My existing Python script ingests numerical data from several APIs, applies a series of transformations, and then writes the results to disk. Functionally everything works, but execution time has grown to the point that it is now the bottleneck in my workflow. I need the code refactored and tuned for speed and lower memory consumption while keeping exactly the same inputs, outputs, and logic flow so it drops straight back into production. You are free to profile, vectorise, parallel-process, cache, or even swap in faster libraries—as long as the final solution runs on standard CPython 3.x and all third-party packages are installable with pip. Deliverables • Optimised .py file(s) with clear, in-code comments explaining the changes • A short performance report showing before-and-after runtimes on my sample dataset • A requirements.txt (or updated one) if new libraries were introduced I will supply the current script, sample API responses, and a small benchmark driver so you can reproduce the slowdown locally. If your first pass already shows a consistent speed-up of at least 3×, we can discuss further micro-optimisations or adding unit tests before final hand-off.