This conversation may be a little dead, but I was also stumped by the “why not numba?” question when I gave a talk on Julia recently. So, I took the benchmarks from test/perf/micro and for all of the python benchmarks I added @jit decorations to the functions. This worked fine except for parse_int which gave me some error that I didn’t understand. After running the benchmarks, this is what I get:
I am on a 2013 macbook pro with an intel i7 quad core (2.6GHz) and 16Gb of ram. The Julia benchmarks were on 0.6.0-rc2.0. As for Python/Numba, we have Python 3.5.2 and numba 0.33.0.
This shows you that Python is definitely outperformed by Julia, but when the Numba compiler works, you can write Python code that is within a factor of three of Julia performance.
There have been many interesting points made above about Julia vs Python/Numba as languages, but in terms of performance it seems that Numba can be quite competitive in tasks that are important in the field of numerical computing.