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 will need the numpy package and the matplotlib package, as usual. We will also need the Shapely package and the scikit-image package, which can be installed using your favorite package manager, such as pip:

          python3.8 -m pip install numpy matplotlib shapely scikit-image
        

The code for this chapter can be found in the Chapter 08 folder of the GitHub repository at https://github.com/PacktPublishing/Applying-Math-with-Python/tree/master/Chapter%2008.

Check out the following video to see the Code in Action: https://bit.ly/3hpeKEF.