[cutler]

Can anything really be done by a single-threaded, GIL-laden, dynamic language trying to compete with a modern, natively parallel, JIT-ed language like Julia? That's a bit apples to oranges, no? Once other languages like Go, Swift, Kotlin, Rust and Julia catch-up with Python's range of data science libraries I'd say Python's days are numbered. Python is the Betmax of programming languages and FP is where the shallowness of its design really shows.

[helgie]

Natively parallel? From https://docs.julialang.org/en/v1/base/multi-threading/ : "Julia is not yet fully thread-safe. In particular segfaults seem to occur during I/O operations and task switching."

One solution for numerical code at least is to use Numba to JIT-compile (with LLVM, as Julia does) code that can be run without using the GIL, which can be run in parallel on top of Python's threading mechanisms (threading.Thread, concurrent.futures), or with Numba's other options (parallel-for, CUDA kernels, CPU or GPU vectorized Ufuncs/GUfuncs, stencil kernels).

[vasili111]

I agree with you that Python lacks many useful and advanced features. But that lack of advanced features give it simplicity and because of it much more programmers than for much advanced languages. Many programmers means more packages, more tutorials and bigger community. That is why hard to beat Python, because it's downside (simplicity) give it substantial advantage over more advanced languages.