Add arrays_overlap and list_overlap as aliases for array_has_any

These aliases match the upstream DataFusion SQL-level aliases, completing
the set of missing array functions from issue #1452.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: timsaucer

python/datafusion/functions.py

@@ -95,6 +95,7 @@
     "array_sort",
     "array_to_string",
     "array_union",
+    "arrays_overlap",
     "arrays_zip",
     "arrow_cast",
     "arrow_typeof",
@@ -194,6 +195,7 @@
     "list_max",
     "list_min",
     "list_ndims",
+    "list_overlap",
     "list_pop_back",
     "list_pop_front",
     "list_position",
@@ -2885,6 +2887,24 @@ def list_has_any(first_array: Expr, second_array: Expr) -> Expr:
     return array_has_any(first_array, second_array)
 
 
+def arrays_overlap(first_array: Expr, second_array: Expr) -> Expr:
+    """Returns true if any element appears in both arrays.
+
+    See Also:
+        This is an alias for :py:func:`array_has_any`.
+    """
+    return array_has_any(first_array, second_array)
+
+
+def list_overlap(first_array: Expr, second_array: Expr) -> Expr:
+    """Returns true if any element appears in both arrays.
+
+    See Also:
+        This is an alias for :py:func:`array_has_any`.
+    """
+    return array_has_any(first_array, second_array)
+
+
 def list_contains(array: Expr, element: Expr) -> Expr:
     """Returns true if the element appears in the array, otherwise false.
 

python/tests/test_functions.py

@@ -394,6 +394,18 @@ def py_flatten(arr):
             ),
             lambda data: [np.any([v in r for v in [1.0, 3.0, 5.0]]) for r in data],
         ),
+        (
+            lambda col: f.arrays_overlap(
+                col, f.make_array(*[literal(v) for v in [1.0, 3.0, 5.0]])
+            ),
+            lambda data: [np.any([v in r for v in [1.0, 3.0, 5.0]]) for r in data],
+        ),
+        (
+            lambda col: f.list_overlap(
+                col, f.make_array(*[literal(v) for v in [1.0, 3.0, 5.0]])
+            ),
+            lambda data: [np.any([v in r for v in [1.0, 3.0, 5.0]]) for r in data],
+        ),
         (
             lambda col: f.array_position(col, literal(1.0)),
             lambda data: [py_indexof(r, 1.0) for r in data],