Python - pydantic（3）错误处理

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