Python @dataclass

from dataclasses import dataclass

def validate_str(max_length):
    def _validate(f):
        def wrapper(self, value):
            if type(value) is not str:
                raise TypeError(f"Expected str, got: {type(value)}")
            elif len(value) > max_length:
                raise ValueError(f"Expected string of max length {max_length}, got string of length {len(value)} : {value}")
            f(self, value)
        return wrapper
    return _validate

@dataclass
class Person:
    name: str
    @validate_str(10)
    def address(self, value):
        self.address = value

class Student:
    def __init__(self, nume, nota):
        self.nume = nume
        self.nota = nota

    def __gt__(self, other):
        return self.nota > other.nota

Apoi puteți compara doi studenți folosind >:

s1 = Student("Ana", 9)
s2 = Student("Bogdan", 8)
print(s1 > s2) # True  

În acest caz, s1.__gt__(s2) este echivalent cu s1 > s2.

Dar nu puteți folosi __gt__() pentru a compara lungimea unei valori cu o valoare maximă,

deoarece acestea nu sunt obiecte personalizate care au definit __gt__().

În schimb, trebuie să folosiți operatorul > direct.

How to make a field required in a Python dataclass, similar to the required option in a Mongoose schema. One way to make a field required in a Python dataclass is to use the field function from the dataclasses module and set the default or default_factory arguments to dataclasses.MISSING. This will raise a TypeError if the field is not provided when creating an instance of the class. For example:

from dataclasses import dataclass, field, MISSING

@dataclass
class Person:
    name: str = field(default=MISSING) # name is required
    age: int = 0 # age has a default value of 0
    hobbies: list[str] = field(default_factory=list) # hobbies is an empty list by default

p1 = Person(name="Alice", age=25, hobbies=["reading", "writing"]) # valid
p2 = Person(name="Bob") # valid, age and hobbies are set to default values
p3 = Person(age=30, hobbies=["gaming", "coding"]) # invalid, name is missing

Another way to make a field required in a Python dataclass is to use a validator function that checks if the field is None or has some other invalid value, and raise an exception if so. You can write your own validator function, or use a library like pydantic or attrs that provides validation features. For example, using pydantic:

from pydantic.dataclasses import dataclass

@dataclass
class Person:
    name: str # name is required, pydantic will raise a ValidationError if it is missing or not a string
    age: int = 0 # age has a default value of 0
    hobbies: list[str] = None # hobbies is optional, but must be a list of strings if provided

p1 = Person(name="Alice", age=25, hobbies=["reading", "writing"]) # valid
p2 = Person(name="Bob") # valid, age is set to default value and hobbies is None
p3 = Person(age=30, hobbies=["gaming", "coding"]) # invalid, name is missing
p4 = Person(name="Charlie", age="twenty", hobbies=42) # invalid, age and hobbies have wrong types