Don’t get me wrong. Python’s popularity is still backed by a rock-solid community of computer scientists, data scientists and AI specialists.
But if you’ve ever been at a dinner table with these people, you also know how much they rant about the weaknesses of Python. From being slow to requiring excessive testing, to producing runtime errors despite prior testing — there’s enough to be pissed off about.
Which is why more and more programmers are adopting other languages — the top players being Julia, Go, and Rust. …
Python is my bread-and-butter, and I love it. Even though I’ve got some points of criticism against the language, I strongly recommend it for anybody starting out in data science. More experienced people in the field tend to be Python-evangelists anyway.
However, this doesn’t mean that you can’t challenge the limits in your field from time to time, for example by exploring a different programming paradigm or a new language.
Science is messy. That’s why there’s some truth to the cliché of the scatterbrained professor. Not all scientists are like that, of course, but new theories and ideas are often birthed from downright chaos.
At the same time, many scientists are very clean and diligent when it comes to everyday tasks like keeping their desks tidy or their email inboxes uncluttered. This may sound paradoxical, but it really isn’t. All that mental mess needs to be managed somehow, and orderliness is a good strategy for keeping it contained within clear boundaries.
They set those boundaries by insisting on tidiness for anything that’s related to, but not directly part of, the mess that is science. That includes emails or the classes they teach, but also the experimental apparatuses they employ and any other tools they’re using. …