Anti-patterns are common but ineffective solutions to recurring problems. Python’s flexibility and conciseness are double-edged swords: they let us solve problems quickly, but also make it easy to adopt practices that quietly harm performance, readability, and reliability.

The Zen of Python reminds us that “there should be one—and preferably only one—obvious way to do it.” In reality, Python often offers many ways to solve the same problem, and not all of them are equally good. This talk explores what happens when the non-obvious choices make their way into production code.

We’ll walk through real-world examples of common Python anti-patterns and worst practices, covering syntax misuse, iterators, web applications, and data science workflows. Rather than focusing on style nitpicks, this session explains why these patterns are problematic and demonstrates how to refactor them into clearer, safer, and more maintainable solutions.