Pydantic基本模型及常用方法，递归模型，字段类型，校验，模型类配置

Author: liaogx

pydantic_tutorial.json

@@ -0,0 +1 @@
+{"id": "123", "signup_ts": "2020-12-22 12:22", "friends": [1, 2, "3"]}

pydantic_tutorial.py

@@ -1,4 +1,133 @@
 #!/usr/bin/python3
 # -*- coding:utf-8 -*-
-# __author__ = '__Jack__'
+# __author__ = "__Jack__"
 
+from datetime import datetime, date
+from pathlib import Path
+from typing import List
+from typing import Optional
+
+from pydantic import BaseModel, ValidationError
+from pydantic import constr
+from sqlalchemy import Column, Integer, String
+from sqlalchemy.dialects.postgresql import ARRAY
+from sqlalchemy.ext.declarative import declarative_base
+
+"""
+Data validation and settings management using python type annotations.
+使用Python的类型注解来进行数据校验和settings管理
+
+pydantic enforces type hints at runtime, and provides user friendly errors when data is invalid.
+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
+
+Define how data should be in pure, canonical python; validate it with pydantic.
+定义数据应该如何在纯规范的Python代码中保存，并用Pydantic验证它
+"""
+
+print("\033[31m1. --- Pydantic的基本用法。Pycharm可以安装Pydantic插件 ---\033[0m")
+
+
+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)
+print(user.id, user.friends)  # 实例化后调用属性
+print(repr(user.signup_ts))
+print(user.dict())
+
+print("\033[31m2. --- 校验失败处理 ---\033[0m")
+try:
+    User(id=1, signup_ts=datetime.today(), friends=[1, 2, "not number"])
+except ValidationError as e:
+    print(e.json())
+
+print("\033[31m3. --- 模型类的的属性和方法 ---\033[0m")
+print(user.dict())
+print(user.json())
+print(user.copy())  # 这里是浅拷贝
+print(User.parse_obj(external_data))
+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"]}')
+print(User.parse_file(path))
+
+print(user.schema())
+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
+print(User.construct(**user_data))  # 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.__fields__.keys())  # 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("\033[31m4. --- 递归模型 ---\033[0m")
+
+
+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())
+
+print("\033[31m5. --- ORM模型：从类实例创建符合ORM对象的模型  ---\033[0m")
+
+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
+    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))
+
+print("\033[31m6. --- 错误处理 ---\033[0m")
+
+user_data = {"id": "error", "signup_ts": "2020-12-22 12 22", "friends": [1, 2, 3]}  # id是字符串 是错误的
+
+try:
+    User(**user_data)
+except ValidationError as e:
+    print(e)
+    print("- " * 30)
+    print(e.json())
+    print("- " * 30)
+    print(e.errors())
+
+print("\033[31m7. --- Pydantic支撑的字段类型  ---\033[0m")  # 官方文档：https://pydantic-docs.helpmanual.io/usage/types/