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)

Technical requirements

For this chapter, we require the standard scientific Python packages, NumPy, Matplotlib, and SciPy. We will also require the PyMC3 package for the final recipe. You can install this using your favorite package manager, such as pip:

          python3.8 -m pip install pymc3

This command will install the most recent version of PyMC3, which, at the time of writing, was 3.9.2. This package provides facilities for probabilistic programming, which involves performing many calculations driven by randomly generated data to understand the likely distribution of a solution to a problem.

The code for this chapter can be found in the Chapter 04 folder of the GitHub repository at

Check out the following video to see the Code in Action: