Applying Math with Python - Second Edition

Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific libraries

Key Features

• Compute complex mathematical problems using programming logic with the help of step-by-step recipes
• Learn how to use Python libraries for computation, mathematical modeling, and statistics
• Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics

Book Description

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.

What you will learn

• 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

Who this book is for

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.