Test nans_to_nulls in pylibcudf

python/cudf/cudf/pylibcudf_tests/conftest.py

@@ -141,6 +141,15 @@ def sorted_opt(request):
     return request.param
 
 
-@pytest.fixture(scope="session", params=[False, True])
+@pytest.fixture(
+    scope="session", params=[False, True], ids=["without_nulls", "with_nulls"]
+)
 def has_nulls(request):
     return request.param
+
+
+@pytest.fixture(
+    scope="session", params=[False, True], ids=["without_nans", "with_nans"]
+)
+def has_nans(request):
+    return request.param

python/cudf/cudf/pylibcudf_tests/test_transform.py

@@ -0,0 +1,32 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+import math
+
+import pyarrow as pa
+from utils import assert_column_eq
+
+from cudf._lib import pylibcudf as plc
+
+
+def test_nans_to_nulls(has_nans):
+    if has_nans:
+        values = [1, float("nan"), float("nan"), None, 3, None]
+    else:
+        values = [1, 4, 5, None, 3, None]
+
+    replaced = [
+        None if (v is None or (v is not None and math.isnan(v))) else v
+        for v in values
+    ]
+
+    h_input = pa.array(values, type=pa.float32())
+    input = plc.interop.from_arrow(h_input)
+    assert input.null_count() == h_input.null_count
+    expect = pa.array(replaced, type=pa.float32())
+
+    mask, null_count = plc.transform.nans_to_nulls(input)
+
+    assert null_count == expect.null_count
+    got = input.with_mask(mask, null_count)
+
+    assert_column_eq(expect, got)