BUG: Catch overflow error when converting to datetime (#45506)

Exception is raised when a part of the date like day, month or year is greater
than 32 bit signed integer.

Added tests for this issue in pandas/tests/frame/methods/test_astype.py

TODO: Remove this when its fixed in Python

Author: Compro-Prasad

pandas/core/arrays/datetimes.py

@@ -37,7 +37,10 @@
     tzconversion,
 )
 from pandas._typing import npt
-from pandas.errors import PerformanceWarning
+from pandas.errors import (
+    OutOfBoundsDatetime,
+    PerformanceWarning,
+)
 from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_inclusive
 
@@ -2215,6 +2218,10 @@ def objects_to_datetime64ns(
             return values.view("i8"), tz_parsed
         except (ValueError, TypeError):
             raise err
+    except OverflowError as err:
+        # Exception is raised when a part of date is greater than 32 bit signed int
+        # TODO: Remove this when its fixed in Python
+        raise OutOfBoundsDatetime("datetime out of range") from err
 
     if tz_parsed is not None:
         # We can take a shortcut since the datetime64 numpy array

pandas/tests/frame/methods/test_compare.py

@@ -180,3 +180,28 @@ def test_compare_unaligned_objects():
         df1 = pd.DataFrame(np.ones((3, 3)))
         df2 = pd.DataFrame(np.zeros((2, 1)))
         df1.compare(df2)
+
+
+def test_compare_datetime64_and_string():
+    # Issue https://github.com/pandas-dev/pandas/issues/45506
+    # Catch OverflowError when comparing datetime64 and string
+    data = [
+        {"a": "2015-07-01", "b": "08335394550"},
+        {"a": "2015-07-02", "b": "+49 (0) 0345 300033"},
+        {"a": "2015-07-03", "b": "+49(0)2598 04457"},
+        {"a": "2015-07-04", "b": "0741470003"},
+        {"a": "2015-07-05", "b": "04181 83668"},
+    ]
+    dtypes = {"a": "datetime64[ns]", "b": "string"}
+    df = DataFrame(data=data).astype(dtypes)
+
+    result_eq1 = df["a"].eq(df["b"])
+    result_eq2 = df["a"] == df["b"]
+    result_neq = df["a"] != df["b"]
+
+    expected_eq = Series([False] * 5)  # For .eq and ==
+    expected_neq = Series([True] * 5)  # For !=
+
+    tm.assert_series_equal(result_eq1, expected_eq)
+    tm.assert_series_equal(result_eq2, expected_eq)
+    tm.assert_series_equal(result_neq, expected_neq)