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The Statistics and Calculus with Python Workshop

You're reading from  The Statistics and Calculus with Python Workshop

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209763
Pages 740 pages
Edition 1st Edition
Languages
Concepts
Authors (6):
Peter Farrell Peter Farrell
Profile icon Peter Farrell
Alvaro Fuentes Alvaro Fuentes
Profile icon Alvaro Fuentes
Ajinkya Sudhir Kolhe Ajinkya Sudhir Kolhe
Profile icon Ajinkya Sudhir Kolhe
Quan Nguyen Quan Nguyen
Profile icon Quan Nguyen
Alexander Joseph Sarver Alexander Joseph Sarver
Profile icon Alexander Joseph Sarver
Marios Tsatsos Marios Tsatsos
Profile icon Marios Tsatsos
View More author details

Table of Contents (14) Chapters

Preface
1. Fundamentals of Python 2. Python's Main Tools for Statistics 3. Python's Statistical Toolbox 4. Functions and Algebra with Python 5. More Mathematics with Python 6. Matrices and Markov Chains with Python 7. Doing Basic Statistics with Python 8. Foundational Probability Concepts and Their Applications 9. Intermediate Statistics with Python 10. Foundational Calculus with Python 11. More Calculus with Python 12. Intermediate Calculus with Python Appendix

Operations on Multiple Matrices

We will now be performing operations between two or more matrices and see the functions that will help us to achieve that. We will be covering how to write an inverse of a matrix, logical operators, dot products, eigenvalues and eigenvectors, outer products, and the determinates of a matrix.

It should be noted that there are plenty of other things you can do with matrices, and the official NumPy documentation is a really good resource for referencing information according to the requirements of the user. Most of the topics that we are going to cover are part of the linear algebra package of the NumPy library. There are far wider applications in physics and mathematics that are beyond the scope of this chapter for each of the topics we are going to cover, but it should suffice to know that they all play a very important role in understanding matrices.

Note

For more information about the NumPy library, refer to https://docs.scipy.org/doc/numpy...

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