Probably not faster in an absolute sense but things like loops in Julia can be properly optimized and will sometimes be more readable than structuring your program entirely around NumPy constructs.
Not in real-world contexts. This is spelled out in Julia for Biologists (https://arxiv.org/abs/2109.09973) which does the operation counting to show why using Numba with SciPy is still an order of magnitude slower in scientific operations like solving differential equations compared to Julia. An order of magnitude on widely used scientific analyses is pretty significant!
