Python の attrs ライブラリは、クラスの作成を簡素化し、定型コードを削減したいと考えている開発者にとって状況を大きく変えるものです。このライブラリは NASA からも信頼されています。
2015 年に Hynek Schlawack によって作成された attrs は、特別なメソッドを自動的に生成し、クラスを定義するクリーンで宣言的な方法を提供する機能により、すぐに Python 開発者の間で人気のツールになりました。
dataclasses は属性のサブセットの一種です。
attrs が役立つ理由:
インストール:
attrs を使い始めるには、pip:
を使用してインストールできます。
pip install attrs
基本的な使用法:
以下は、attrs を使用してクラスを定義する方法の簡単な例です:
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)
attrs は、クラスの init、repr、および eq メソッドを自動的に生成します。
@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/ ↩
以上がattrsをもっと使用する必要がある理由の詳細内容です。詳細については、PHP 中国語 Web サイトの他の関連記事を参照してください。