常见触发错误的情况

• 如果传入的字段多了会自动过滤
• 如果传入的少了会报错，必填字段
• 如果传入的字段名称对不上也会报错
• 如果传入的类型不对会自动转换，如果不能转换则会报错

 

错误的触发

pydantic 会在它正在验证的数据中发现错误时引发 ValidationError

 

注意

• 验证代码不应该抛出 ValidationError 本身
• 而是应该抛出 ValueError、TypeError、AssertionError 或他们的子类
• ValidationError 会包含所有错误及其发生方式的信息

 

访问错误的方式

• e.errors()：返回输入数据中发现的错误的列表
• e.json()：以 JSON 格式返回错误（推荐）
• str(e)：以人类可读的方式返回错误

 

简单栗子

# 一定要导入 ValidationError
from pydantic import BaseModel, ValidationError


class Person(BaseModel):
    id: int
    name: str


try:
    # id是个int类型，如果不是int或者不能转换int会报错
    p = Person(id="ss", name="hallen")  
except ValidationError as e:
　　# 打印异常消息
    print(e.errors())

 

e.errors() 的输出结果

[{'loc': ('id',), 'msg': 'value is not a valid integer', 'type': 'type_error.integer'}]

 

e.json() 的输出结果

[
  {
    "loc": [
      "id"
    ],
    "msg": "value is not a valid integer",
    "type": "type_error.integer"
  }
]

 

str(e) 的输出结果

1 validation error for Person
id
  value is not a valid integer (type=type_error.integer)

 

复杂栗子

class Location(BaseModel):
    lat = 0.1
    lng = 10.1


class Model(BaseModel):
    is_required: float
    gt_int: conint(gt=42)
    list_of_ints: List[int] = None
    a_float: float = None
    recursive_model: Location = None


data = dict(
    list_of_ints=['1', 2, 'bad'],
    a_float='not a float',
    recursive_model={'lat': 4.2, 'lng': 'New York'},
    gt_int=21
)

try:
    Model(**data)
except ValidationError as e:
    print(e.json(indent=4))

 

输出结果

[
    {
        "loc": [
            "is_required"
        ],
        "msg": "field required",
        "type": "value_error.missing"
    },
    {
        "loc": [
            "gt_int"
        ],
        "msg": "ensure this value is greater than 42",
        "type": "value_error.number.not_gt",
        "ctx": {
            "limit_value": 42
        }
    },
    {
        "loc": [
            "list_of_ints",
            2
        ],
        "msg": "value is not a valid integer",
        "type": "type_error.integer"
    },
    {
        "loc": [
            "a_float"
        ],
        "msg": "value is not a valid float",
        "type": "type_error.float"
    },
    {
        "loc": [
            "recursive_model",
            "lng"
        ],
        "msg": "value is not a valid float",
        "type": "type_error.float"
    }
]

• value_error.missing：必传字段缺失
• value_error.number.not_gt：字段值没有大于 42
• type_error.integer：字段类型错误，不是 integer

 

自定义错误

# 导入 validator
from pydantic import BaseModel, ValidationError, validator


class Model(BaseModel):
    foo: str

    # 验证器
    @validator('foo')
    def name_must_contain_space(cls, v):
        if v != 'bar':
            # 自定义错误信息
            raise ValueError('value must be bar')
        # 返回传进来的值
        return v


try:
    Model(foo="ber")
except ValidationError as e:
    print(e.json())

 

 

输出结果

[
  {
    "loc": [
      "foo"
    ],
    "msg": "value must be bar",
    "type": "value_error"
  }
]

 

自定义错误模板类

from pydantic import BaseModel, PydanticValueError, ValidationError, validator


class NotABarError(PydanticValueError):
    code = 'not_a_bar'
    msg_template = 'value is not "bar", got "{wrong_value}"'


class Model(BaseModel):
    foo: str

    @validator('foo')
    def name_must_contain_space(cls, v):
        if v != 'bar':
            raise NotABarError(wrong_value=v)
        return v


try:
    Model(foo='ber')
except ValidationError as e:
    print(e.json())

 

输出结果

[
  {
    "loc": [
      "foo"
    ],
    "msg": "value is not \"bar\", got \"ber\"",
    "type": "value_error.not_a_bar",
    "ctx": {
      "wrong_value": "ber"
    }
  }
]

 

PydanticValueError

自定义错误类需要继承这个或者 PydanticTypeError