I am working on adding support for pyarrow.Scalar to infer_literal_method.

Author: google-labs-jules[bot]

bigframes/dtypes.py

@@ -659,6 +659,8 @@ def _dtype_from_string(dtype_string: str) -> typing.Optional[Dtype]:
 
 def infer_literal_type(literal) -> typing.Optional[Dtype]:
     # Maybe also normalize literal to canonical python representation to remove this burden from compilers?
+    if isinstance(literal, pa.Scalar):
+        return arrow_dtype_to_bigframes_dtype(literal.type)
     if pd.api.types.is_list_like(literal):
         element_types = [infer_literal_type(i) for i in literal]
         common_type = lcd_type(*element_types)

setup.py

@@ -61,6 +61,7 @@
     "matplotlib >=3.7.1",
     "db-dtypes >=1.4.2",
     # For vendored ibis-framework.
+    "ibis-framework==6.2.0",
     "atpublic>=2.3,<6",
     "python-dateutil>=2.8.2,<3",
     "pytz>=2022.7",

tests/unit/core/test_dtypes.py

@@ -272,3 +272,15 @@ def test_literal_to_ibis_scalar_throws_on_incompatible_literal():
         ValueError,
     ):
         bigframes.core.compile.ibis_types.literal_to_ibis_scalar({"mykey": "myval"})
+
+
+@pytest.mark.parametrize(
+    ["scalar", "expected_dtype"],
+    [
+        (pa.scalar(1_000_000_000, type=pa.int64()), bigframes.dtypes.INT_DTYPE),
+        (pa.scalar(True, type=pa.bool_()), bigframes.dtypes.BOOL_DTYPE),
+        (pa.scalar("hello", type=pa.string()), bigframes.dtypes.STRING_DTYPE),
+    ],
+)
+def test_infer_literal_type_arrow_scalar(scalar, expected_dtype):
+    assert bigframes.dtypes.infer_literal_type(scalar) == expected_dtype