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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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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

Differentiating and integrating symbolically using SymPy

At some point, you may have to differentiate a function that is not a simple polynomial, and you may need to do this in some kind of automated fashion—for example, if you are writing software for education. The Python scientific stack includes a package called SymPy, which allows us to create and manipulate symbolic mathematical expressions within Python. In particular, SymPy can perform differentiation and integration of symbolic functions, just like a mathematician.

In this recipe, we will create a symbolic function and then differentiate and integrate this function using the SymPy library.

Getting ready

Unlike some of the other scientific Python packages, there does not seem to be a standard alias under which SymPy is imported in the literature. Instead, the documentation uses a star import at several points, which is not in line with the PEP8 style guide. This is possibly to make the mathematical expressions...

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