Pydantic
Base Model
from pydantic import BaseModel
class User(BaseModel):
id: int
name = 'John Doe'
age: int
The above code defines a base model with three attributes: id, name, and age. The id and age attributes are required, while the name attribute has a default value of John Doe.
Instantiating the model is as simple as passing in the required attributes:
user = User(id=1, age=25)
Optional
from typing import Optional
class User(BaseModel):
id: int
name: str
age: Optional[int] = None
The code defines an optional age attribute. The attribute is optional because it has a default value of None.
Nested Models
from typing import List
class User(BaseModel):
id: int
name: str
age: int
class Post(BaseModel):
id: int
title: str
content: str
writer: User
The code defines a Post model with a writer attribute of type User. The writer attribute is a nested model.
Validators
from pydantic import BaseModel, validator, ValidationError
class User(BaseModel):
id: int
name: str
age: int
@validator('age')
def check_age(cls, v):
if v < 18:
raise ValidationError('must be 18 or older')
return v
The code defines a validator for the age attribute. The validator checks if the age is greater than or equal to 18. If the age is less than 18, a ValidationError is raised.
Field
from pydantic import BaseModel, Field
class User(BaseModel):
id: int = Field(..., gt=0)
name: str = Field(..., min_length=3, description='Name of the user')
age: int = Field(None, gt=0, lt=150)
The Field class is very useful when you want to add metadata to your model attributes. ... means required. gt means greater than. lt means less than. There are many other arguments you can pass to the Field class, such as description, title, alias, etc.