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

pandas/core/groupby/ops.py

@@ -379,6 +379,12 @@ def _call_cython_op(
         if values.dtype == "float16":
             values = values.astype(np.float32)
 
+        values = values.T
+        if mask is not None:
+            mask = mask.T
+            if result_mask is not None:
+                result_mask = result_mask.T
+
         if self.how in ["any", "all"]:
             if mask is None:
                 mask = isna(values)
@@ -392,12 +398,6 @@ def _call_cython_op(
             values = values.astype(bool, copy=False).view(np.int8)
             is_numeric = True
 
-        values = values.T
-        if mask is not None:
-            mask = mask.T
-            if result_mask is not None:
-                result_mask = result_mask.T
-
         out_shape = self._get_output_shape(ngroups, values)
         func = self._get_cython_function(self.kind, self.how, values.dtype, is_numeric)
         values = self._get_cython_vals(values)

pandas/tests/groupby/test_grouping.py

@@ -1180,3 +1180,28 @@ 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():
+    # 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 with sorting enabled and check if any Timedelta exists
+    result = df.groupby("A", sort=True)["B"].any()
+
+    # Corrected expected result: groups with only NaT should return False, else True
+    expected = Series([True, True], index=["foo", "bar"], name="B")
+
+    # Set the expected index name to match the result
+    expected.index.name = "A"
+
+    # Sort the expected result to match the order of result
+    expected = expected.sort_index()
+
+    # Assert that the result matches the expected output
+    tm.assert_series_equal(result, expected)