Dask set dtype to an array of integers

With Dask I try to create a column that has type list with integers. For example:

import dask.dataframe as dd
import pandas as pd

# Have an example Dask Dataframe
ddf = dd.from_pandas(pd.DataFrame({
    'id': [1, 2, 3, 4, 5],
    'name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'],
    'age': [25, 30, 35, 40, 45]
}), npartitions=1)

# now create an array type column
ddf["alist"] = ddf.apply(
    lambda k: [1, 0, 0], axis=1, meta=("alist", "list<item: int64>")
)

This particular case fails because:

TypeError: data type 'list<item: int64>' not understood

Eventually I want to write to parquet:

ddf.to_parquet(
    "example",
    engine="pyarrow",
    compression="snappy",
    overwrite=True,
)

and if I specify the dtype incorrect it raises:

ValueError: Failed to convert partition to expected pyarrow schema:
    `ArrowInvalid('Could not convert [1, 2, 3] with type list: tried to convert to int64', 'Conversion failed for column alist with type object')`

Expected partition schema:
    id: int64
    name: large_string
    age: int64
    alist: int64
    __null_dask_index__: int64

Received partition schema:
    id: int64
    name: large_string
    age: int64
    alist: list<item: int64>
      child 0, item: int64
    __null_dask_index__: int64

This error *may* be resolved by passing in schema information for
the mismatched column(s) using the `schema` keyword in `to_parquet`.

As discussed here, you can also specify the PyArrow types upon writing:

ddf.to_parquet(
    "example",
    engine="pyarrow",
    compression="snappy",
    overwrite=True,
    schema={
    "alist": pa.list_(pa.int32()),
    }
)