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

Vectorized operations


All NumPy operations are vectorized, where you apply operations to the whole array instead of on each element individually. This is not just neat and handy but also improves the performance of computation compared to using loops. In this section, we will experience the power of NumPy vectorized operations. A key idea worth keeping in mind before we start exploring this subject is to always think of entire sets of arrays instead of each element; this will help you enjoy learning about NumPy Arrays and their performance. Let's start by doing some simple calculations with scalars and between NumPy Arrays:

In [1]: import numpy as np 
In [2]: x = np.array([1, 2, 3, 4]) 
In [3]: x + 1 
Out[3]: array([2, 3, 4, 5]) 

All the elements in the array are added by 1 simultaneously. This is very different from Python or most other programming languages. The elements in a NumPy Array all have the same dtype; in the preceding example, this is numpy.int (this is either...