My experience with Julia was good, and the language is convenient, however two major factors made me not use it after test projects:

1. Very scarce packages ecosystem. Like there's dataframes.jl file with poor mans implementation of Pandas.

2. Recompiling everything every time. It meant that a Julia program in some script would take ~40 seconds compiling with dataframes & some other important packages.

I think if a language is to replace Python in science, it would need to either be very fast (recompilation on every run breaks this, and running Julia in a notebook/shell is interesting, but outside of pure scientific code, it should be easier to re-run it), or it should offer ergonomics. Pandas has very rough corners, especially when you need grouping with nontrivial operations, or grouped window functions. Joins aren't easy either. Any system that makes this more ergonomic, could bite a bit off Python. But I don't see such.