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 QR decomposition of a matrix

Similar to the LU decomposition, QR decomposition is the decomposition of an original matrix into its constituent parts.

In this particular case, the matrix A = QR, where Q is an orthogonal matrix and R is an upper triangular matrix.

Before getting into further details, let us look into the properties of an orthogonal matrix:

  • It is a square matrix
  • Multiplying Q with its transpose results in an identity matrix

How to do it…

QR decomposition can be done by using the qr function within scipy.linalg.

In the following code, let us look into how QR decomposition works:

  1. Load the relevant packages:
import scipy.linalg as linalg
import numpy as np
  1. Initialize a matrix:
A = np...