How to use Validator in BaseModel Pydantic

About

When you are going to set variables to BaseModel in Pydantic, you might want to validate the type of variables, or value itself.

Code Snippet

from pydantic import BaseModel, validator

class Person(BaseModel):
    name: str
    age: int

    @validator("age", pre=True)
    def convert_age(cls, value):
        if isinstance(value, str):
            try:
                value = int(value)
            except ValueError:
                raise ValueError("Age must be an integer or a string that can be converted to an integer")
        return value

    @validator("age")
    def check_age_range(cls, value):
        if value > 105:
            raise ValueError("you must be already dead")
        if value < 0:
            raise ValueError("you have not born yet")
        return value

For sure we can constrain the range of variables with Feild, but validator is more flexible.

from pydantic import BaseModel, Field

class Person(BaseModel):
    name: str
    age: int = Field(..., ge=0, le=105)