Book Image

NumPy Essentials

By : Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway
Book Image

NumPy Essentials

By: Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway

Overview of this book

In today’s world of science and technology, it’s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features.
Table of Contents (16 chapters)
NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Chapter 5. Linear Algebra in NumPy

NumPy is designed for numeric computations; underneath the hood it is still the powerful ndarray object, but at the same time NumPy provides different types of objects to solve mathematical problems. In this chapter, we will cover the matrix object and polynomial object to help you solve problems using a non-ndarray way. Again, NumPy provides a lot of standard mathematical algorithms and supports multi-dimensional data. While a matrix can't perform three-dimensional data, using the ndarray objects with the NumPy functions of linear algebra and polynomials is more preferable (the extensive SciPy library is another good choice for linear algebra, but NumPy is our focus in this book). Let's use NumPy to do some math now!

The topics that will be covered in this chapter are:

  • Matrix and vector operations
  • Decompositions
  • Mathematics of polynomials
  • Regression and curve fitting