You can find a lot of discussion that is relevant in this thread: “Julia motivation: why weren’t Numpy, Scipy, Numba, good enough?”
Many points are brought up. For me, the focus on first class numerical programming support is among the most important. Numpy’s syntax is pretty awkward compared to Julia.
I have really come to love multiple dispatch, which relieves me of almost all the tedious input parsing that you encounter in Python (and even much more so in Matlab!) I would estimate that about 25% of all coding time I’ve spent in Matlab was on input parsing, or figuring out how to efficiently branch my code based on the type of a variable.
Lightweight or zero-cost abstractions are great! You can choose an appropriate level of abstraction without sacrificing speed, and get very elegant and readable code.
EDIT: Oh, and of the remaining 75% of programming time, I’d say most of it was spent figuring out insane code vectorization tricks, to help performance. This is pretty much the same in Numpy.