Fixes #45506 Catch overflow error when converting to datetime (#45532)

Author: Compro-Prasad

doc/source/whatsnew/v1.5.0.rst

@@ -247,7 +247,7 @@ Conversion
 - Bug in :meth:`Float64Index.astype` to unsigned integer dtype incorrectly casting to ``np.int64`` dtype (:issue:`45309`)
 - Bug in :meth:`Series.astype` and :meth:`DataFrame.astype` from floating dtype to unsigned integer dtype failing to raise in the presence of negative values (:issue:`45151`)
 - Bug in :func:`array` with ``FloatingDtype`` and values containing float-castable strings incorrectly raising (:issue:`45424`)
--
+- Bug when comparing string and datetime64ns objects causing ``OverflowError`` exception. (:issue:`45506`)
 
 Strings
 ^^^^^^^

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 +2218,9 @@ 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
+ raise OutOfBoundsDatetime("Out of bounds nanosecond timestamp") from err
 
  if tz_parsed is not None:
  # We can take a shortcut since the datetime64 numpy array

pandas/tests/series/methods/test_compare.py

@@ -114,3 +114,28 @@ def test_compare_unaligned_objects():
  ser1 = pd.Series([1, 2, 3])
  ser2 = pd.Series([1, 2, 3, 4])
  ser1.compare(ser2)
+
+
+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 = pd.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 = pd.Series([False] * 5) # For .eq and ==
+ expected_neq = pd.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)