Handle Interval scalars when passed in list-like inputs to `cudf.In…

…dex` (#13956)

closes #13952 

This PR fixes an issue with `IntervalColumn` construction where we can utilize the existing type inference to create a pandas Series and then construct an `IntervalColumn` out of it since pyarrow is unable to read this kind of input correctly.

Authors:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

Approvers:
  - Matthew Roeschke (https://github.com/mroeschke)

URL: #13956

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

@@ -2454,25 +2454,29 @@ def as_column(
 
 def _construct_array(
     arbitrary: Any, dtype: Optional[Dtype]
-) -> Union[np.ndarray, cupy.ndarray]:
+) -> Union[np.ndarray, cupy.ndarray, pd.api.extensions.ExtensionArray]:
     """
-    Construct a CuPy or NumPy array from `arbitrary`
+    Construct a CuPy/NumPy/Pandas array from `arbitrary`
     """
     try:
         dtype = dtype if dtype is None else cudf.dtype(dtype)
         arbitrary = cupy.asarray(arbitrary, dtype=dtype)
     except (TypeError, ValueError):
         native_dtype = dtype
+        inferred_dtype = None
         if (
             dtype is None
             and not cudf._lib.scalar._is_null_host_scalar(arbitrary)
-            and infer_dtype(arbitrary, skipna=False)
+            and (inferred_dtype := infer_dtype(arbitrary, skipna=False))
             in (
                 "mixed",
                 "mixed-integer",
             )
         ):
             native_dtype = "object"
+        if inferred_dtype == "interval":
+            # Only way to construct an Interval column.
+            return pd.array(arbitrary)
         arbitrary = np.asarray(
             arbitrary,
             dtype=native_dtype

python/cudf/cudf/tests/test_interval.py

@@ -136,6 +136,19 @@ def test_create_interval_df(data1, data2, data3, data4, closed):
     assert_eq(expect_three, got_three)
 
 
+def test_create_interval_index_from_list():
+    interval_list = [
+        np.nan,
+        pd.Interval(2.0, 3.0, closed="right"),
+        pd.Interval(3.0, 4.0, closed="right"),
+    ]
+
+    expected = pd.Index(interval_list)
+    actual = cudf.Index(interval_list)
+
+    assert_eq(expected, actual)
+
+
 def test_interval_index_unique():
     interval_list = [
         np.nan,