diff options
Diffstat (limited to 'tests/test_executor.py')
| -rw-r--r-- | tests/test_executor.py | 27 |
1 files changed, 18 insertions, 9 deletions
diff --git a/tests/test_executor.py b/tests/test_executor.py index 78d037a..35935b9 100644 --- a/tests/test_executor.py +++ b/tests/test_executor.py @@ -94,11 +94,10 @@ class TestExecutor(unittest.TestCase): with self.subTest(f"tpch-h {i + 1}"): sql, _ = self.sqls[i] a = self.cached_execute(sql) - b = pd.DataFrame(table.rows, columns=table.columns) - - # The executor represents NULL values as None, whereas DuckDB represents them as NaN, - # and so the following is done to silence Pandas' "Mismatched null-like values" warnings - b = b.fillna(value=np.nan) + b = pd.DataFrame( + ((np.nan if c is None else c for c in r) for r in table.rows), + columns=table.columns, + ) assert_frame_equal(a, b, check_dtype=False, check_index_type=False) @@ -778,14 +777,24 @@ class TestExecutor(unittest.TestCase): self.assertEqual(result.rows, expected) def test_dict_values(self): - tables = { - "foo": [{"raw": {"name": "Hello, World"}}], - } - result = execute("SELECT raw:name AS name FROM foo", read="snowflake", tables=tables) + tables = {"foo": [{"raw": {"name": "Hello, World", "a": [{"b": 1}]}}]} + result = execute("SELECT raw:name AS name FROM foo", read="snowflake", tables=tables) self.assertEqual(result.columns, ("NAME",)) self.assertEqual(result.rows, [("Hello, World",)]) + result = execute("SELECT raw:a[0].b AS b FROM foo", read="snowflake", tables=tables) + self.assertEqual(result.columns, ("B",)) + self.assertEqual(result.rows, [(1,)]) + + result = execute("SELECT raw:a[1].b AS b FROM foo", read="snowflake", tables=tables) + self.assertEqual(result.columns, ("B",)) + self.assertEqual(result.rows, [(None,)]) + + result = execute("SELECT raw:a[0].c AS c FROM foo", read="snowflake", tables=tables) + self.assertEqual(result.columns, ("C",)) + self.assertEqual(result.rows, [(None,)]) + tables = { '"ITEM"': [ {"id": 1, "attributes": {"flavor": "cherry", "taste": "sweet"}}, |