BUG: groupby.sum with timedelta64 and NaT

doc/source/whatsnew/v1.3.5.rst

@@ -19,6 +19,7 @@ Fixed regressions
 - Fixed regression in creating a :class:`DataFrame` from a timezone-aware :class:`Timestamp` scalar near a Daylight Savings Time transition (:issue:`42505`)
 - Fixed performance regression in :func:`read_csv` (:issue:`44106`)
 - Fixed regression in :meth:`Series.duplicated` and :meth:`Series.drop_duplicates` when Series has :class:`Categorical` dtype with boolean categories (:issue:`44351`)
+- Fixed regression in :meth:`.GroupBy.sum` with ``timedelta64[ns]`` dtype containing ``NaT`` failing to treat that value as NA (:issue:`42659`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/_libs/groupby.pyi

@@ -56,6 +56,7 @@ def group_add(
     values: np.ndarray,  # ndarray[complexfloating_t, ndim=2]
     labels: np.ndarray,  # const intp_t[:]
     min_count: int = ...,
+    datetimelike: bool = ...,
 ) -> None: ...
 def group_prod(
     out: np.ndarray,  # floating[:, ::1]

pandas/_libs/groupby.pyx

@@ -495,7 +495,8 @@ def group_add(add_t[:, ::1] out,
               int64_t[::1] counts,
               ndarray[add_t, ndim=2] values,
               const intp_t[::1] labels,
-              Py_ssize_t min_count=0) -> None:
+              Py_ssize_t min_count=0,
+              bint datetimelike=False) -> None:
     """
     Only aggregates on axis=0 using Kahan summation
     """
@@ -557,7 +558,14 @@ def group_add(add_t[:, ::1] out,
                     val = values[i, j]
 
                     # not nan
-                    if val == val:
+                    # With dt64/td64 values, values have been cast to float64
+                    #  instead if int64 for group_add, but the logic
+                    #  is otherwise the same as in _treat_as_na
+                    if val == val and not (
+                        add_t is float64_t
+                        and datetimelike
+                        and val == <float64_t>NPY_NAT
+                    ):
                         nobs[lab, j] += 1
                         y = val - compensation[lab, j]
                         t = sumx[lab, j] + y

pandas/core/groupby/ops.py

@@ -531,6 +531,16 @@ def _call_cython_op(
                     result_mask=result_mask,
                     is_datetimelike=is_datetimelike,
                 )
+            elif self.how in ["add"]:
+                # We support datetimelike
+                func(
+                    result,
+                    counts,
+                    values,
+                    comp_ids,
+                    min_count,
+                    datetimelike=is_datetimelike,
+                )
             else:
                 func(result, counts, values, comp_ids, min_count)
         else:

pandas/tests/groupby/test_function.py

@@ -1162,3 +1162,27 @@ def test_mean_on_timedelta():
         pd.to_timedelta([4, 5]), name="time", index=Index(["A", "B"], name="cat")
     )
     tm.assert_series_equal(result, expected)
+
+
+def test_groupby_sum_timedelta_with_nat():
+    # GH#42659
+    df = DataFrame(
+        {
+            "a": [1, 1, 2, 2],
+            "b": [pd.Timedelta("1d"), pd.Timedelta("2d"), pd.Timedelta("3d"), pd.NaT],
+        }
+    )
+    td3 = pd.Timedelta(days=3)
+
+    gb = df.groupby("a")
+
+    res = gb.sum()
+    expected = DataFrame({"b": [td3, td3]}, index=Index([1, 2], name="a"))
+    tm.assert_frame_equal(res, expected)
+
+    res = gb["b"].sum()
+    tm.assert_series_equal(res, expected["b"])
+
+    res = gb["b"].sum(min_count=2)
+    expected = Series([td3, pd.NaT], dtype="m8[ns]", name="b", index=expected.index)
+    tm.assert_series_equal(res, expected)