It is nevertheless im
portant because a simple plot is a nice test if you are considering using a language for scientific computing. Criticism about plotting libraries quality is even more valid. I cannot believe that plotting an scatter plot of a few million points is so slow while AAA videogames render millions of pixels in real time.
On the long term? I think Julia is a better language than Python, Matlab or R for several reasons like modularity, package management and performance. But these are things that require at least 10 hours of use (to say something) instead of 10 minutes. With so many languages promising enlightent and transcendence to a next power level you cannot expect people to make that kind of investment.
Makie.jl [1] does it's plotting on the GPU (like a video game engine), so can handle millions of datapoints just fine.
Also, note that for people to whom plotting is really important, it's quite easy nowadays to just AOT compile your plotting library to your sysimage with PackageCompiler.jl [2] for instant plots.
Julia's model is based around multimethods, which enables pretty crazy levels of interoperability (imagine being able to use scipy and tensorflow together, with tensorflow's autograd just magically working on most of scipy's functions). More about it here https://m.youtube.com/watch?v=kc9HwsxE1OY
I don't use poetry but I will risk a comment. You will have to judge:
At the language level Julia separates files from modules. Modules are just a language construct, another object. They can span several files, have several them in the same file or even declare them in the REPL. This last point is important for me since I can (re)evaluate code in the REPL without polluting it. Usually Python feels more interactive but this is a point where Julia wins at the REPL.
Julia's package manager is just another library and you have support integrated for it inside the REPL.
I'm not devops, I don't think I can judge the relative merits of Julia's package manager design relative to Python's but for me "it feels" better. You will have to judge for yourself reading the docs and this things about federated package management.
I have not tried it but Julia allows to build a sysimage with all dependencies included. I know there are similar things to build standalone Python apps but last time I tried (long ago) it was a pain.
Finally, Python depends a lot on external C libraries to achieve performance. This obviously complicates deployment and is a reason why I use Ubuntu: I have binary wheels for almost everything. Julia provides performance without external tools and packages are usually pure Julia code.
I think Julia's is vastly superior concerning packaging but of course for 99.9% of people this is not enough to make a switch yet (me included, for the moment I use it for hobby projects).
Regarding modularity is amazing and not obvious at first why Julia's performance increases modularity. The reason is that in Python since you need to use C/C++ for performance your data structures need to be shaped appropriately when they cross this interface. This rigidity propagates through your program and makes you build big frameworks. I have in mind for example PyTorch or Tensorflow. So you have Numpy arrays, PyTorch tensor... you have of course almost transparent conversion between them since numpy is a standard. But all of this is achieved because a behemoth like Facebook or Google are behind injecting money and manpower. It's pyramid building: some engineering but a lot of work. Even then you are stuck with arrays and contorting your program to vectorized operations.
There are of course some dark spots for Julia but I have the feeling they will be solved. I don't consider myself a fan boy or an early adopter but I think it has a future. So I thought for Python 20 years ago :)
On the long term? I think Julia is a better language than Python, Matlab or R for several reasons like modularity, package management and performance. But these are things that require at least 10 hours of use (to say something) instead of 10 minutes. With so many languages promising enlightent and transcendence to a next power level you cannot expect people to make that kind of investment.