Python High Performance - Second Edition

Numerical code

If your goal is to write numerical code, an excellent strategy is to start directly with a NumPy implementation. Using NumPy is a safe bet because it is available and tested on many platforms and, as we have seen in the earlier chapters, many other packages treat NumPy arrays as first-class citizens.

When properly written (such as by taking advantage of broadcasting and other techniques we learned in Chapter 2, Pure Python Optimizations), NumPy performance is already quite close to the native performance achievable by C code, and won't require further optimization. That said, certain algorithms are hard to express efficiently using NumPy's data structures and methods. When this happens, two very good options can be, for example, ...