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2019 | OriginalPaper | Chapter
19. Code Optimization
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Abstract
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 that contain or use externally compiled code, typically written in C or in 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 therefore vastly better than if the same computation were to be implemented purely in Python.