This is the code repository for Applying Math with Python - Second Edition, published by Packt.
Over 70 practical recipes for solving real-world computational math problems
The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore 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.
This book covers the following exciting features:
- Become familiar with basic Python packages, tools, and libraries for solving mathematical problems
- Explore real-world applications of mathematics to reduce a problem in optimization
- Understand the core concepts of applied mathematics and their application in computer science
- Find out how to choose the most suitable package, tool, or technique to solve a problem
- Implement basic mathematical plotting, change plot styles, and add labels to plots using Matplotlib
- Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
from decimal import getcontext
ctx = getcontext()
num = Decimal('1.1')
num**4 # Decimal('1.4641')
ctx.prec=4 # set the new precision
num**4 # Decimal('1.464')
Following is what you need for this book: Whether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | Python 3.6 or higher | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Sam Morley is an experienced lecturer in mathematics and a researcher in pure mathematics. He is currently a research software engineer at the University of Oxford working on the DataSig project. He was previously a lecturer in mathematics at the University of East Anglia and Nottingham Trent University. His research interests lie in functional analysis, especially Banach algebras. Sam has a firm commitment to providing high-quality, inclusive, and enjoyable teaching, with the aim of inspiring his students and spreading his enthusiasm for mathematics.
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Simply click on the link to claim your free PDF.