python data_error ai_generated true

pydantic_core._pydantic_core.ValidationError: 1 validation error for User age Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='twenty', input_type=str]

ID: python/pydantic-validation-error-input-type

Also available as: JSON · Markdown · 中文
80%Fix Rate
88%Confidence
0Evidence
2024-06-12First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
2.x active — — —

Root Cause

Pydantic v2 strict type coercion: a string field was passed where an int was declared, and the string is not a numeric literal so Pydantic refuses to coerce it. Unlike v1, v2 no longer silently coerces arbitrary strings to ints by default.

generic

中文

Pydantic v2 严格类型转换：声明为 int 的字段收到了字符串，且该字符串不是数字字面量，因此 Pydantic 拒绝转换。与 v1 不同，v2 默认不再将任意字符串静默转换为整数。

Workarounds

  1. 85% success 
    from pydantic import BaseModel, field_validator

class User(BaseModel):
    age: int

    @field_validator('age', mode='before')
    @classmethod
    def parse_age(cls, v):
        if isinstance(v, str) and v.isalpha():
            words = {'twenty': 20, 'thirty': 30}
            return words.get(v.lower(), v)
        return v
  2. 80% success 
    from typing import Union
from pydantic import BaseModel, BeforeValidator
from typing_extensions import Annotated

CoercedInt = Annotated[int, BeforeValidator(lambda v: int(v) if str(v).isdigit() else v)]

class User(BaseModel):
    age: CoercedInt

Dead Ends

Common approaches that don't work:

  1. 90% fail

    Silently swallowing ValidationError means the invalid object never gets constructed; downstream code receives None or crashes with AttributeError later.

  2. 75% fail

    strict=False only relaxes some coercions; 'twenty' is not a valid integer representation and will still raise int_parsing.

  3. 60% fail

    v1 also raises for non-numeric strings like 'twenty'; the behavior difference is only for numeric-looking strings such as '25'.