Strategy Pattern in Python: Write Flexible, Clean Code How to pick different algorithms at runtime—without a tangled web of if/else Hey there! Today I want to write about a design pattern: the Strategy Pattern . Perfect for when you have multiple ways to solve the same problem and you want clean, maintainable code. Why the Strategy Pattern? Let’s start with a scenario: imagine you're building a pizza delivery app, and different restaurants have wildly different APIs. Without planning, you might end up stuffing your code full of branching logic. Instead, the Strategy Pattern lets you isolate each integration into its own “strategy,” making your code neat and flexible. In software design terms, a strategy defines a family of algorithms, each encapsulated in its own class (or callable), and they’re all interchangeable at runtime. This helps avoid massive if/else blocks and makes adding new behavior super easy without touching existing code—hell...
Avoiding Pandas Pitfalls: View vs Copy and Keeping Your Base DataFrame Safe Ever accidentally changed your original pandas DataFrame without meaning to? I certainly have. It can feel like a debugging nightmare. In this post, I want to walk you through some of the lessons I’ve learned, like how pandas handles views and copies, and how to organize your code so you don't trip over unexpected behavior. Understanding View vs Copy in pandas In pandas, a “view” is just another way of looking at the same data in memory. That means if you change it, the original changes too. A “copy”, on the other hand, is a separate object entirely. What you do with it stays isolated. The problem is that pandas doesn’t always make it obvious whether you're dealing with a view or a copy. Sometimes a slice of a DataFrame gives you a view. Other times it gives you a copy. That inconsistency is the reason behind the infamous SettingWithCopyWarning [1] . For example, if...