Backport PR #52566 on branch 2.0.x (BUG: DataFrame reductions casting…

… ts resolution always to nanoseconds) (#52618)

Backport PR #52566: BUG: DataFrame reductions casting ts resolution always to nanoseconds

Co-authored-by: Patrick Hoefler <61934744+phofl@users.noreply.github.com>

doc/source/whatsnew/v2.0.1.rst

@@ -28,6 +28,7 @@ Bug fixes
 - 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`)
+- Bug in :meth:`DataFrame.max` and related casting different :class:`Timestamp` resolutions always to nanoseconds (:issue:`52524`)
 - Bug in :meth:`ArrowDtype.__from_arrow__` not respecting if dtype is explicitly given (:issue:`52533`)
 - Bug in :func:`read_csv` casting PyArrow datetimes to NumPy when ``dtype_backend="pyarrow"`` and ``parse_dates`` is set causing a performance bottleneck in the process (:issue:`52546`)
 - Bug in :class:`arrays.DatetimeArray` constructor returning an incorrect unit when passed a non-nanosecond numpy datetime array (:issue:`52555`)

pandas/core/dtypes/cast.py

@@ -1414,9 +1414,9 @@ def find_common_type(types):
 
     # take lowest unit
     if all(is_datetime64_dtype(t) for t in types):
-        return np.dtype("datetime64[ns]")
+        return np.dtype(max(types))
     if all(is_timedelta64_dtype(t) for t in types):
-        return np.dtype("timedelta64[ns]")
+        return np.dtype(max(types))
 
     # don't mix bool / int or float or complex
     # this is different from numpy, which casts bool with float/int as int

pandas/tests/frame/test_reductions.py

@@ -1508,6 +1508,42 @@ def test_reductions_skipna_none_raises(
         with pytest.raises(ValueError, match=msg):
             getattr(obj, all_reductions)(skipna=None)
 
+    @td.skip_array_manager_invalid_test
+    def test_reduction_timestamp_smallest_unit(self):
+        # GH#52524
+        df = DataFrame(
+            {
+                "a": Series([Timestamp("2019-12-31")], dtype="datetime64[s]"),
+                "b": Series(
+                    [Timestamp("2019-12-31 00:00:00.123")], dtype="datetime64[ms]"
+                ),
+            }
+        )
+        result = df.max()
+        expected = Series(
+            [Timestamp("2019-12-31"), Timestamp("2019-12-31 00:00:00.123")],
+            dtype="datetime64[ms]",
+            index=["a", "b"],
+        )
+        tm.assert_series_equal(result, expected)
+
+    @td.skip_array_manager_not_yet_implemented
+    def test_reduction_timedelta_smallest_unit(self):
+        # GH#52524
+        df = DataFrame(
+            {
+                "a": Series([pd.Timedelta("1 days")], dtype="timedelta64[s]"),
+                "b": Series([pd.Timedelta("1 days")], dtype="timedelta64[ms]"),
+            }
+        )
+        result = df.max()
+        expected = Series(
+            [pd.Timedelta("1 days"), pd.Timedelta("1 days")],
+            dtype="timedelta64[ms]",
+            index=["a", "b"],
+        )
+        tm.assert_series_equal(result, expected)
+
 
 class TestNuisanceColumns:
     @pytest.mark.parametrize("method", ["any", "all"])