Python使用Pydantic实现数据校验的最佳实践

def register_user(data):
    if not data.get('username'):
        raise ValueError('用户名不能为空')
    if not isinstance(data.get('age'), int):
        raise ValueError('年龄必须是数字')
    if data.get('age') < 18:
        raise ValueError('年龄必须大于18岁')
    # ... 继续校验邮箱、手机号等等
    # 校验通过后，才真正开始处理业务逻辑

from pydantic import BaseModel, Field

class User(BaseModel):
    username: str
    age: int = Field(ge=18)
    email: str

def register_user(data):
    user = User(**data)
    # 校验已经自动完成，这里只管业务逻辑
    save_to_database(user)

from pydantic import BaseModel

class Product(BaseModel):
    price: float
    quantity: int

data = {"price": "19.99", "quantity": "3"}
product = Product(**data)

print(product.price)      # 19.99，已经是float
print(product.quantity)   # 3，已经是int

from pydantic import BaseModel
from typing import Optional

class UserProfile(BaseModel):
    username: str
    age: int
    phone: Optional[str] = None
    is_active: bool = True

data = {"username": "张三", "age": 25}
profile = UserProfile(**data)
print(profile.phone)      # None
print(profile.is_active)  # True

from pydantic import BaseModel, Field

class Product(BaseModel):
    name: str = Field(min_length=1, max_length=100)
    price: float = Field(gt=0, description="价格必须大于0")
    rating: int = Field(ge=1, le=5)
    tags: list[str] = Field(max_items=10)

from pydantic import BaseModel, field_validator
import re

class Account(BaseModel):
    username: str
    password: str
    confirm_password: str

    @field_validator('username')
    def username_alphanumeric(cls, v):
        if not v.isalnum():
            raise ValueError('用户名只能包含字母和数字')
        if len(v) < 3:
            raise ValueError('用户名至少3个字符')
        return v.lower()  # 可以顺便做规范化

    @field_validator('confirm_password')
    def passwords_match(cls, v, info):
        if v != info.data.get('password'):
            raise ValueError('两次输入的密码不一致')
        return v

from pydantic import BaseModel
from typing import List

class Address(BaseModel):
    street: str
    city: str
    zip_code: str

class OrderItem(BaseModel):
    product_id: int
    quantity: int
    price: float

class Order(BaseModel):
    order_id: str
    address: Address
    items: List[OrderItem]
    total: float

order_data = {
    "order_id": "ORD-001",
    "address": {"street": "123 Main St", "city": "Beijing", "zip_code": "100000"},
    "items": [
        {"product_id": 1, "quantity": 2, "price": 19.99},
        {"product_id": 2, "quantity": 1, "price": 49.99}
    ],
    "total": 89.97
}

order = Order(**order_data)

from pydantic import BaseModel, Field, field_validator
from datetime import datetime

class APIResponse(BaseModel):
    user_id: int = Field(alias="id")
    full_name: str = Field(alias="name")
    created_at: datetime

    @field_validator('created_at', mode='before')
    def parse_date(cls, v):
        # 处理各种奇怪的日期格式
        if isinstance(v, str):
            return v.replace('Z', '+00:00')
        return v