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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

Product type Paperback

Published in Dec 2022

Publisher Packt

ISBN-13 9781804618370

Length 376 pages

Edition 2nd Edition

Languages

Python

Tools

Matplotlib

Concepts

Statistics

Author (1):

Sam Morley

View More author details

Table of Contents (13) Chapters

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

Why subscribe?

12. Other Books You May Enjoy

Further reading

There are a large number of textbooks on statistics and statistical theory. The following books are good references for the statistics covered in this chapter:

Mendenhall, W., Beaver, R., and Beaver, B. (2006), Introduction To Probability And Statistics. 12th ed., (Belmont, Calif.: Thomson Brooks/Cole).
Freedman, D., Pisani, R., and Purves, R. (2007), Statistics. New York: W.W. Norton.

The pandas documentation (https://pandas.pydata.org/docs/index.html) and the following pandas book serve as good references for working with pandas:

McKinney, W., (2017), Python for Data Analysis. 2nd ed., (Sebastopol: O’Reilly Media, Inc, US).

The SciPy documentation (https://docs.scipy.org/doc/scipy/tutorial/stats.html) also contains detailed information about the statistics module that was used several times in this chapter.

The rest of the chapter is locked

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Authors (1)

Morley

Morley

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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