I personally use Mathematica for this purpose and find it indispensable. I wonder if the PL designers should focus more on this class of languages, instead of features for production languages (like fancy type systems).
In Julia, explorative programming is productionized programming, you don't have to switch to a different or restricted set of tools to make your program fast, it's the same code that you can profile on the fly. With types, you can patch your hotspots by adding a specific method to the generic function, and it will get invoked automatically instead of the more general case algorithm.
In those languages, I can actually take the theoretical practical hardware limits (memory BW, throughput, latency), and relatively easily achieve 99% of their utilization.
When people say “very fast” and “my cores are at 100%” they often mean “my program achieves 1% of the perf the hardware can deliver”.
So? That's the case for every JIT or interpreted language...
Dragging something in from scikit-learn was literally like...two lines of code. It's not the same...investment as most FFI systems.