BUG: groupby().any() returns true for groups with timedelta all NaT

pandas/core/groupby/ops.py

@@ -374,6 +374,13 @@ def _call_cython_op(
         if is_datetimelike:
             values = values.view("int64")
             is_numeric = True
+
+            # Fix for NaT handling: ensure NaT is treated as False in any() and all()
+            if self.how in ["any", "all"]:
+                # Set NaT (which is represented as the smallest int64) to False (0)
+                nat_mask = values == np.iinfo(np.int64).min
+                values[nat_mask] = 0  # Treat NaT as False
+
         elif dtype.kind == "b":
             values = values.view("uint8")
         if values.dtype == "float16":

pandas/tests/groupby/test_grouping.py

@@ -1180,3 +1180,19 @@ def test_grouping_by_key_is_in_axis():
     result = gb.sum()
     expected = DataFrame({"a": [1, 2], "b": [1, 2], "c": [7, 5]})
     tm.assert_frame_equal(result, expected)
+
+
+def test_groupby_any_with_timedelta(self):
+        # Create a DataFrame with Timedelta and NaT values
+        df = DataFrame({
+            "A": ["foo", "foo", "bar", "bar"],
+            "B": [pd.Timedelta(1, unit='D'), pd.NaT, pd.Timedelta(2, unit='D'), pd.NaT]
+        })
+
+        # Group by column A and check if any Timedelta exists (i.e., non-NaT)
+        result = df.groupby("A")["B"].any()
+
+        # Expected result: groups with only NaT should return False, others should return True
+        expected = Series([True, False], index=["foo", "bar"], name="B")
+
+        tm.assert_series_equal(result, expected)