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Applying Math with Python

You're reading from   Applying Math with Python Over 70 practical recipes for solving real-world computational math problems

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618370
Length 376 pages
Edition 2nd Edition
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Concepts
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Author (1):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts 2. Chapter 2: Mathematical Plotting with Matplotlib FREE CHAPTER 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

Accelerating code with Cython

Python is often criticized for being a slow programming language – an endlessly debatable statement. Many of these criticisms can be addressed by using a high-performance compiled library with a Python interface – such as the scientific Python stack – to greatly improve performance. However, there are some situations where it is difficult to avoid the fact that Python is not a compiled language. One way to improve performance in these (fairly rare) situations is to write a C extension (or even rewrite the code entirely in C) to speed up the critical parts. This will certainly make the code run more quickly, but it might make it more difficult to maintain the package. Instead, we can use Cython, which is an extension of the Python language that is transpiled into C and compiled for great performance improvements.

For example, we can consider some code that’s used to generate an image of the Mandelbrot set. For comparison,...

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