
 
 class TestDataFrameReplaceRegex:
     @pytest.mark.parametrize(