Optimize Python performance only after confirming need through software profiling. Profiling identifies the slowest and most memory-intensive code paths and guides focused optimization efforts. Assess return on investment: skip optimization when programs run infrequently or when optimization costs exceed execution costs. Prioritize correctness and maintainability first by testing and refactoring; these steps often yield speed and memory improvements as a byproduct. Use a checklist before optimizing: ensure thorough testing, perform refactoring for readability and Pythonic style, and run profiling to locate inefficiencies. Optimize targeted hotspots rather than guessing broadly to achieve efficient use of effort.
Do you want to optimize the performance of your Python program to make it run faster or consume less memory? Before diving into any performance tuning, you should strongly consider using a technique called software profiling. It can help you decide whether optimizing the code is necessary and, if so, which parts of the code you should focus on. Sometimes, the return on investment in performance optimizations just isn't worth the effort.
When it comes to improving the quality of your code, you'll probably optimize for performance as a final step, if you do it at all. Often, your code will become speedier and more memory efficient thanks to other changes that you make. When in doubt, go through this short checklist to figure out whether to work on performance: Testing: Have you tested your code to prove that it works as expected and without errors?
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