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# Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems, Second Edition

By Sam Morley
\$39.99 \$27.98
Book Dec 2022 376 pages 2nd Edition
eBook
\$39.99 \$27.98
Print
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eBook
\$39.99 \$27.98
Print
\$49.99
Subscription
\$15.99 Monthly

## What do you get with eBook?

DRM FREE - Read whenever, wherever and however you want

# An Introduction to Basic Packages, Functions, and Concepts

Before getting started on any practical recipes, we’ll use this opening chapter to introduce several core mathematical concepts and structures and their Python representations. We’ll look at basic numerical types, basic mathematical functions (trigonometric functions, exponential function, and logarithms), and matrices. Matrices are fundamental in most computational applications because of the connection between matrices and solutions of systems of linear equations. We’ll explore some of these applications in this chapter, but matrices will play an important role throughout this book.

We’ll cover the following main topics in this order:

• Exploring Python numerical types
• Understanding basic mathematical functions
• Diving into the world of NumPy
• Working with matrices and linear algebra

NumPy arrays and the basic mathematical functions we will see in this chapter will be...

## Key benefits

• 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

## 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

## Product Details

Publication date : Dec 9, 2022
Length 376 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781804618370
Category :
Concepts :

## What do you get with eBook?

DRM FREE - Read whenever, wherever and however you want

## Product Details

Publication date : Dec 9, 2022
Length 376 pages
Edition : 2nd Edition
Language : English
ISBN-13 : 9781804618370
Category :
Concepts :

13 Chapters
Preface
1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts
2. Chapter 2: Mathematical Plotting with Matplotlib
3. Chapter 3: Calculus and Differential Equations
4. Chapter 4: Working with Randomness and Probability
5. Chapter 5: Working with Trees and Networks
6. Chapter 6: Working with Data and Statistics
7. Chapter 7: Using Regression and Forecasting
8. Chapter 8: Geometric Problems
9. Chapter 9: Finding Optimal Solutions
10. Chapter 10: Improving Your Productivity
11. Index
12. Other Books You May Enjoy

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