Python的dataclasses模塊通過(guò)自動(dòng)生成常見(jiàn)方法減少冗餘代碼，提高開(kāi)發(fā)效率。 ①使用@dataclass裝飾器可自動(dòng)創(chuàng)建__init__、__repr__、__eq__等方法；②支持字段默認(rèn)值和default_factory處理可變類型；③允許自定義__post_init__驗(yàn)證邏輯及設(shè)置frozen、order參數(shù)控制實(shí)例行為。該模塊適用於簡(jiǎn)化數(shù)據(jù)容器類的定義，同時(shí)保留靈活性與可控性。

Python's dataclasses module, introduced in Python 3.7, makes it easier to define classes that are primarily used to store data. Instead of writing repetitive boilerplate code like __init__ , __repr__ , or __eq__ methods, you can use the @dataclass decorator to automatically generate them.

Automatically Generates Common Methods

Without dataclasses , you'd typically write something like this:

 class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __repr__(self):
        return f"Person(name={self.name!r}, age={self.age})"

    def __eq__(self, other):
        if not isinstance(other, Person):
            return False
        return self.name == other.name and self.age == other.age

With dataclasses , you just declare the fields:

 from dataclasses import dataclass

@dataclass
class Person:
    name: str
    age: int

This automatically creates the __init__ , __repr__ , and __eq__ methods for you — no more manual typing of repetitive code.

Supports Default Values and Mutability

You can assign default values directly in the class definition:

 @dataclass
class Product:
    name: str
    price: float = 9.99
    in_stock: bool = True

Now when you create a Product instance, you don't have to specify every field:

 p = Product("T-shirt")
# p has name='T-shirt', price=9.99, in_stock=True

If you need more complex defaults — like mutable types such as lists or dicts — use the default_factory parameter:

 from dataclasses import field

@dataclass
class Inventory:
    items: list = field(default_factory=list)

That way, each instance gets its own separate list instead of sharing one across all instances (which would happen if you wrote items=[] directly).

Lets You Customize Behavior When Needed

Even though dataclasses auto-generates a lot, you're not locked in — you can still define your own methods or override generated ones.

For example, if you want custom validation when setting attributes:

 @dataclass
class Rectangle:
    width: int
    height: int

    def __post_init__(self):
        if self.width <= 0 or self.height <= 0:
            raise ValueError("Dimensions must be positive")

The __post_init__ method runs after the auto-generated __init__ , so you can add logic like validation or computed properties there.

Also, you can tweak how the decorator behaves by passing arguments:

• @dataclass(frozen=True) makes instances immutable.
• @dataclass(order=True) adds comparison operators like , <code>> , etc.

Basically, dataclasses cuts down on repetitive code while keeping flexibility. It's especially handy for simple data containers where behavior is minimal. Not magic, but definitely helpful once you get into the rhythm of using it.

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