Push DeviceScalar construction into cython for list.contains (#7864)

Fixes #7854.

Authors:
  - https://github.com/brandon-b-miller

Approvers:
  - Keith Kraus (https://github.com/kkraus14)
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #7864

python/cudf/cudf/_lib/lists.pyx

@@ -145,7 +145,10 @@ def extract_element(Column col, size_type index):
     return result
 
 
-def contains_scalar(Column col, DeviceScalar search_key):
+def contains_scalar(Column col, object py_search_key):
+
+    cdef DeviceScalar search_key = py_search_key.device_value
+
     cdef shared_ptr[lists_column_view] list_view = (
         make_shared[lists_column_view](col.view())
     )

python/cudf/cudf/core/column/lists.py

@@ -237,9 +237,10 @@ def contains(self, search_key):
         Series([False, True, True])
         dtype: bool
         """
+        search_key = cudf.Scalar(search_key)
         try:
             res = self._return_or_inplace(
-                contains_scalar(self._column, search_key.device_value)
+                contains_scalar(self._column, search_key)
             )
         except RuntimeError as e:
             if (

python/cudf/cudf/tests/test_contains.py

@@ -1,11 +1,17 @@
 from datetime import datetime as dt
 
+import numpy as np
 import pandas as pd
 import pytest
 
 from cudf import Series
 from cudf.core.index import RangeIndex, as_index
-from cudf.tests.utils import assert_eq
+from cudf.tests.utils import (
+    DATETIME_TYPES,
+    NUMERIC_TYPES,
+    TIMEDELTA_TYPES,
+    assert_eq,
+)
 
 
 def cudf_date_series(start, stop, freq):
@@ -72,3 +78,43 @@ def test_index_contains(values, item, expected):
 def test_rangeindex_contains():
     assert_eq(True, 9 in RangeIndex(start=0, stop=10, name="Index"))
     assert_eq(False, 10 in RangeIndex(start=0, stop=10, name="Index"))
+
+
+@pytest.mark.parametrize("dtype", NUMERIC_TYPES)
+def test_lists_contains(dtype):
+    dtype = np.dtype(dtype)
+    inner_data = np.array([1, 2, 3], dtype=dtype)
+
+    data = Series([inner_data])
+
+    contained_scalar = inner_data.dtype.type(2)
+    not_contained_scalar = inner_data.dtype.type(42)
+
+    assert data.list.contains(contained_scalar)[0]
+    assert not data.list.contains(not_contained_scalar)[0]
+
+
+@pytest.mark.parametrize("dtype", DATETIME_TYPES + TIMEDELTA_TYPES)
+def test_lists_contains_datetime(dtype):
+    dtype = np.dtype(dtype)
+    inner_data = np.array([1, 2, 3])
+
+    unit, _ = np.datetime_data(dtype)
+
+    data = Series([inner_data])
+
+    contained_scalar = inner_data.dtype.type(2)
+    not_contained_scalar = inner_data.dtype.type(42)
+
+    assert data.list.contains(contained_scalar)[0]
+    assert not data.list.contains(not_contained_scalar)[0]
+
+
+def test_lists_contains_bool():
+    data = Series([[True, True, True]])
+
+    contained_scalar = True
+    not_contained_scalar = False
+
+    assert data.list.contains(contained_scalar)[0]
+    assert not data.list.contains(not_contained_scalar)[0]