This article outlines ten effective strategies to optimize Python code for better performance. It covers techniques like using sets for membership testing, avoiding unnecessary copies, and leveraging local functions to reduce execution time and memory usage. Each hack is supported by code examples and performance comparisons.

This article outlines principles and methods for optimizing code performance, primarily using C++ examples. It emphasizes the importance of considering efficiency during development to avoid performance issues later. The authors also provide practical advice for estimating performance impacts while writing code.