How can I specify the equivalent of a spark ArrayType(StringType()) in a pandas dataframe and use a Pandera schema

Question

I would like to be able to specify the dtype within the array as well. Spark how something like ArrayType(StringType()). Does this exist in python/pandas/numpy/pandera world?

import pandas as pd
import pandera as pa
import numpy as np

# data to validate
df = pd.DataFrame({
    "column1": [1, 4],
    "column2": [-1.3, -1.4],
    "column3": [["value_1","value1a"], ["value_2","value_2a"]],

})

# define schema
schema = pa.DataFrameSchema({
    "column1": pa.Column(int, checks=pa.Check.le(10)),
    "column2": pa.Column(float, checks=pa.Check.lt(-1.2)),
    "column3": pa.Column(np.str_),
})

validated_df = schema(df)
print(validated_df)
print(validated_df.dtypes)

What I get:

   column1  column2              column3
0        1     -1.3   [value_1, value1a]
1        4     -1.4  [value_2, value_2a]
column1      int64
column2    float64
column3     object
dtype: object

What I'd like:

   column1  column2              column3
0        1     -1.3   [value_1, value1a]
1        4     -1.4  [value_2, value_2a]
column1      int64
column2    float64
column3     ArrayType(StringType()) -> Or the equivalent
dtype: object