Pydantic 数据类句柄 ValidationError

Question

我正在寻找将数据传递到 pydantic 数据类时处理 ValidationError 的解决方案。

from pydantic import BaseModel

class TestB(BaseModel):
    id: str
    price : float
    quantity : int

class TestA(BaseModel):
    orderId: str
    totalAmountPaid: float
    products: List[TestB]

data_correct_type = {"orderId": "12341234", "totalAmountPaid": 395.5,
              "products": [{"id": "abcd0001", "price": '299', "quantity": 1},
                           {"id": "abcd0002", "price": '199', "quantity": 1}]}

data_wrong_type = {"orderId": "12341234", "totalAmountPaid": 395.5,
              "products": [{"id": "abcd0001", "price": '299', "quantity": 1},
                           {"id": "abcd0002", "price": 'abc', "quantity": 1}]}

result_pydantic_1 = TestA(**data_correct_type) #works
result_pydantic_2 = TestA(**data_wrong_type) 

output:
raise 1 validation error for TestA
products -> 1 -> price
  value is not a valid float (type=type_error.float)

有什么方法可以用 None 值之类的东西替换错误类型的字段？谢谢！

Answer 1

代码：

from pydantic import BaseModel, validator
from typing import Union

class P(BaseModel):
    string: str
    number: Union[float, None]

    # pre=True → run before standard validators
    @validator('number', pre=True)
    def set_to_none_if_not_a_numeric(cls, v):
        try:
            return float(v)
        except ValueError:
            print('Not a number!')
            return None

a = P(**{"string": "11", "number": "12aa"})
b = P(**{"string": "11", "number": "12"})

print(a)
print('----')
print(b)

输出：

Not a number!
string='11' number=None
----
string='11' number=12.0

回答

what if I want to do it with all the fields?

我建议首先检查pydantic docs。很详细，有很多很好的例子

关于您的问题的示例：

class DemoModel(BaseModel):
    square_numbers: List[int] = []
    cube_numbers: List[int] = []

    # '*' is the same as 'cube_numbers', 'square_numbers' here:
    @validator('*', pre=True)
    def split_str(cls, v):
        if isinstance(v, str):
            return v.split('|')
        return v

    @validator('cube_numbers', 'square_numbers')
    def check_sum(cls, v):
        if sum(v) > 42:
            raise ValueError('sum of numbers greater than 42')
        return v

所以你可以使用@validator('field1', 'field2')或@validator('*')

回答

However what I want to achieve is for all the field in the dataclass, it will try to convert to the desired type as defined in the dataclass

发挥你的想象力：

代码：

from pydantic import BaseModel, validator
from typing import Optional


class P(BaseModel):
    string_or_none: Optional[str]
    float_or_none: Optional[float]
    int_or_none: Optional[int]
    bool_or_none: Optional[bool]

    @validator('*', pre=True)
    def cast_var_types(cls, value, field):

        allowed_type = field.type_
        try:
            print(f'Try cast {value} to', str(allowed_type))
            return allowed_type(value)
        except ValueError:
            return None


good_values = P(**{"string_or_none": 1234321, "float_or_none": "12.111", "int_or_none": "987", "bool_or_none": 1})
bad_values = P(**{"string_or_none": "this will be good anyway", "float_or_none": "abc", "int_or_none": "bac", "bool_or_none": "any value can be casted to bool"})

print()
print('  ====================================')
print()

for k in ["string_or_none", "float_or_none", "int_or_none", "bool_or_none"]:
    print(k, "of good values is:")
    print('type →', type(getattr(good_values, k)))
    print('value →', getattr(good_values, k))
    print('----------------')
    print(k, "of bad values is:")
    print('type →', type(getattr(bad_values, k)))
    print('value →', getattr(bad_values, k))
    print('----------------')

并输出：

Try cast 1234321 to <class 'str'>
Try cast 12.111 to <class 'float'>
Try cast 987 to <class 'int'>
Try cast 1 to <class 'bool'>
Try cast this will be good anyway to <class 'str'>
Try cast abc to <class 'float'>
Try cast bac to <class 'int'>
Try cast any value can be casted to bool to <class 'bool'>

  ====================================

string_or_none of good values is:
type → <class 'str'>
value → 1234321
----------------
string_or_none of bad values is:
type → <class 'str'>
value → this will be good anyway
----------------
float_or_none of good values is:
type → <class 'float'>
value → 12.111
----------------
float_or_none of bad values is:
type → <class 'NoneType'>
value → None
----------------
int_or_none of good values is:
type → <class 'int'>
value → 987
----------------
int_or_none of bad values is:
type → <class 'NoneType'>
value → None
----------------
bool_or_none of good values is:
type → <class 'bool'>
value → True
----------------
bool_or_none of bad values is:
type → <class 'bool'>
value → True
----------------

Pydantic 数据类句柄 ValidationError

Pydantic dataclass handle ValidationError

python

pydantic

回答

回答