[superdimwit]

I think if you're just plugging together reasonably "vanilla" components from python / R libraries, and only using vectorised operations, those languages are fine and you can get away with using vectorised libraries wrapping C++.

The moment Julia shines is when your workloads can't be phrased by stringing together the limited set of vectorised verbs that python / r libraries give you: this is anything stateful and loopy like reinforcement learning, systematic trading, monte carlo simulations etc. It's also useful if you really care about performance and are doing "vanilla" computations at a truly large scale. If you want to avoid copying memory (i.e. doing vectorised operations), or want to tightly optimise / fused some numerical operations, it's great.

The other issue with python / r wrapping c++ libraries is that different libraries will generally not play well together (without coming out into python / r space, and doing a lot of copying / allocation). This tends to encourage large monolithic c/++ codebases like numpy and pandas, that are pretty impenetrable and difficult to extend / modify.

[improbable22]

One more advantage to these libraries being written in Julia is that, if they are almost do what you need but not quite, it's often pretty easy to reach inside and patch the function which needs changing. You already speak the language and don't need to stop the world to do this. The barrier to doing this to (say) numpy is just much higher.