BUG: Fix error in replace with strings that are large numbers (#25616)

doc/source/whatsnew/v0.24.2.rst

@@ -32,6 +32,7 @@ Fixed Regressions
 - Fixed regression in creating a period-dtype array from a read-only NumPy array of period objects. (:issue:`25403`)
 - Fixed regression in :class:`Categorical`, where constructing it from a categorical ``Series`` and an explicit ``categories=`` that differed from that in the ``Series`` created an invalid object which could trigger segfaults. (:issue:`25318`)
 - Fixed pip installing from source into an environment without NumPy (:issue:`25193`)
+- Fixed regression in :meth:`DataFrame.replace` where large strings of numbers would be coerced into ``int64``, causing an ``OverflowError`` (:issue:`25616`)
 
 .. _whatsnew_0242.enhancements:
 

pandas/core/internals/blocks.py

@@ -1079,7 +1079,7 @@ def coerce_to_target_dtype(self, other):
 
         try:
             return self.astype(dtype)
-        except (ValueError, TypeError):
+        except (ValueError, TypeError, OverflowError):
             pass
 
         return self.astype(object)
@@ -3210,7 +3210,7 @@ def _putmask_smart(v, m, n):
                 nv = v.copy()
                 nv[m] = nn_at
                 return nv
-    except (ValueError, IndexError, TypeError):
+    except (ValueError, IndexError, TypeError, OverflowError):
         pass
 
     n = np.asarray(n)

pandas/tests/series/test_replace.py

@@ -280,3 +280,17 @@ def test_replace_mixed_types_with_string(self):
         result = s.replace([2, '4'], np.nan)
         expected = pd.Series([1, np.nan, 3, np.nan, 4, 5])
         tm.assert_series_equal(expected, result)
+
+    def test_replace_with_no_overflowerror(self):
+        # GH 25616
+        # casts to object without Exception from OverflowError
+        s = pd.Series([0, 1, 2, 3, 4])
+        result = s.replace([3], ['100000000000000000000'])
+        expected = pd.Series([0, 1, 2, '100000000000000000000', 4])
+        tm.assert_series_equal(result, expected)
+
+        s = pd.Series([0, '100000000000000000000',
+                      '100000000000000000001'])
+        result = s.replace(['100000000000000000000'], [1])
+        expected = pd.Series([0, 1, '100000000000000000001'])
+        tm.assert_series_equal(result, expected)