Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib

© Robert Johansson 2019

Robert JohanssonNumerical Python https://doi.org/10.1007/978-1-4842-4246-9_19

19. Code Optimization

Robert Johansson¹ 

(1)

Urayasu-shi, Chiba, Japan

 

In this book we have explored various topics of scientific and technical computing 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 that is suitable for exploratory computing and rapid prototyping – which minimizes development efforts – and high-performance numerics, which minimize application runtimes. High-performance numerics is achieved not through the Python language itself, but rather through leveraging libraries ...