Backport PR #52458: BUG: tm.assert_series_equalcheck_dtype=False) wit…

…h different resos (#52526)

doc/source/whatsnew/v2.0.1.rst

@@ -24,6 +24,7 @@ Bug fixes
 - Bug in :func:`to_datetime` and :func:`to_timedelta` when trying to convert numeric data with a :class:`ArrowDtype` (:issue:`52425`)
 - Bug in :meth:`Series.describe` not returning :class:`ArrowDtype` with ``pyarrow.float64`` type with numeric data (:issue:`52427`)
 - Fixed segfault in :meth:`Series.to_numpy` with ``null[pyarrow]`` dtype (:issue:`52443`)
+- Bug in :func:`pandas.testing.assert_series_equal` where ``check_dtype=False`` would still raise for datetime or timedelta types with different resolutions (:issue:`52449`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_201.other:

pandas/_testing/asserters.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import operator
 from typing import (
     Literal,
     cast,
@@ -10,6 +11,7 @@
 from pandas._libs.missing import is_matching_na
 from pandas._libs.sparse import SparseIndex
 import pandas._libs.testing as _testing
+from pandas._libs.tslibs.np_datetime import compare_mismatched_resolutions
 
 from pandas.core.dtypes.common import (
     is_bool,
@@ -23,6 +25,7 @@
 )
 from pandas.core.dtypes.dtypes import (
     CategoricalDtype,
+    DatetimeTZDtype,
     PandasDtype,
 )
 from pandas.core.dtypes.missing import array_equivalent
@@ -736,6 +739,23 @@ def assert_extension_array_equal(
         and isinstance(right, DatetimeLikeArrayMixin)
         and type(right) == type(left)
     ):
+        # GH 52449
+        if not check_dtype and left.dtype.kind in "mM":
+            if not isinstance(left.dtype, np.dtype):
+                l_unit = cast(DatetimeTZDtype, left.dtype).unit
+            else:
+                l_unit = np.datetime_data(left.dtype)[0]
+            if not isinstance(right.dtype, np.dtype):
+                r_unit = cast(DatetimeTZDtype, left.dtype).unit
+            else:
+                r_unit = np.datetime_data(right.dtype)[0]
+            if (
+                l_unit != r_unit
+                and compare_mismatched_resolutions(
+                    left._ndarray, right._ndarray, operator.eq
+                ).all()
+            ):
+                return
         # Avoid slow object-dtype comparisons
         # np.asarray for case where we have a np.MaskedArray
         assert_numpy_array_equal(

pandas/tests/util/test_assert_series_equal.py

@@ -404,3 +404,21 @@ def test_identical_nested_series_is_equal():
     tm.assert_series_equal(x, x, check_exact=True)
     tm.assert_series_equal(x, y)
     tm.assert_series_equal(x, y, check_exact=True)
+
+
+@pytest.mark.parametrize("dtype", ["datetime64", "timedelta64"])
+def test_check_dtype_false_different_reso(dtype):
+    # GH 52449
+    ser_s = Series([1000213, 2131232, 21312331]).astype(f"{dtype}[s]")
+    ser_ms = ser_s.astype(f"{dtype}[ms]")
+    with pytest.raises(AssertionError, match="Attributes of Series are different"):
+        tm.assert_series_equal(ser_s, ser_ms)
+    tm.assert_series_equal(ser_ms, ser_s, check_dtype=False)
+
+    ser_ms -= Series([1, 1, 1]).astype(f"{dtype}[ms]")
+
+    with pytest.raises(AssertionError, match="Series are different"):
+        tm.assert_series_equal(ser_s, ser_ms)
+
+    with pytest.raises(AssertionError, match="Series are different"):
+        tm.assert_series_equal(ser_s, ser_ms, check_dtype=False)