It's not to knock it, but the real strength of Python is as a language that the non-professional programmer can pick up right away and use to put their non-programming skills on wheels.
Jupyter, numpy, pandas, scikit-learn and such are the center of the ecosystem for many people. You can point your finger at these people and tell them to try Julia because it is better on paper... And they will, and they won't like it and they'll go back to Python.
Exactly this. At this point I'm not sure where I'd even go for the tasks I use this suite of tools for. I'd check out Julia but I haven't run into many problems that would inspire me to switch.
(this was a genuine question as someone who primarily writes code in Python for fun, not professionally, and is looking to level up for fun)
Video games are a great example. You'd like a level designer to be able to edit scripts that control arbitrary things about the game play, but you also need a performance and GFX pro to program other parts.
While its ecosystem is surely much smaller than even Julia's, Nim [1] has a lot of the concise, lexical feel of Python, but semantics more like Modula3/Ada and with Lisp-like syntax macros/metaprogramming to fill in other gaps.
Another possibility is Cython [2] which is basically a superset of Python with gradual typing..more targeted at writing new C-like Python modules/leveraging the Python runtime environment than "standing on its own".
CPython (and most other interpreters) has the big problem of the global interpreter lock which prevents it from taking advantage of threads... And that's a problem in a world where (1) 8-core laptops are common, (2) much bigger machines can be found in the server room and (3) many workloads can be parallelized over threads with just a little work.