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Human-Cabbage | 3 days ago

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

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ziotom78|3 days ago

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.

dgfl|3 days ago

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.

jey|3 days ago

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).

ainch|3 days ago

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.