There are many good reasons to use Julia for scientific programming, not the least of which is that it is often much faster than Python.
There are some high profile examples, such as Oceananigans.jl which is
… a fast, friendly, flexible software package for finite volume simulations of the nonhydrostatic and hydrostatic Boussinesq equations on CPUs and GPUs. It runs on GPUs (wow, fast!), though we believe Oceananigans makes the biggest waves with its ultra-flexible user interface that makes simple simulations easy, and complex, creative simulations possible.
Before throwing away your Python code read Why I no longer recommend Julia. It is a well informed and quite concerning critique of some of the fundamental design principles and approaches taken by the language and those who develop it.
There is an interesting discussion on the Julia forum, which is notable for it’s lack of defensiveness, and openness to the critique:
but there don’t seem to me to be any substantive links to plans to remedy the fundamental issues that are brought up, the most concerning to me is that the language design allows for extremely hard to track down errors, that in many cases may be totally silent, such that code may produce erroneous answers and the user will not know.