Add tests and address reviews

python/cudf/cudf/tests/test_datetime.py

@@ -1541,3 +1541,12 @@ def test_is_quarter_end(data, dtype):
     got = gs.dt.is_quarter_end
 
     assert_eq(expect, got)
+
+
+def test_error_values():
+    s = cudf.Series([1, 2, 3], dtype="datetime64[ns]")
+    with pytest.raises(
+        NotImplementedError,
+        match="DateTime Arrays is not yet implemented in cudf",
+    ):
+        s.values

python/cudf/cudf/tests/test_timedelta.py

@@ -1386,3 +1386,12 @@ def test_timedelta_reductions(data, op, dtype):
         assert True
     else:
         assert_eq(expected.to_numpy(), actual)
+
+
+def test_error_values():
+    s = cudf.Series([1, 2, 3], dtype="timedelta64[ns]")
+    with pytest.raises(
+        NotImplementedError,
+        match="TimeDelta Arrays is not yet implemented in cudf",
+    ):
+        s.values

python/dask_cudf/dask_cudf/backends.py

@@ -263,6 +263,8 @@ def is_categorical_dtype_cudf(obj):
 
     @percentile_dispatch.register((cudf.Series, cp.ndarray, cudf.Index))
     def percentile_cudf(a, q, interpolation="linear"):
+        # Cudf dispatch to the equivalent of `np.percentile`:
+        # https://numpy.org/doc/stable/reference/generated/numpy.percentile.html
         a = cudf.Series(a)
         # a is series.
         n = len(a)
@@ -273,7 +275,6 @@ def percentile_cudf(a, q, interpolation="linear"):
 
         if cudf.utils.dtypes.is_categorical_dtype(a.dtype):
             result = cp.percentile(a.cat.codes, q, interpolation=interpolation)
-            import pandas as pd
 
             return (
                 pd.Categorical.from_codes(