As an experienced python/data science user, this (creating fast complex column-wise transforms) is rarely a problem for me.
The truly huge advantage for Julia is how it plays with parralelism. The GIL makes it an absolute pain to do parallelism in python. Always ends up in threading hacks with numba or joblib, or multiprocessing, which has its own unfixable flaws
Being able to iterate and mangle huge columns with real lambdas and without having to marshal arguments to/from C++ is a huge advantage.
Where I used to spend hours in aggregate searching through docs for pandas/numpy, for stupid shit like "how do I shift but also skip NaNs", now I just write a for-loop in a couple minutes and get on with my work.
There's a whole subclass of tasks in R/pandas to work around the interpreter that just aren't needed in Julia.
For me at least it's well worth the syntactical warts and slow interpreter.