Problems while inserting into a table with a Tuple column

[michael-tsel]

Describe the bug
client.insert_dataframe() method throws if the table contains tuple column

To Reproduce
Suppose I have the following table:

CREATE TABLE IF NOT EXISTS tt
(
    a Tuple(x Float32, y Float32),
) 
ENGINE = Memory;


INSERT INTO tt VALUES ((0.0, 0.0))

I can query this table in different ways:

1. client.query_dataframe("SELECT * FROM tt", settings={'use_numpy': False})
   which gives a dataframe of form:

         a
0 	(0.0, 0.0)

2. client.query_dataframe("SELECT * FROM tt", settings={'use_numpy': False, 'namedtuple_as_json': True, 'allow_experimental_object_type': 1})
   which gives a dataframe of form:

         a
0 	{'x': 0.0, 'y': 0.0}

However, I'm struggling with inserting new data.
Both

df = pd.DataFrame([{'a': (0.1, 0.1),},])
db.client.insert_dataframe(f"INSERT INTO tt VALUES", df)

and

df = pd.DataFrame([{'a': {'x': 0.1, 'y': 0.1},},])
client.insert_dataframe(f"INSERT INTO tt VALUES", df)

throw this exception:

TypeError: 'NoneType' object is not iterable

The same but with settings={'use_numpy': False} throws:

TypeError: Unsupported column type: <class 'numpy.ndarray'>. list or tuple is expected.

So the only way I can insert new data at the moment is:

df = pd.DataFrame([{'a': (0.0, 0.1),},])
client.execute(f"INSERT INTO tt VALUES", df.values.tolist(), settings={'use_numpy': False})

The same with df = pd.DataFrame([{'a': {'x': 0.1, 'y': 0.1},},]) throws:

TypeMismatchError: Code: 53. Type mismatch in VALUES section. Repeat query with types_check=True for detailed info. Column a: required argument is not a float

Expected behavior
I expected at least one of client.insert_dataframe() above to work

Versions

• clickhouse-driver 0.2.7
• ClickHouse server 24.1.2.5
• Python 3.11.6.