Tags
global Field global BaseModel from pydantic import BaseModel from pydantic import Field
Pydantic is a Python library for serializing data into models that can be validated with a deep set of built in valitators or your own custom validators, and deserialize back to JSON or dictionary.
Installation #
To install pydantic you will first need python and pip. Once you have pip installed you can install pydantic with pip.
pip install pydantic
Always install in a virtual environment
Creating a Pydantic model #
To get started with pydantic you will first need to create a Pydantic model.
This is a python class that inherits from pydantic.BaseModel.
from pydantic import BaseModel from pydantic import Field from typing import Optional class Person(BaseModel): name: str = Field(...) age: int
parsing an object #
person = Person(name="John Doe", age=30) print(person)
name='John Doe' age=30
data serialization #
Pydantic has some very robust serialization methods that will automatically coherse your data into the type specified by the type-hint in the model if it can.
person = Person(name=12, age="30") print(f'name: {person.name}, type: {type(person.name)}') print(f'age: {person.age}, type: {type(person.age)}')
1 validation error for Person name Input should be a valid string [type=string_type, input_value=12, input_type=int] For further information visit https://errors.pydantic.dev/2.3/v/string_type
person = Person(name="John Doe", age='thirty') print(f'name: {person.name}, type: {type(person.name)}') print(f'age: {person.age}, type: {type(person.age)}')
1 validation error for Person age Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='thirty', input_type=str] For further information visit https://errors.pydantic.dev/2.3/v/int_parsing