NumPy Essentials

by Liang-Hua Chin, Tanmay Datta

Overview

Boost your scientific and analytic capabilities in no time at all by discovering how to build realworld applications with NumPy

About This Book

• Optimize your Python scripts with powerful NumPy modules
• Explore the vast opportunities to build outstanding scientific/ analytical modules by yourself
• Packed with rich examples to help you master NumPy arrays and universal functions

Who This Book Is For

If you are an experienced Python developer who intends to drive your numerical and scientific applications with NumPy, this book is for you. Prior experience or knowledge of working with the Python language is required.

What You Will Learn

• Manipulate the key attributes and universal functions of NumPy
• Utilize matrix and mathematical computation using linear algebra modules
• Implement regression and curve fitting for models
• Perform time frequency / spectral density analysis using the Fourier Transform modules
• Collate with the distutils and setuptools modules used by other Python libraries
• Establish Cython with NumPy arrays
• Write extension modules for NumPy code using the C API
• Build sophisticated data structures using NumPy array with libraries such as Panda and Scikits

In Detail

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, stepbystep 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, highspeed programs using a wide range of mathematical features.

Style and approach

This quick guide will help you get to grips with the nittygritties of NumPy using with practical programming examples. Each topic is explained in both theoretical and practical ways with handson examples providing you efficient way of learning and adequate knowledge to support your professional work.

Product Details

ISBN-13:	9781784393670
Publisher:	Packt Publishing
Publication date:	04/29/2016
Pages:	156
Product dimensions:	7.50(w) x 9.25(h) x 0.33(d)

About the Author

Leo (LiangHuan) Chin is a data engineer with more than 5 years of experience in the field of Python. He works for Gogoro smart scooter, Taiwan, where his job entails discovering new and interesting biking patterns . His previous work experience includes ESRI, California, USA, which focused on spatialtemporal data mining. He loves data, analytics, and the stories behind data and analytics. He received an MA degree of GIS in geography from State University of New York, Buffalo. When Leo isn't glued to a computer screen, he spends time on photography, traveling, and exploring some awesome restaurants across the world. You can reach Leo at http://chinleock.github.io/portfolio/.

Tanmay Dutta is a seasoned programmer with expertise in programming languages such as Python, Erlang, C++, Haskell, and F#. He has extensive experience in developing numerical libraries and frameworks for investment banking businesses. He was also instrumental in the design and development of a risk framework in Python (pandas, NumPy, and Django) for a wealth fund in Singapore. Tanmay has a master's degree in financial engineering from Nanyang Technological University, Singapore, and a certification in computational finance from Tepper Business School, Carnegie Mellon University.