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Clean Code in Python

Creating Classes with a More Compact Syntax

Explore how to write more compact Python classes using the dataclasses module. Understand the use of the @dataclass decorator to automate __init__ creation, how to manage mutable default attributes with field, and implement post-initialization validation. This lesson helps you write clean, maintainable code while reducing boilerplate in class definitions.

We'll cover the following...

Let's continue with the idea that sometimes, we need objects to hold values. There's a common boilerplate in Python when it comes to the initialization of objects, which is to declare in the __init__ method all attributes that the object will have, and then set that to internal variables, typically in this form:

Python 3.8
def __init__(self, x, y, ...):
    self.x = x
    self.y = y

The dataclasses module

Ever since Python 3.7 was released, we can simplify this by using the dataclasses module.

The @dataclass decorator

This module provides a @dataclass decorator, which, when applied to a class, will take all the class attributesThese are attributes shared by all instances of a class. with annotations and treat them as instance attributesThese are attributes attached to a specific instance of a class., as if they were declared in the initialization method. When using this decorator, it will ...