And 5 ways to make use of it in data science and machine learning

@property is my favorite decorator in Python. I even have been using Python for a few years now, and with each passing 12 months, my expertise and luxury level with the language steadily grows. Amongst all of the techniques and tricks that I’ve learned to write down higher code over time, the property decorator stands out as probably the most remarkable one. It has consistently helped me tackle complex problems and write clean, efficient, and chic code.

The @property decorator is an incredibly powerful feature that enhances the best way you possibly can interact along with your class attributes. It could be a strong method to intertwine attributes and create dependencies amongst them. On this beginner-friendly guide, we’ll explore the underlying concept of properties and dive into 5 alternative ways you should utilize them to supercharge your classes. We’ll also walk through some practical examples in data science and machine learning to solidify your understanding.

Side note: I get so excited after I get to introduce properties to a Python enthusiast for the primary time and see sparkles of their eyes. If that is your first time learning about them, I hope you get sparkles in your eyes too! In case you’re already a seasoned developer and acquainted with the concept, I hope you continue to find something recent on this post. In any case, please let me know within the comments if there may be anything that might have been explained higher or if there are every other essential use cases I can have missed.

TL;DR, in Python, a property is a special attribute that enables controlled access to certain points of an object. Consider this straightforward class that defines a Student with two easy attributes name, and class. we will instantiate and access the name attribute as follows: