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Overview of this book

Are you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python. The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions. By the end of this book, you’ll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.
Preface
1. Fundamentals of Python
Free Chapter
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

Matrix Operations on a Single Matrix

In this chapter, we will study the different ways of manipulating matrices and how to implement them in Python. Understanding how a matrix works broadly means understanding the fundamentals of how 2D or multidimensional arrays work. Once we have a good understanding of the basics of 2D matrices, those interested can delve into advanced studies of matrices, which includes special types of matrices such as sparse matrices, vector spaces, eigenvalues, and eigenvectors, which can involve more than two dimensions.

Matrices in Python can be implemented using either lists or arrays. Nested lists in Python work perfectly fine, but Python has a powerful package that makes matrix implementation much easier called NumPy. SciPy is another package that helps in matrix manipulation, but it is usually more suitable for larger matrix computations. We will be using both of these modules throughout this chapter.

It is assumed...