fix testcase

Author: shuoweil

tests/system/small/test_dataframe_io.py

@@ -386,25 +386,25 @@ def test_to_pandas_batches_preserves_dtypes_for_populated_nested_json(session):
             STRUCT(JSON '{"x":1}' AS json_field, 'test' AS str_field) AS json_struct
     """
     df = session.read_gbq(sql, index_col="id")
-
     batches = list(df.to_pandas_batches())
 
-    # Check that we processed the row
     assert sum(len(b) for b in batches) == 1
 
-    # Check dtypes on the resulting batch
-    assert isinstance(batches[0].dtypes["json_array"], pd.ArrowDtype)
-    assert isinstance(batches[0].dtypes["json_array"].pyarrow_dtype, pa.ListType)
+    # Check dtypes based on pandas version
+    if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable:
+        assert isinstance(batches[0].dtypes["json_array"], pd.ArrowDtype)
+        assert isinstance(batches[0].dtypes["json_array"].pyarrow_dtype, pa.ListType)
+    else:
+        # In pandas 1.x, list types become object dtype
+        assert batches[0].dtypes["json_array"] == "object"
+
+    # Struct types work in both pandas versions
     assert isinstance(batches[0].dtypes["json_struct"], pd.ArrowDtype)
     assert isinstance(batches[0].dtypes["json_struct"].pyarrow_dtype, pa.StructType)
 
 
 def test_to_pandas_batches_should_not_error_on_empty_nested_json(session):
-    """Verify to_pandas_batches() works with empty nested JSON types.
-
-    Regression test for PyArrow limitation with empty JSON arrays.
-    """
-    # This SQL query is MINIMAL and tests only the EMPTY regression case.
+    """Verify to_pandas_batches() works with empty nested JSON types."""
     sql = """
         SELECT
             1 AS id,
@@ -413,14 +413,16 @@ def test_to_pandas_batches_should_not_error_on_empty_nested_json(session):
     """
     df = session.read_gbq(sql, index_col="id")
 
-    # The main point of this test is that this line does not raise an error.
+    # The main point: this should not raise an error
     batches = list(df.to_pandas_batches())
-
-    # Verify the row was actually processed and not just skipped
     assert sum(len(b) for b in batches) == 1
 
-    # Verify dtypes are still correct, even with empty data
-    assert isinstance(batches[0].dtypes["json_array"], pd.ArrowDtype)
+    # Check dtypes based on pandas version
+    if bigframes.features.PANDAS_VERSIONS.is_arrow_list_dtype_usable:
+        assert isinstance(batches[0].dtypes["json_array"], pd.ArrowDtype)
+    else:
+        assert batches[0].dtypes["json_array"] == "object"
+
     assert isinstance(batches[0].dtypes["json_struct"], pd.ArrowDtype)