Python has firmly placed itself as the non-programmer’s programming language. Not even BASIC could come close to the sheer heights python has reached. I, like a lot of other people in physics, use it pretty frequently. Python has so many libraries—each performing even the most granular of functions—that programming a small applet or code in anything else is almost laughable.

There’s a problem though. Despite Python’s power, I don’t enjoy using it. If I could, I would do everything in Julia and leave Python behind—other people, however, aren’t as enthusiastic about Julia as I am.

Julia has become, in most respects, a data science language—people use it for machine learning and that kind of stuff—but Julia’s toolset—including linear algebra with OpenBLAS, GPU and parallelism out of the box, simple environment management, and easier optimization—make that fate all the more tragic to me. Compared to Python, Julia has everything out of the box, without needing to mess around with external libraries like NumPy, environment managers like Conda, and external compilers like Numba or Cython. Yet, still, Python somehow reigns supreme.

Python is a slow, clunky mess, and I hate using it, yet somehow, the world keeps using it. God help me. I just want most of my calculations to run at a decent speed.