Pandera Schema Model

schema = pa.DataFrameSchema({
    # built-in python types
    "int_column": pa.Column(int),
    "float_column": pa.Column(float),
    "str_column": pa.Column(str),

    # pandas dtype string aliases
    "int_column2": pa.Column("int64"),
    "float_column2": pa.Column("float64"),
    # pandas > 1.0.0 support native "string" type
    "str_column2": pa.Column("str"),

    # pandera DataType
    "int_column3": pa.Column(pa.Int),
    "float_column3": pa.Column(pa.Float),
    "str_column3": pa.Column(pa.String),
})

import pandas as pd
import pandera as pa


# data to validate
df = pd.DataFrame({
    "column1": [1, 4, 0, 10, 9],
    "column2": [-1.3, -1.4, -2.9, -10.1, -20.4],
    "column3": ["value_1", "value_2", "value_3", "value_2", "value_1"]
})

# 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(str, checks=[
        pa.Check.str_startswith("value_"),
        # define custom checks as functions that take a series as input and
        # outputs a boolean or boolean Series
        pa.Check(lambda s: s.str.split("_", expand=True).shape[1] == 2)
    ]),
})

validated_df = schema(df)
print(validated_df)

#     column1  column2  column3
#  0        1     -1.3  value_1
#  1        4     -1.4  value_2
#  2        0     -2.9  value_3
#  3       10    -10.1  value_2
#  4        9    -20.4  value_1