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 8. Speeding Up NumPy with Cython

Python combined with the NumPy library provides the user with a tool to write highly complex functions and analysis. As the size and complexity of code grow, the number of inefficiencies in the code base starts to creep in. Once the project is in its completion stages, developers should start focusing on the performance of the code and analyze the bottlenecks. Python provides many tools and libraries to create optimized and faster-performing code.

In this chapter, we will be looking at one such tool called Cython. Cython is a static compiler for Python and the language "Cython," which is particularly popular among developers working on scientific libraries/numerical computing. Many famous analytics libraries written in Python make intensive use of Cython (pandas, SciPy, scikit-learn, and so on).

The Cython programming language is a superset of Python and the user still enjoys all the functionalities and higher level constructs provided by Python the...