我使用 pydantic 的数据类装饰器创建了一个类,我想在参数成为该类的属性之前检查参数的类型。这是我的代码:
from pydantic.dataclasses import dataclass
from pydantic import validator
@dataclass
class Person(object):
name: Optional[str] = None
@validator('name')
def name_must_be_str(cls, v):
if type(v) is not str:
raise TypeError("'name' must be str, not " + type(v).__name__)
return v
现在,当我创建像 person = Person(12) 这样的实例时,参数也变成字符串('12')。在实例将参数转换为字符串之前如何检查类型?
我以前从未使用过 pydantic,因此以下可能不是最好的解决方案,但根据 docs,您可以使用
__post_init__
dunder 方法 dataclass
以便在值转换为之前运行代码指定类型:
from typing import Optional
from pydantic.dataclasses import dataclass
from pydantic import validator
@dataclass
class Person:
name: Optional[str] = None
def __post_init__(self):
if not isinstance(self.name, str):
print(f'Careful! Your name, {self.name}, is not a string!')
@validator('name')
def name_must_be_str(cls, v):
if type(v) is not str:
raise TypeError("'name' must be str, not " + type(v).__name__)
return v
print(Person(1))
# Careful! Your name, 1, is not a string!
# Person(name='1')
还有预验证器,可以指定为
@validator('name', pre=True)
,它们也在强制转换之前运行代码:
@dataclass
class Person:
name: Optional[str] = None
@validator('name', pre=True)
def name_must_be_str(cls, v):
if type(v) is not str:
raise TypeError("'name' must be str, not " + type(v).__name__)
return v
print(Person(1))
但对我来说,他们由于某种原因返回两个相同的错误:
ValidationError: 2 validation errors
name
'name' must be str, not int (type=type_error)
name
'name' must be str, not int (type=type_error)