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 2. The NumPy ndarray Object

Array-oriented computing is the very heart of computational sciences. It is something that most Python programmers are not accustomed to. Though list or dictionary comprehension is relative to an array and sometimes used similarly to an array, there is a huge difference between a list/dictionary and an array in terms of performance and manipulation. This chapter introduces a basic array object in NumPy. It covers the information that can be gleaned from the intrinsic characteristics of NumPy arrays without performing any external operations on the array.

The topics that will be covered in the chapter are as follows:

  • numpy.ndarray and how to use it-basic array-oriented computing
  • Performance of numpy.ndarray-memory access, storage, and retrieval
  • Indexing, slicing, views, and copies
  • Array data types