✅ Final Checklist
In summary, optimisation in Python often revolves around improving the time complexity of your algorithms, logic, and code; reducing or smoothing memory usage; leveraging Python's built-in optimisation features; or bringing in external libraries that are implemented in C for speed.
In the next section, we'll cover a brief checklist of “easy wins”. On the flip-side, like any other language, Python also has its own set of performance pitfalls, often referred to as anti-patterns. These anti-patterns can degrade the performance of a Python application if not addressed. So we'll also review common anti-patterns in Python, and provide suggestions on how to avoid them!