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