In this chapter, we covered some of the main high-performance computing methods in Python. Numba is one of the easiest and most efficient options. Cython is useful with more complex use-cases and when it is necessary to leverage C/C++ code. Also, IPython.parallel allows us to leverage multicore CPUs or multiple computers for independent tasks. Finally, we discussed further high-performance computing techniques.
In the next chapter, we will explore a few customization options in IPython and the Notebook.