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

Views and copies


There are primarily two ways of accessing data by slicing and indexing. They are called copies and views: you can either access elements directly from an array, or create a copy of the array that contains only the accessed elements. Since a view is a reference of the original array (in Python, all variables are references), modifying a view modifies the original array too. This is not true for copies.

The may_share_memory function in NumPy miscellaneous routines can be used to determine whether two arrays are copies or views of each other. While this method does the job in most cases, it is not always reliable, since it uses heuristics. It may return incorrect results too. For introductory purposes, however, we shall take it for granted.

Generally, slicing an array creates a view and indexing it creates a copy. Let us study these differences through a few code snippets. First, let's create a random 100x10 array.

In [21]: x = np.random.rand(100,10) 

Now, let us extract...