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Pythons attrs-Bibliothek ist ein Game-Changer für Entwickler, die die Klassenerstellung vereinfachen und den Boilerplate-Code reduzieren möchten. Dieser Bibliothek vertraut sogar die NASA.
Attrs wurde 2015 von Hynek Schlawack entwickelt und hat sich aufgrund seiner Fähigkeit, automatisch spezielle Methoden zu generieren und eine saubere, deklarative Möglichkeit zum Definieren von Klassen bereitzustellen, schnell zu einem beliebten Tool unter Python-Entwicklern entwickelt.
Datenklassen sind eine Art Teilmenge von Attributen.
Warum Attrs nützlich sind:
Installation:
Um mit attrs zu beginnen, können Sie es mit pip:
installieren
pip install attrs
Grundlegende Verwendung:
Hier ist ein einfaches Beispiel für die Verwendung von Attrs zum Definieren einer Klasse:
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 generiert automatisch die Methoden init, repr und eq für Ihre Klassen:
@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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