为什么你应该更多地使用 attrs

介绍

Python 的 attrs 库对于希望简化类创建并减少样板代码的开发人员来说是一个游戏规则改变者。这个库甚至受到 NASA 的信任。
attrs 由 Hynek Schlawack 于 2015 年创建，因其能够自动生成特殊方法并提供干净的声明式方式来定义类，而迅速成为 Python 开发人员最喜欢的工具。
数据类是属性的一种子集。

为什么 attrs 有用：

• 减少样板代码
• 提高代码可读性和可维护性
• 提供强大的数据验证和转换功能
• 通过优化实施提高性能

2. 属性入门

安装：
要开始使用 attrs，您可以使用 pip 安装它：

pip install attrs

基本用法：
这是一个如何使用 attrs 定义类的简单示例：

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)

3. attrs的核心特性

一个。自动方法生成：

attrs 会自动为您的类生成 init、repr 和 eq 方法：

@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

b.具有类型和默认值的属性定义：

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)

c.验证器和转换器：

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

4. 高级使用

一个。自定义属性行为：

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')

b.冻结的实例和插槽：

@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

c.工厂函数和初始化后处理：

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)

5. 最佳实践和常见陷阱

最佳实践：

• 使用类型注释以获得更好的代码可读性和 IDE 支持
• 利用验证器确保数据完整性
• 对不可变对象使用冻结类
• 利用自动方法生成来减少代码重复

常见陷阱：

• 忘记在类上使用 @attr.s 装饰器
• 过度使用可能是单独方法的复杂验证器
• 不考虑大量使用工厂函数对性能的影响

6. attrs 与其他库

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.

Comparison with dataclasses:

• attrs is more feature-rich and flexible
• dataclasses are built into Python 3.7+, making them more accessible
• attrs has better performance in most cases
• dataclasses are tied to the Python version, while attrs as an external library can be used with any Python version.

Comparison with pydantic:

• pydantic is focused on data validation and settings management
• attrs is more general-purpose and integrates better with existing codebases
• pydantic has built-in JSON serialization, while attrs requires additional libraries

When to choose attrs:

• For complex class hierarchies with custom behaviors
• When you need fine-grained control over attribute definitions
• For projects that require Python 2 compatibility (though less relevant now)

7. Performance and Real-world Applications

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)

8. Conclusion and Call to Action

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:

• GitHub repository: https://github.com/python-attrs/attrs
• Documentation: https://www.attrs.org/
• PyPI page: https://pypi.org/project/attrs/

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!

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1. https://stefan.sofa-rockers.org/2020/05/29/attrs-dataclasses-pydantic/ ↩

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