python type_error ai_generated true

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

ID: python/pydantic-type-error-int-parsing-string

Also available as: JSON · Markdown · 中文
80%Fix Rate
89%Confidence
0Evidence
2024-06-05First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
2.x active — — —

Root Cause

Pydantic v2's stricter parsing rejects non-numeric strings for `int` fields where v1 would silently coerce or raise a different message.

generic

中文

Pydantic v2 更严格的解析会拒绝非数字字符串赋给 `int` 字段，而 v1 会静默转换或抛出不同消息。

Workarounds

  1. 90% success 
    Use a custom validator: `@field_validator('age', mode='before')
@classmethod
def parse_age(cls, v):
    return int(v) if str(v).isdigit() else 0`
  2. 88% success 
    Change the field type to `Union[int, str]` and validate downstream, or use `Annotated[int, BeforeValidator(lambda v: int(v) if str(v).isdigit() else 0)]`.

Dead Ends

Common approaches that don't work:

  1. 75% fail

    Wrapping in `try/except ValueError` does not catch `ValidationError` in v2 because it is a separate exception type.

  2. 60% fail

    Using `int(value)` before passing to the model defeats the purpose and still raises for non-numeric input.