Code Optimization

This book explored various scientific and technical computing topics using Python and its ecosystem of libraries. As touched upon in the very first chapter of this book, the Python environment for scientific computing generally strikes a good balance between a high-level environment suitable for exploratory computing and rapid prototyping that minimizes development efforts and high-performance computing that minimizes application runtimes. High-performance numerical computation is achieved not using the Python language itself, but rather through leveraging external compiled libraries, often written in C or Fortran. Because of this, in computing applications that rely heavily on libraries such as NumPy and SciPy, most of the number crunching is performed by compiled code, and the performance is vastly better than if the computation were to be implemented purely in Python.