Pydantic使用
Pydantic可以在代码运行时提供类型提示, 数据校验失败时提供友好的错误提示, 使用Python的类型注解来进行数据校验和settings管理
一般使用
from datetime import datetime from typing import List from typing import Optional from pydantic import BaseModel # 1 定义模型 class User(BaseModel): id: int # 必须字段 name: str = "John Snow" # 有默认值,选填字段 signup_ts: Optional[datetime] = None friends: List[int] = [] # 列表中元素是int类型或者可以直接转换成int类型 external_data = { "id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"], # "3"是可以int("3")的 } # 2 检验数据 user = User(**external_data) # 3 访问数据 print(user.id, user.friends) # 实例化后调用属性 print(repr(user.signup_ts)) # .dict 方法, 返回数据字典 print(user.dict())
校验失败处理
假如数据检验不通过, 会抛出pydantic.ValidationError
from datetime import datetime from typing import List from typing import Optional from pydantic import BaseModel, ValidationError class User(BaseModel): id: int # 必须字段 name: str = "John Snow" # 有默认值,选填字段 signup_ts: Optional[datetime] = None friends: List[int] = [] # 列表中元素是int类型或者可以直接转换成int类型 external_data = { "id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"], # "3"是可以int("3")的 } try: User(id=1, signup_ts=datetime.today(), friends=[1, 2, "not number"]) except ValidationError as e: print(e.json()) """ [ { "loc": [ "friends", 2 ], "msg": "value is not a valid integer", "type": "type_error.integer" } ] """
模型类的的属性和方法
from datetime import datetime from pathlib import Path from typing import List from typing import Optional from pydantic import BaseModel class User(BaseModel): id: int # 必须字段 name: str = "John Snow" # 有默认值,选填字段 signup_ts: Optional[datetime] = None friends: List[int] = [] # 列表中元素是int类型或者可以直接转换成int类型 external_data = { "id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"], # "3"是可以int("3")的 } user = User(**external_data) # 获得已检验数据的字典数据 print(user.dict()) # 获得已检验数据的json数据 print(user.json()) # 这里是浅拷贝 print(user.copy()) # 通过对象解析 print(User.parse_obj(external_data)) # 通过字符串解析 print(User.parse_raw('{"id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"]}')) path = Path('pydantic_tutorial.json') path.write_text('{"id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"]}') # 通过文本解析 print(User.parse_file(path)) # 获得对象 概要 # {'title': 'User', 'type': 'object', 'properties': {'id': ...} } print(user.schema()) # 获得对象 概要json # {'title': 'User', 'type': 'object', 'properties': {'id': ...} } print(user.schema_json()) user_data = {"id": "error", "signup_ts": "2020-12-22 12 22", "friends": [1, 2, 3]} # id是字符串 是错误的 # 不检验数据直接创建模型类,不建议在construct方法中传入未经验证的数据 print(User.construct(**user_data)) # 获得所有字段 # 定义模型类的时候,所有字段都注明类型,字段顺序就不会乱 print(User.__fields__.keys())
模型嵌套
from datetime import datetime, date from typing import List from typing import Optional from pydantic import BaseModel class User(BaseModel): id: int # 必须字段 name: str = "John Snow" # 有默认值,选填字段 signup_ts: Optional[datetime] = None friends: List[int] = [] # 列表中元素是int类型或者可以直接转换成int类型 external_data = { "id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"], # "3"是可以int("3")的 } user = User(**external_data) class Sound(BaseModel): sound: str class Dog(BaseModel): birthday: date weight: float = Optional[None] sound: List[Sound] # 不同的狗有不同的叫声。递归模型(Recursive Models)就是指一个嵌套一个 dogs = Dog(birthday=date.today(), weight=6.66, sound=[{"sound": "wang wang ~"}, {"sound": "ying ying ~"}]) print(dogs.dict())
与ORM结合
from datetime import datetime from typing import List from typing import Optional from pydantic import BaseModel from pydantic import constr from sqlalchemy import Column, Integer, String from sqlalchemy.dialects.postgresql import ARRAY from sqlalchemy.ext.declarative import declarative_base Base = declarative_base() class CompanyOrm(Base): __tablename__ = 'companies' id = Column(Integer, primary_key=True, nullable=False) public_key = Column(String(20), index=True, nullable=False, unique=True) name = Column(String(63), unique=True) domains = Column(ARRAY(String(255))) class CompanyModel(BaseModel): id: int # constr用于约束字符串 public_key: constr(max_length=20) name: constr(max_length=63) domains: List[constr(max_length=255)] class Config: orm_mode = True co_orm = CompanyOrm( id=123, public_key='foobar', name='Testing', domains=['example.com', 'foobar.com'], ) print(CompanyModel.from_orm(co_orm))
验证器
如何使用及参数见passwords_match
方法
from pydantic import BaseModel, ValidationError, validator class UserModel(BaseModel): name: str username: str password1: str password2: str @validator('name') def name_must_contain_space(cls, v, **kwargs): if ' ' not in v: raise ValueError('must contain a space') return v.title() @validator('password2') def passwords_match(cls, v, values, **kwargs): """ :param v: 当前字段的值: zxcvbn2 :param values: 已经验证的数据: {'username': 'scolvin', 'password1': 'zxcvbn'} :param kwargs: {'field': ModelField(name='password2', type=str, required=True), 'config': <class '__main__.Config'>} :return: """ if 'password1' in values and v != values['password1']: raise ValueError('passwords do not match') return v @validator('username') def username_alphanumeric(cls, v): assert v.isalnum(), 'must be alphanumeric' return v try: UserModel( name='samuel', username='scolvin', password1='zxcvbn', password2='zxcvbn2', ) except ValidationError as e: print(e) """ 2 validation errors for UserModel name must contain a space (type=value_error) password2 passwords do not match (type=value_error) """
全部字段类型
见官方文档: Field Types
更多其他使用方法见: pydantic-docs
本文来自博客园,作者:403·Forbidden,转载请注明原文链接:https://www.cnblogs.com/lczmx/p/15747166.html