6 techniques for reducing the computational cost of Python algorithms
A junior software developer shall be forgiven for being happy when their code works. If that’s you, I do not judge you.
However, if you are ready to get to the next level of building software with Python, your code should not just run and pass some tests. It should also be written with the available computing resources — and the energy bill — in mind.
Every inefficient loop, poorly chosen data structure, or redundant computation burns more electricity than necessary. Unlike C, for example, where you must reserve bits from your disk for each new variable you create, Python will consume resources as it sees fit. This makes it extremely beginner-friendly, but also rather energy-intensive when used wrong.
Sloppy algorithms are not just bad for the performance of a code. They are bad for the planet, too. Software companies like Microsoft are struggling to keep their carbon emissions low because of all of the energy they consume for AI and other tasks. At the same time, sustainability is a growing concern. Sustainability-minded programmers are therefore becoming a valuable resource for many companies.
