Pydantic是python用来做数据校验的非常好用的工具，让我们来一步一步慢慢深入吧。
1 基础使用
主要在于如何继承Pydantic中的BaseModel对类中的数据进行数据校验。1.1 数据项缺失
from pydantic import BaseModel
from typing import List
class User(BaseModel):
id: int
age: int
name: str
friends: List[str]
user = User(id=1, age=18, name="john")
# pydantic_core._pydantic_core.ValidationError: 1 validation error for User
# friends
# Field required [type=missing, input_value={'id': 1, 'age': 18, 'name': 'john'}, input_type=dict]
1.2 数据类型不匹配
from pydantic import BaseModel
from typing import List
class User(BaseModel):
id: int
age: int
name: str
friends: List[str]
user = User(id=1, age=18, name="john", friends=["mary", 1])
# pydantic_core._pydantic_core.ValidationError: 1 validation error for User
# friends.1
# Input should be a valid string [type=string_type, input_value=1, input_type=int]1.3 数据验证通过
from pydantic import BaseModel
from typing import List
class User(BaseModel):
id: int
age: int
name: str
friends: List[str]
user = User(id=1, age=18, name="john", friends=["mary", "tom"])
print(user) # class
print(user.model_dump()) # dict
print(user.model_dump_json()) # json
# id=1 age=18 name='john' friends=['mary', 'tom']
# {'id': 1, 'age': 18, 'name': 'john', 'friends': ['mary', 'tom']}
# {"id":1,"age":18,"name":"john","friends":["mary","tom"]}2 数据验证
基础数据类型：int, str 枚举类型：Literal 注释数据类型：Annotated + 大小判断from pydantic import BaseModel
from typing import List, Literal
from typing_extensions import Annotated
from annotated_types import Gt
class User(BaseModel):
id: Annotated[int, "id需要大于1", Gt(1)]
age: int
name: str
color: Literal["green", "red"]
friends: List[str]
user = User(id=2, age=18, name="john", color="red", friends=["mary", "tom"])
print(user)3 多个类联合使用
某一个校验类可以当做其他校验类的内容来使用from pydantic import BaseModel
from typing import List, Literal
from typing_extensions import Annotated
from annotated_types import Gt
class User(BaseModel):
id: Annotated[int, "id需要大于1", Gt(1)]
age: int
name: str
color: Literal["green", "red"]
friends: List[str]
class Address(BaseModel):
zip: str
city: str
phone: str
class Room(BaseModel):
student: User
address: Address
user = User(id=2, age=18, name="tom", color="red", friends=["john"])
address = Address(zip="xx", city="beijing", phone="124")
room = Room(student=user, address=address)
print(room)
4 Fields详细使用
4.1 Fields默认设置
默认设置有两种，一种是使用Fields原生进行设置，另一种借助Annotated注释数据类型结合Fiedls函数进行设置。from pydantic import BaseModel, Field
from typing import List, Literal
from typing_extensions import Annotated
from annotated_types import Gt
from uuid import uuid4
class User(BaseModel):
id: Annotated[int, "id需要大于1", Gt(1)]
age: int = Field(gt=0, lt=100, default=18)
name: Annotated[str, Field(default_factory=lambda: uuid4().hex)]
color: Literal["green", "red"]
friends: List[str]
user = User(id=2, color="red", friends=["john"])
print(user)
# id=2 age=18 name='f6ba920c437b4d74b266bc5b0f1c285e' color='red' friends=['john']
4.2 Fields严格模式
一般来说，如果BaseModel中设置为int，input为str时，BaseModel会自动从str转为int，如果我们不想要这种转换，则可以设置Fields为严格模式。from pydantic import BaseModel, Field
from typing import List, Literal
from typing_extensions import Annotated
from annotated_types import Gt
from uuid import uuid4
class User(BaseModel):
id: Annotated[int, "id需要大于1", Gt(1)]
age: int = Field(gt=0, lt=100, default=18, strict=True)
name: Annotated[str, Field(default_factory=lambda: uuid4().hex, strict=True)]
color: Literal["green", "red"]
friends: List[str]
user = User(id=2, color="red", friends=["john"])
print(user)
# id=2 age=18 name='f6ba920c437b4d74b266bc5b0f1c285e' color='red' friends=['john']