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python库pydantic教程

青火_ 人气:9

一、简介

pydantic 库是 python 中用于数据接口定义检查与设置管理的库。

pydantic 在运行时强制执行类型提示，并在数据无效时提供友好的错误。

它具有如下优点：

二、安装

pip install pydantic

要测试 pydantic 是否已编译，请运行：

import pydantic
print('compiled:', pydantic.compiled)

支持使用dotenv文件获取配置，需要安装 python-dotenv

pip install pydantic[dotenv]

三、常见模型

pydantic中定义对象都是通过模型的，你可以认为模型就是类型语言中的类型。

1、BaseModel 基本模型

from pydantic import BaseModel
class User(BaseModel):
    id: int
    name = 'Jane Doe'

上面的例子，定义了一个User模型，继承自BaseModel，有2个字段，id是一个整数并且是必需的，name是一个带有默认值的字符串并且不是必需的

实例化使用：

user = User(id='123')

实例化将执行所有解析和验证，如果有错误则会触发 ValidationError 报错。

模型具有以下属性：

2、递归模型

可以使用模型本身作为注释中的类型来定义更复杂的数据结构。

from typing import List
from pydantic import BaseModel

class Foo(BaseModel):
    count: int
    size: float = None

class Bar(BaseModel):
    apple = 'x'
    banana = 'y'

class Spam(BaseModel):
    foo: Foo
    bars: List[Bar]

3、GenericModel 通用模型（泛型）：

使用 typing.TypeVar 的实例作为参数，传递给 typing.Generic，然后在继承了pydantic.generics.GenericModel 的模型中使用：

from typing import Generic, TypeVar, Optional, List

from pydantic import BaseModel, validator, ValidationError
from pydantic.generics import GenericModel

DataT = TypeVar('DataT')

class Error(BaseModel):
    code: int
    message: str

class DataModel(BaseModel):
    numbers: List[int]
    people: List[str]

class Response(GenericModel, Generic[DataT]):
    data: Optional[DataT]
    error: Optional[Error]

    @validator('error', always=True)
    def check_consistency(cls, v, values):
        if v is not None and values['data'] is not None:
            raise ValueError('must not provide both data and error')
        if v is None and values.get('data') is None:
            raise ValueError('must provide data or error')
        return v

data = DataModel(numbers=[1, 2, 3], people=[])
error = Error(code=404, message='Not found')

print(Response[int](data=1))
#> data=1 error=None
print(Response[str](data='value'))
#> data='value' error=None
print(Response[str](data='value').dict())
#> {'data': 'value', 'error': None}
print(Response[DataModel](data=data).dict())
"""
{
    'data': {'numbers': [1, 2, 3], 'people': []},
    'error': None,
}
"""
print(Response[DataModel](error=error).dict())
"""
{
    'data': None,
    'error': {'code': 404, 'message': 'Not found'},
}
"""
try:
    Response[int](data='value')
except ValidationError as e:
    print(e)
    """
    2 validation errors for Response[int]
    data
      value is not a valid integer (type=type_error.integer)
    error
      must provide data or error (type=value_error)
    """

4、create_model 动态模型

在某些情况下，直到运行时才知道模型的结构。为此 pydantic 提供了create_model允许动态创建模型的方法。

from pydantic import BaseModel, create_model
DynamicFoobarModel = create_model('DynamicFoobarModel', foo=(str, ...), bar=123)

四、常用类型

五、验证器

使用validator装饰器可以实现自定义验证和对象之间的复杂关系。

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):
        if ' ' not in v:
            raise ValueError('must contain a space')
        return v.title()

    @validator('password2')
    def passwords_match(cls, v, values, **kwargs):
        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

user = UserModel(
    name='samuel colvin',
    username='scolvin',
    password1='zxcvbn',
    password2='zxcvbn',
)
print(user)
#> name='Samuel Colvin' username='scolvin' password1='zxcvbn' password2='zxcvbn'

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)
    """

关于验证器的一些注意事项：

from typing import List
from pydantic import BaseModel, ValidationError, validator

class ParentModel(BaseModel):
    names: List[str]

class ChildModel(ParentModel):
    @validator('names', each_item=True)
    def check_names_not_empty(cls, v):
        assert v != '', 'Empty strings are not allowed.'
        return v

# This will NOT raise a ValidationError because the validator was not called
try:
    child = ChildModel(names=['Alice', 'Bob', 'Eve', ''])
except ValidationError as e:
    print(e)
else:
    print('No ValidationError caught.')
    #> No ValidationError caught.


class ChildModel2(ParentModel):
    @validator('names')
    def check_names_not_empty(cls, v):
        for name in v:
            assert name != '', 'Empty strings are not allowed.'
        return v

try:
    child = ChildModel2(names=['Alice', 'Bob', 'Eve', ''])
except ValidationError as e:
    print(e)
    """
    1 validation error for ChildModel2
    names
      Empty strings are not allowed. (type=assertion_error)
    """
from pydantic import BaseModel, validator

def normalize(name: str) -> str:
    return ' '.join((word.capitalize()) for word in name.split(' '))

class Producer(BaseModel):
    name: str

    # validators
    _normalize_name = validator('name', allow_reuse=True)(normalize)

class Consumer(BaseModel):
    name: str
    # validators
    _normalize_name = validator('name', allow_reuse=True)(normalize)

六、配置

如果您创建一个继承自BaseSettings的模型，模型初始化程序将尝试通过从环境中读取，来确定未作为关键字参数传递的任何字段的值。（如果未设置匹配的环境变量，则仍将使用默认值。）

这使得很容易：

from typing import Set

from pydantic import (
    BaseModel,
    BaseSettings,
    PyObject,
    RedisDsn,
    PostgresDsn,
    Field,
)

class SubModel(BaseModel):
    foo = 'bar'
    apple = 1

class Settings(BaseSettings):
    auth_key: str
    api_key: str = Field(..., env='my_api_key')

    redis_dsn: RedisDsn = 'redis://user:pass@localhost:6379/1'
    pg_dsn: PostgresDsn = 'postgres://user:pass@localhost:5432/foobar'

    special_function: PyObject = 'math.cos'

    # to override domains:
    # export my_prefix_domains='["foo.com", "bar.com"]'
    domains: Set[str] = set()

    # to override more_settings:
    # export my_prefix_more_settings='{"foo": "x", "apple": 1}'
    more_settings: SubModel = SubModel()

    class Config:
        env_prefix = 'my_prefix_'  # defaults to no prefix, i.e. ""
        fields = {
            'auth_key': {
                'env': 'my_auth_key',
            },
            'redis_dsn': {
                'env': ['service_redis_dsn', 'redis_url']
            }
        }

print(Settings().dict())
"""
{
    'auth_key': 'xxx',
    'api_key': 'xxx',
    'redis_dsn': RedisDsn('redis://user:pass@localhost:6379/1',
scheme='redis', user='user', password='pass', host='localhost',
host_type='int_domain', port='6379', path='/1'),
    'pg_dsn': PostgresDsn('postgres://user:pass@localhost:5432/foobar',
scheme='postgres', user='user', password='pass', host='localhost',
host_type='int_domain', port='5432', path='/foobar'),
    'special_function': <built-in function cos>,
    'domains': set(),
    'more_settings': {'foo': 'bar', 'apple': 1},
}
"""

支持 Dotenv 文件设置变量，pydantic 有两种方式加载它：

class Settings(BaseSettings):
    ...

    class Config:
        env_file = '.env'
        env_file_encoding = 'utf-8'

或者

settings=Settings(_env_file='prod.env',_env_file_encoding='utf-8')

即使使用 dotenv 文件，pydantic 仍会读取环境变量，环境变量将始终优先于从 dotenv 文件加载的值。

pydantic 支持设置敏感信息文件，同样有2种方式加载：

class Settings(BaseSettings):
    ...
    database_password: str
    class Config:
        secrets_dir = '/var/run'

或者：

settings = Settings(_secrets_dir='/var/run')

即使使用 secrets 目录，pydantic仍会从 dotenv 文件或环境中读取环境变量，dotenv 文件和环境变量将始终优先于从 secrets 目录加载的值。

七、与 mypy 一起使用

Pydantic 附带了一个 mypy 插件，向 mypy 添加了许多重要的特定于 pydantic 的功能，以提高其对代码进行类型检查的能力。

例如以下脚本：

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

class Model(BaseModel):
    age: int
    first_name = 'John'
    last_name: NoneStr = None
    signup_ts: Optional[datetime] = None
    list_of_ints: List[int]

m = Model(age=42, list_of_ints=[1, '2', b'3'])
print(m.middle_name)  # not a model field!
Model()  # will raise a validation error for age and list_of_ints

在没有任何特殊配置的情况下，mypy 会捕获其中一个错误：

13: error: "Model" has no attribute "middle_name"

启用插件后，它会同时捕获：

13: error: "Model" has no attribute "middle_name"
16: error: Missing named argument "age" for "Model"
16: error: Missing named argument "list_of_ints" for "Model"

要启用该插件，只需添加pydantic.mypy到mypy 配置文件中的插件列表：

[mypy]
plugins = pydantic.mypy

要更改插件设置的值，请​​在 mypy 配置文件中创建一个名为 的部分[pydantic-mypy]，并为要覆盖的设置添加键值对：

[mypy]
plugins = pydantic.mypy

follow_imports = silent
warn_redundant_casts = True
warn_unused_ignores = True
disallow_any_generics = True
check_untyped_defs = True
no_implicit_reexport = True

# for strict mypy: (this is the tricky one :-))
disallow_untyped_defs = True

[pydantic-mypy]
init_forbid_extra = True
init_typed = True
warn_required_dynamic_aliases = True
warn_untyped_fields = True

总结

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