Python의 속성 라이브러리는 클래스 생성을 단순화하고 상용구 코드를 줄이려는 개발자를 위한 획기적인 제품입니다. 이 라이브러리는 NASA에서도 신뢰받고 있습니다.
2015년 Hynek Schlawack이 만든 attrs는 특수 메서드를 자동으로 생성하고 클래스를 정의하는 깔끔하고 선언적인 방법을 제공하는 기능으로 인해 Python 개발자들 사이에서 빠르게 선호되는 도구가 되었습니다.
데이터클래스는 일종의 속성 하위 집합입니다.
속성이 유용한 이유:
설치:
attrs를 시작하려면 pip를 사용하여 설치할 수 있습니다.
pip install attrs
기본 사용법:
다음은 attrs를 사용하여 클래스를 정의하는 방법에 대한 간단한 예입니다.
import attr @attr.s class Person: name = attr.ib() age = attr.ib() # Creating an instance person = Person("Alice", 30) print(person) # Person(name='Alice', age=30)
attrs는 클래스에 대한 init, repr 및 eq 메소드를 자동으로 생성합니다.
@attr.s class Book: title = attr.ib() author = attr.ib() year = attr.ib() book1 = Book("1984", "George Orwell", 1949) book2 = Book("1984", "George Orwell", 1949) print(book1) # Book(title='1984', author='George Orwell', year=1949) print(book1 == book2) # True
import attr from typing import List @attr.s class Library: name = attr.ib(type=str) books = attr.ib(type=List[str], default=attr.Factory(list)) capacity = attr.ib(type=int, default=1000) library = Library("City Library") print(library) # Library(name='City Library', books=[], capacity=1000)
import attr def must_be_positive(instance, attribute, value): if value <= 0: raise ValueError("Value must be positive") @attr.s class Product: name = attr.ib() price = attr.ib(converter=float, validator=[attr.validators.instance_of(float), must_be_positive]) product = Product("Book", "29.99") print(product) # Product(name='Book', price=29.99) try: Product("Invalid", -10) except ValueError as e: print(e) # Value must be positive
import attr @attr.s class User: username = attr.ib() _password = attr.ib(repr=False) # Exclude from repr @property def password(self): return self._password @password.setter def password(self, value): self._password = hash(value) # Simple hashing for demonstration user = User("alice", "secret123") print(user) # User(username='alice')
@attr.s(frozen=True) # slots=True is the default class Point: x = attr.ib() y = attr.ib() point = Point(1, 2) try: point.x = 3 # This will raise an AttributeError except AttributeError as e: print(e) # can't set attribute
import attr import uuid @attr.s class Order: id = attr.ib(factory=uuid.uuid4) items = attr.ib(factory=list) total = attr.ib(init=False) def __attrs_post_init__(self): self.total = sum(item.price for item in self.items) @attr.s class Item: name = attr.ib() price = attr.ib(type=float) order = Order(items=[Item("Book", 10.99), Item("Pen", 1.99)]) print(order) # Order(id=UUID('...'), items=[Item(name='Book', price=10.99), Item(name='Pen', price=1.99)], total=12.98)
Library | Features | Performance | Community |
---|---|---|---|
attrs | Automatic method generation, attribute definition with types and default values, validators and converters | Better performance than manual code | Active community |
pydantic | Data validation and settings management, automatic method generation, attribute definition with types and default values, validators and converters | Good performance | Active community |
dataclasses | Built into Python 3.7+, making them more accessible | Tied to the Python version | Built-in Python library |
attrs and dataclasses are faster than pydantic1.
Performance:
attrs generally offers better performance than manually written classes or other libraries due to its optimized implementations.
Real-world example:
from attr import define, Factory from typing import List, Optional @define class Customer: id: int name: str email: str orders: List['Order'] = Factory(list) @define class Order: id: int customer_id: int total: float items: List['OrderItem'] = Factory(list) @define class OrderItem: id: int order_id: int product_id: int quantity: int price: float @define class Product: id: int name: str price: float description: Optional[str] = None # Usage customer = Customer(1, "Alice", "alice@example.com") product = Product(1, "Book", 29.99, "A great book") order_item = OrderItem(1, 1, 1, 2, product.price) order = Order(1, customer.id, 59.98, [order_item]) customer.orders.append(order) print(customer)
attrs is a powerful library that simplifies Python class definitions while providing robust features for data validation and manipulation. Its ability to reduce boilerplate code, improve readability, and enhance performance makes it an invaluable tool for Python developers.
Community resources:
Try attrs in your next project and experience its benefits firsthand. Share your experiences with the community and contribute to its ongoing development. Happy coding!
https://stefan.sofa-rockers.org/2020/05/29/attrs-dataclasses-pydantic/ ↩
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