Python

In Python, comparing objects can be trickier than it seems. By default, objects are compared based on their memory address, not the values they hold. This can lead to unexpected results when comparing custom objects. Thankfully, Python’s magic methods provide a way to define custom equality and comparison behaviors, unlocking powerful features like object sorting […]

Attribute access in Python is a fundamental aspect of object-oriented programming (OOP). Python’s “magic methods” offer a powerful way to customize how attributes are accessed and manipulated within your classes. This guide will dive into three essential magic methods (__getattr__, __setattr__, and __getattribute__) that allow you to control the retrieval and assignment of attribute values,

Python’s flexibility shines in its ability to make callable objects, which are objects that behave like functions. This means you can use parentheses () to “call” them, passing arguments and receiving results, just as you would with regular functions. By mastering this technique, you can write more concise, expressive, and object-oriented code. In this guide,

Data classes were introduced in Python 3.7 as a streamlined way to create classes primarily designed for storing and manipulating data. They eliminate boilerplate code, automatically generate common methods, and make your code more readable and maintainable. This guide will walk you through the steps of defining and using data classes in Python, highlighting their

Python’s data classes offer a streamlined way to create classes for storing data. However, sometimes you need to perform additional actions or calculations after an object has been initialized with its basic attributes. This is where post-initialization in Python comes into play. In this guide, we’ll delve into how to leverage the __post_init__ method in