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

The Python and NumPy C-API


The Python implementation that we are using is a C-based implementation of the Python interpreter. NumPy is specifically for this C-based Python implementation. This implementation of Python comes with a C-API, which is the backbone of the interpreter and provides low-level control to its user. NumPy has further augmented this by providing a rich C-API.

Writing functions in C/C++ can provide developers with the flexibility to leverage some of the advanced libraries available in these languages. However, the cost is apparent in terms of having to write too much boilerplate code around parsing input in order to construct return values. Additionally, developers have to take care while referencing/dereferencing objects since this could eventually create nasty bugs and memory leaks. There is also the problem of future compatibility of the code as the C-API keeps on evolving; hence, if a developer wants to migrate to a later version of Python, they may be up for a lot...