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FastAPI从入门到实战(2)——Pydantic模型

MinChess
2022-11-23 / 0 评论 / 0 点赞 / 1,120 阅读 / 2,007 字 / 正在检测是否收录...

前面了解了一下python的类型提示,这里就接着记录一下Pydantic这个用来执行数据校验的库。而且FastAPI就是基于python的类型提示和Padantic实现的数据验证。

简介

  • 官网:https://pydantic-docs.helpmanual.io/

  • Pydantic就是一个基于Python类型提示来定义数据验证、序列化和文档(使用JSON模式)的库;

  • 使用Python的类型提示来进行数据校验和settings管理;

  • 可以在代码运行的时候提供类型提示,数据校验失败的时候提供友好的错误提示;

  • 定义数据应该如何在纯规范的Python代码中保存,并用Pydantic验证;

基本用法

  • 数据规范的情况

    这里的**符号是为了分配参数用的,可以分配字典

# -*- coding: utf-8 -*-
# @Time: 2022/11/23 15:42
# @Author: MinChess
# @File: pydantic_tutorial.py
# @Software: PyCharm

from pydantic import BaseModel
from datetime import datetime
from typing import List,Optional

class User(BaseModel):
    id: int #必填字段(无默认值的时候,其为必填字段)
    name: str = "MinChess" #有默认值,选填字段
    signup_ts: Optional[datetime] = None
    friends: List[int] = [] # 列表中的元素需要是int类型或者能转换为int类型的str

external_data = {
    "id":"123",
    "signuo_ts": "2011-2-12 12:23:20",
    "friends": [1,2,'3']
}

user = User(**external_data)
print(user.id,user.friends)
print(repr(user.signup_ts))
print(user.dict())

运行结果:

123 [1, 2, 3]
None
{'id': 123, 'name': 'MinChess', 'signup_ts': None, 'friends': [1, 2, 3]}

字符串类型的数据也转为了int型

  • 数据不规范:id不为int类型或可转为int的str类型
external_data = {
    "id":"123h",
    "signuo_ts": "2011-2-12 12:23:20",
    "friends": [1,2,'3']
}

输出:

Traceback (most recent call last):
  File "E:\project\PythonProject\FastApiProject\pydantic_tutorial.py", line 23, in <module>
    user = User(**external_data)
  File "pydantic\main.py", line 342, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for User
id
  value is not a valid integer (type=type_error.integer)

报错说id是int型,这里就可以看出,提示和规范还是非常友好的了

校验失败处理

  • 给属性赋错误类型
try:
    User(id=1,signup_ts=datetime.now(),friends=[1,5,'dsd']) # 直接给属性赋值
except ValidationError as e:
    print(e.json()) # 错误json格式化
  • 输出结果:
[
  {
    "loc": [
      "friends",
      2
    ],
    "msg": "value is not a valid integer",
    "type": "type_error.integer"
  }
]

模型类的属性和方法

  • 解析和转换
print(user.dict()) # 转换为字典
print(user.json()) # 转换为json
print(user.copy()) # 浅copy

print(User.parse_obj(obj=external_data)) # 直接解析字典数据
print(User.parse_raw('{"id": 123, "name": "MinChess", "signup_ts": null, "friends": [1, 2, 3]}')) # 解析标准格式的数据,里面是双引号

path = Path('pydantic_tutorial.json')
path.write_text('{"id": 123, "name": "MinChess", "signup_ts": null, "friends": [1, 2, 3]}')
print(User.parse_file(path)) # 解析文件

print(user.schema()) #此方法用来告诉我们实例的数据格式的方案
print(user.schema_json())

user_data = {"id": 123, "name": "MinChess", "signup_ts": "2022-12-20 12:12:30", "friends": [1, 2, 3]}
print(User.construct(**user_data)) # 此方法不校验数据,直接创建模型类

print(User.__fields__.keys()) # (这里查看所有字段)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
  • 输出:
{'id': 123, 'name': 'MinChess', 'signup_ts': None, 'friends': [1, 2, 3]}
{"id": 123, "name": "MinChess", "signup_ts": null, "friends": [1, 2, 3]}
id=123 name='MinChess' signup_ts=None friends=[1, 2, 3]
id=123 name='MinChess' signup_ts=None friends=[1, 2, 3]
id=123 name='MinChess' signup_ts=None friends=[1, 2, 3]
id=123 name='MinChess' signup_ts=None friends=[1, 2, 3]
{'title': 'User', 'type': 'object', 'properties': {'id': {'title': 'Id', 'type': 'integer'}, 'name': {'title': 'Name', 'default': 'MinChess', 'type': 'string'}, 'signup_ts': {'title': 'Signup Ts', 'type': 'string', 'format': 'date-time'}, 'friends': {'title': 'Friends', 'default': [], 'type': 'array', 'items': {'type': 'integer'}}}, 'required': ['id']}
{"title": "User", "type": "object", "properties": {"id": {"title": "Id", "type": "integer"}, "name": {"title": "Name", "default": "MinChess", "type": "string"}, "signup_ts": {"title": "Signup Ts", "type": "string", "format": "date-time"}, "friends": {"title": "Friends", "default": [], "type": "array", "items": {"type": "integer"}}}, "required": ["id"]}
id=123 name='MinChess' signup_ts='2022-12-20 12:12:30' friends=[1, 2, 3]
dict_keys(['id', 'name', 'signup_ts', 'friends'])

递归模型

  • 创建递归
class Sound(BaseModel):
    sound:str

class Dog(BaseModel):
    birthday: date
    weight: float = Optional[None]
    sound: List[Sound]

dogs = Dog(birthday=date.today(),weight=6.25,sound=[{"sound":"wang wang"},{"sound":"ying ying"}])
print(dogs.json())
  • 输出
{"birthday": "2022-11-23", "sound": [{"sound": "wang wang"}, {"sound": "ying ying"}]}

ORM模型

ORM 全称 Object Relational Mapping, 叫对象关系映射。简单的说,ORM 将数据库中的表与面向对象语言中的类建立了一种对应关系。

  • 利用sqlalchemy创建模型

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 = 12,
    public_key= 'dddddd',
    name= 'MinChess',
    domains=['eeeeeee','fffffff']
)

print(CompanyModel.from_orm(co_orm))
  • 输出
id=12 public_key='dddddd' name='MinChess' domains=['eeeeeee', 'fffffff']

Pydantic支持的所有字段类型

Pydantic支持很多类型的数据,除了常用的那些基本类型外,还有一些不常用的类型,具体参看官网:

https://pydantic-docs.helpmanual.io/usage/types/

image-20221123170940674

源码

# -*- coding: utf-8 -*-
# @Time: 2022/11/23 15:42
# @Author: MinChess
# @File: pydantic_tutorial.py
# @Software: PyCharm

from pydantic import BaseModel,ValidationError,constr
from datetime import datetime,date
from pathlib import Path
from typing import List,Optional
from sqlalchemy import Column,Integer,String
from sqlalchemy.dialects.postgresql import ARRAY
from sqlalchemy.ext.declarative import declarative_base

class User(BaseModel):
    id: int #必填字段(无默认值的时候,其为必填字段)
    name: str = "MinChess" #有默认值,选填字段
    signup_ts: Optional[datetime] = None
    friends: List[int] = [] # 列表中的元素需要是int类型或者能转换为int类型的str

external_data = {
    "id":"123",
    "signuo_ts": "2011-2-12 12:23:20",
    "friends": [1,2,'3']
}

user = User(**external_data)
print("====="*6,'基本使用','====='*6)
print(user.id,user.friends)
print(repr(user.signup_ts))
print(user.dict())

print("====="*6,'校验失败处理','====='*6)
try:
    User(id=1,signup_ts=datetime.now(),friends=[1,5,'dsd']) # 直接给属性赋值
except ValidationError as e:
    print(e.json()) # 错误json格式化

print("====="*6,'模型类的属性和方法','====='*6)
print(user.dict()) # 转换为字典
print(user.json()) # 转换为json
print(user.copy()) # 浅copy

print(User.parse_obj(obj=external_data)) # 直接解析字典数据
print(User.parse_raw('{"id": 123, "name": "MinChess", "signup_ts": null, "friends": [1, 2, 3]}')) # 解析标准格式的数据,里面是双引号

path = Path('pydantic_tutorial.json')
path.write_text('{"id": 123, "name": "MinChess", "signup_ts": null, "friends": [1, 2, 3]}')
print(User.parse_file(path)) # 解析文件

print(user.schema()) #此方法用来告诉我们实例的数据格式的方案
print(user.schema_json())

user_data = {"id": 123, "name": "MinChess", "signup_ts": "2022-12-20 12:12:30", "friends": [1, 2, 3]}
print(User.construct(**user_data)) # 此方法不校验数据,直接创建模型类

print(User.__fields__.keys()) # (这里查看所有字段)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("====="*6,'递归模型','====='*6)

class Sound(BaseModel):
    sound:str

class Dog(BaseModel):
    birthday: date
    weight: float = Optional[None]
    sound: List[Sound]

dogs = Dog(birthday=date.today(),weight=6.25,sound=[{"sound":"wang wang"},{"sound":"ying ying"}])
print(dogs.json())

print("====="*6,'ORM模型','====='*6)

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 = 12,
    public_key= 'dddddd',
    name= 'MinChess',
    domains=['eeeeeee','fffffff']
)

print(CompanyModel.from_orm(co_orm))
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