BUG: .max() gives wrong answer for non-nanosecond datetimeindex (#50719)

* fix max non-nano

* match dtype in NaT case too

* check unit in test

Co-authored-by: MarcoGorelli <>

Author: MarcoGorelli

pandas/core/nanops.py

@@ -370,9 +370,9 @@ def _wrap_results(result, dtype: np.dtype, fill_value=None):
                 result = np.nan
 
             if isna(result):
-                result = np.datetime64("NaT", "ns")
+                result = np.datetime64("NaT", "ns").astype(dtype)
             else:
-                result = np.int64(result).view("datetime64[ns]")
+                result = np.int64(result).view(dtype)
             # retain original unit
             result = result.astype(dtype, copy=False)
         else:

pandas/tests/arrays/datetimes/test_reductions.py

@@ -10,6 +10,10 @@
 
 
 class TestReductions:
+    @pytest.fixture(params=["s", "ms", "us", "ns"])
+    def unit(self, request):
+        return request.param
+
     @pytest.fixture
     def arr1d(self, tz_naive_fixture):
         """Fixture returning DatetimeArray with parametrized timezones"""
@@ -28,17 +32,20 @@ def arr1d(self, tz_naive_fixture):
         )
         return arr
 
-    def test_min_max(self, arr1d):
+    def test_min_max(self, arr1d, unit):
         arr = arr1d
+        arr = arr.as_unit(unit)
         tz = arr.tz
 
         result = arr.min()
-        expected = pd.Timestamp("2000-01-02", tz=tz)
+        expected = pd.Timestamp("2000-01-02", tz=tz).as_unit(unit)
         assert result == expected
+        assert result.unit == expected.unit
 
         result = arr.max()
-        expected = pd.Timestamp("2000-01-05", tz=tz)
+        expected = pd.Timestamp("2000-01-05", tz=tz).as_unit(unit)
         assert result == expected
+        assert result.unit == expected.unit
 
         result = arr.min(skipna=False)
         assert result is NaT

pandas/tests/test_nanops.py

@@ -1157,10 +1157,14 @@ def prng(self):
 
 
 class TestDatetime64NaNOps:
+    @pytest.fixture(params=["s", "ms", "us", "ns"])
+    def unit(self, request):
+        return request.param
+
     # Enabling mean changes the behavior of DataFrame.mean
     # See https://github.com/pandas-dev/pandas/issues/24752
-    def test_nanmean(self):
-        dti = pd.date_range("2016-01-01", periods=3)
+    def test_nanmean(self, unit):
+        dti = pd.date_range("2016-01-01", periods=3).as_unit(unit)
         expected = dti[1]
 
         for obj in [dti, DatetimeArray(dti), Series(dti)]:
@@ -1173,8 +1177,9 @@ def test_nanmean(self):
             result = nanops.nanmean(obj)
             assert result == expected
 
-    @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"])
-    def test_nanmean_skipna_false(self, dtype):
+    @pytest.mark.parametrize("constructor", ["M8", "m8"])
+    def test_nanmean_skipna_false(self, constructor, unit):
+        dtype = f"{constructor}[{unit}]"
         arr = np.arange(12).astype(np.int64).view(dtype).reshape(4, 3)
 
         arr[-1, -1] = "NaT"