At float version of test_interpolate_by_trailing_nulls

py-polars/tests/unit/operations/test_interpolate_by.py

@@ -118,8 +118,10 @@ def test_interpolate_by_leading_nulls() -> None:
     assert_frame_equal(result, expected)
 
 
-def test_interpolate_by_trailing_nulls() -> None:
-    df = pl.DataFrame(
+@pytest.mark.parametrize("dataset", ["floats", "dates"])
+def test_interpolate_by_trailing_nulls(dataset: str) -> None:
+    input_data = {
+        "dates": pl.DataFrame(
         {
             "times": [
                 date(2020, 1, 1),
@@ -130,10 +132,25 @@ def test_interpolate_by_trailing_nulls() -> None:
                 date(2020, 1, 13),
             ],
             "values": [1, None, None, 5, None, None],
+        }),
+        "floats": pl.DataFrame(
+        {
+            "times": [0.2, 0.4, 0.5, 0.6, 0.9, 1.1],
+            "values": [1, None, None, 5, None, None],
         }
-    )
+        )
+    }
+
+    expected_data = {
+        "dates": pl.DataFrame({"values": [1.0, 1.7999999999999998, 4.6, 5.0, None, None]}),
+        "floats": pl.DataFrame({"values": [1.0, 3.0, 4.0, 5.0, None, None]})
+    }
+
+    df = input_data[dataset]
+    expected = expected_data[dataset]
+
     result = df.select(pl.col("values").interpolate_by("times"))
-    expected = pl.DataFrame({"values": [1.0, 1.7999999999999998, 4.6, 5.0, None, None]})
+
     assert_frame_equal(result, expected)
     result = (
         df.sort("times", descending=True)