Book Image

Applying Math with Python

By : Sam Morley
Book Image

Applying Math with Python

By: Sam Morley

Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (12 chapters)

Accelerating code with Cython

Python is often criticized for being a slow programming language – a statement that is endlessly debatable. Many of these criticisms can be addressed by using a high-performance compiled library with a Python interface – such as the scientific Python stack – to greatly improve performance. However, there are some situations where it is difficult to avoid the fact that Python is not a compiled language. One way to improve performance in these (fairly rare) situations is to write a C extension (or even rewrite the code entirely in C) to speed up the critical parts. This will certainly make the code run faster, but it might make it more difficult to maintain the package. Instead, we can use Cython, which is an extension of the Python language that is transpiled into C and compiled for great performance improvements.

For example, we can consider some code that's used to generate an image of the Mandelbrot set. For comparison...