Furthermore Python is written in C, has always relied principally on C extensions. It doesn’t make sense to set beating them as a goal. And some Python libraries have armies as big as the Julia dev community working on them. Still these problems around interoperability (but not only interop) are being worked on. These are hard to solve and resource intensive and have had to compete with all the other really important stuff just to grab a couple of things at random: BinaryBuilder and Pkg. People often choose other languages over Julia, eg Rust, not for lack of advertising or even libraries, but for fundamental reasons, such as interoperability and ease of deployment. Asking for more or better isn’t a good approach IMO. The material is mostly of very high quality. There has already been an enormous amount of effort expended showcasing and promoting Julia on the forums you mention and others. It seems that the OP’s original interest here wasn’t “science” but data science. For example, a lot of people are doing high dimensional data visualizations (umap, tsne, etc.): how could Julia make inroads there? Or rather, should Julia make inroads there? To have an actionable discussion, the question needs to be much more direct. There are other areas where Julia has not made inroads, like in data science it still seems to be R > Python > everything else, and then machine learning, etc. In pharmacometrics for example, R, MATLAB, and Julia are “the” languages, also discussed along with Perl and Fortran, with Python not even a player in this game. Julia has been making some major inroads in some areas. Nobody works on “science”, people work in subsets like materials physics, ecology, etc. “Julia isn’t as popular/mainstream/well-supported as language X” is a valid criticism, but “be more popular” or “develop more/better packages” aren’t very actionable, and don’t lead to productive discussions.Īnd even one step further, “science” is too big to be a meaningful entity to discuss too. Lastly, the quantity and magnitude of prompt responses to the suggestion of a nobody newcomer to the language, like myself, is impressive to say the least. Sincerely, I can’t even begin to imagine the amount of thought, time and energy that’s been generously poured into making Julia the already amazing accomplishment that it is. For the science programming problems Julia can’t solve yet, that’s where the Julia language and it’s libraries should be further developed and informing the science programming community is coming soon.Įdit: I apologize to those my suggestion doesn’t set well with. Search the web to see what problems scientists are resorting to learning harder languages like Go and Rust to solve, for example.įor the problems Julia can solve already, start an online campaign via blogs, Reddit, YouTube, Twitter, StackOverFlow, GitHub, etc… to inform scientist how much easier it would be to implement Julia. The Julia team could grow the user base by filling the programming language gaps that data scientists are resorting to more difficult languages for, such as Go and Rust.
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