The ELI5 is that numba allows you designate specific functions in your application for compilation. That tells the library to run a just in time compiler on it. If you can do that successfully, you can wind up with that portion of your code base running at or near C-speed. There are a lot of gotchas and limitations, and it only works for a subset of the python language. But when it works, it can make loops over numpy arrays very fast, when they would normally be extremely slow in pure python. In a lot of cases, just a few compute bound processes are holding back the whole application. Numba allows you to improve those portions in a very targeted way, if you have a suitable use case.
The ELI5 is that numba allows you designate specific functions in your application for compilation. That tells the library to run a just in time compiler on it. If you can do that successfully, you can wind up with that portion of your code base running at or near C-speed. There are a lot of gotchas and limitations, and it only works for a subset of the python language. But when it works, it can make loops over numpy arrays very fast, when they would normally be extremely slow in pure python. In a lot of cases, just a few compute bound processes are holding back the whole application. Numba allows you to improve those portions in a very targeted way, if you have a suitable use case.