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

NumPy in Academia and Industry


It is said that, if you stand at Times Square long enough, you will meet everyone in the world. By now, you must have been convinced that NumPy is the Times Square of SciPy. If you are writing scientific applications in Python, there is not much you can do without digging into NumPy. Figure 2 shows the scope of SciPy in scientific computing at varying levels of abstraction. The red arrow denotes the various low-level functions that are expected of scientific software, and the blue arrow denotes the different application domains that exploit these functions. Python, armed with the SciPy stack, is at the forefront of the languages that provide these capabilities.

A Google Scholar search for NumPy returns nearly 6,280 results. Some of these are papers and articles about NumPy and the SciPy stack itself, and many more are about NumPy's applications in a wide variety of research problems. Academics love Python, which is showcased by the increasing popularity of the SciPy stack as the primary language of scientific programming in countless universities and research labs all over the world. The experiences of many scientists and software professionals have been published on the Python website:

Figure 2: Python versus other languages