Accelerating pure Python code with Numba and Just-In-Time compilation

Numba (http://numba.pydata.org) is a package created by Anaconda, Inc (http://www.anaconda.com). Numba takes pure Python code and translates it automatically (JIT) into optimized machine code. In practice, this means that we can write a non-vectorized function in pure Python, using for loops, and have this function vectorized automatically by using a single decorator. Performance speedups when compared to pure Python code can reach several orders of magnitude and may even outmatch manually-vectorized NumPy code.

In this section, we will show you how to accelerate pure Python code generating a Mandelbrot fractal.

Getting ready

Numba should already be installed in Anaconda, but you can also install it manually with conda install numba.

How to do it...

1. Let's import NumPy and define a few variables:

   >>> import numpy as np
       import matplotlib.pyplot as plt
       %matplotlib inline
   >>> size = 400
       iterations...