Getting Started With Pydantic

        
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

loading from json

serializing to json

validation