Nested Datatypes specified in schema

[WilliamCVan]

How do we specify a nested datatype like a list/array of integers?

I would like to specify a nested data structure for the schema, below I am trying to specify a list of integers for "column0". I could not find any documentation on how to specify nested types, is it possible?

df = pd.DataFrame({
    "column0": [[1, 2], [4], [0, 5, 7], [10], [9, 8]],
    "column1": [1, 4, 0, 10, 9],
    "column3": ["value_1", "value_2", "value_3", "value_2", "value_1"],
})

# define schema
schema = pa.DataFrameSchema({
    "column0": pa.Column(int),  <-- ERROR HERE
    "column1": pa.Column(int, checks=pa.Check.le(10)),
    "column3": pa.Column(str),
})

validated_df = schema(df)
print(validated_df)

Getting the following ERROR message:
"pandera.errors.SchemaError: expected series 'column0' to have type int64, got object"

[cosmicBboy]

you can use object as the dtype, and then use custom checks to validate stuff about that column

# define schema
schema = pa.DataFrameSchema({
    "column0": pa.Column(
        object,
        checks=pa.Check(lambda element: all(isinstance(x, int) for x in element), element_wise=True)
    ),
    "column1": pa.Column(int, checks=pa.Check.le(10)),
    "column3": pa.Column(str),
})

[WilliamCVan]

@cosmicBboy Thank you for your help, resolved my issue