Book Image

SciPy Recipes

By : V Kishore Ayyadevara, Ruben Oliva Ramos
Book Image

SciPy Recipes

By: V Kishore Ayyadevara, Ruben Oliva Ramos

Overview of this book

With the SciPy Stack, you get the power to effectively process, manipulate, and visualize your data using the popular Python language. Utilizing SciPy correctly can sometimes be a very tricky proposition. This book provides the right techniques so you can use SciPy to perform different data science tasks with ease. This book includes hands-on recipes for using the different components of the SciPy Stack such as NumPy, SciPy, matplotlib, and pandas, among others. You will use these libraries to solve real-world problems in linear algebra, numerical analysis, data visualization, and much more. The recipes included in the book will ensure you get a practical understanding not only of how a particular feature in SciPy Stack works, but also of its application to real-world problems. The independent nature of the recipes also ensure that you can pick up any one and learn about a particular feature of SciPy without reading through the other recipes, thus making the book a very handy and useful guide.
Table of Contents (11 chapters)

Calculating the eigenvalue and eigenvector of a matrix

One of the major advantages of eigenvalue calculation is its ability to reduce the dimensions of a dataset, which in turn reduces the computations required to solve a given set of variables.

The eigenvector of a given vector is the vector that satisfies the following condition:

In the preceding equation, A is the matrix of our interest, v is the eigenvector, and λ is the eigenvalue of the given matrix.

How to do it…

In SciPy, we calculate the eigenvector and eigenvalue of a given matrix by using the eig function in scipy.linalg.

Using the following code, let us look at calculating the eigenvector and the corresponding eigenvalue of a given matrix:

  1. Initialize...