Julia is aimed at scientific computing. It competes against Python with numpy/scipy, R, etc.

Correct, but I would add: Julia is better than Python+NumPy/SciPy when you need extreme speed in custom logic that can’t be easily vectorized. As Julia is JIT-compiled, if your code calls most of the functions just once it won’t provide a big advantage, as the time spent compiling functions can be significant (e.g., if you use some library heavily based on macros).

To produce plots out of data files, Python and R are probably the best solutions.

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dgfl 2 hours ago | parent | next [-]

Disagree on the last statement. Makie is tremendously superior to matplotlib. I love ggplot but it is slow, as all of R is. And my work isn’t so heavy on statistics anyway.

Makie has the best API I’ve seen (mostly matlab / matplotlib inspired), the easiest layout engine, the best system for live interactive plots (Observables are amazing), and the best performance for large data and exploration. It’s just a phenomenal visualization library for anything I do. I suggest everyone to give it a try.

Matlab is the only one that comes close, but it has its own pros and cons. I could write about the topic in detail, as I’ve spent a lot of time trying almost everything that exists across the major languages.

	▲	Certhas an hour ago \| parent \| next [-]
		I love Makie but for investigating our datasets Python is overall superior (I am not familiar enough with R), despite Julia having the superior Array Syntax and Makie having the better API. This is simply because of the brilliant library support available in scikit learn and the whole compilation overhead/TTFX issue. For these workflows it's a huge issue that restarting your interactive session takes minutes instead of seconds.
	▲	dan-robertson 2 hours ago \| parent \| prev [-]
		I tried some Julia plotting libraries a few years ago and they had apis that were bad for interactively creating plots as well as often being buggy. I don’t have performance problems with ggplot so that’s what I tend to lean to. Matplotlib being bad isn’t much of a problem anymore as LLMs can translate from ggplot to matplotlib for you.

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ainch an hour ago | parent | prev | next [-]

Even then, if you're familiar with NumPy it's pretty easy to switch to Jax's NumPy API, and then you can easily jit in Python as well.

	▲	JanisErdmanis 22 minutes ago \| parent [-]
		As long as someone else does the porting and maintains the compatability between both subecosystems of thoose who prefer using Jax and thoose who prefer depending on the NumPy. Also not having zero overhead structs that one can in an array handicaps types of performance codes one can write.

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jey 2 hours ago | parent | prev [-]

And I would further add: In addition to performance, Julia's language and semantics are much more ergonomic and natural for mathematical and algorithmic code. Even linear algebra in Python is syntactically painful. (Yes, they added the "@" operator for matmul, but this is still true).