Introduction

Even when working on complex projects like my homelab monitor, I find it useful to keep a "sandbox" script where I can test fundamental Python behaviors. I call this script Classy. It’s a dedicated space for experimenting with data structures and Object-Oriented patterns without the noise of external APIs.

The Experiment

In this iteration of Classy, I focused on three main areas: type-hinted lists, the immutability of tuples, and the scope of nested classes.

1. Type-Hinted Lists

I wanted to see how Python 3.9+ handles type-hinted collection initialization within a class method.

def list_of_fruit() -> str:
    fruits = ["rockmelon", "strawberry", "apple", "mango"]
    print(f"fruits {fruits}")

2. Lists vs. Tuples

A common point of confusion for beginners (and a good refresher for pros) is when to use a Tuple over a List. In Classy, I built a sub-class specifically to compare the two: - Lists: Perfect for dynamic fruit inventories. - Tuples: Ideal for fixed configurations that shouldn't change during runtime.

3. Nested Classes

One of the more interesting patterns I tested was nesting a Tuple class inside the Lists class. While not always the best architectural choice for production, understanding how Python handles nested namespaces is crucial for building robust libraries.

Lessons Learned

Classy reminded me that while Python is flexible, being explicit with your data structures saves a lot of debugging time later. Even simple things like using a custom __main__ entry point can make your utility scripts feel much more professional and reusable.

Conclusion

Every developer should have a "Classy" equivalent—a place where they aren't afraid to break things or write "ugly" code just to see how the engine works. It’s the fastest way to sharpen your technical edge.