NumPy Essentials

Leo (Liang-Huan) Chin, Tanmay Dutta

• Editorial: Packt Publishing
• Año: 2016
• ISBN:
  9781784393670
• Edición: 1
• Categoría: Informatik
• Categoría: Programmierung in Python

Boost your scientific and analytic capabilities in no time at all by discovering how to build real-world 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, 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.

Style and approach

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

NumPy Essentials

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NumPy Essentials

Copyright © 2016 Packt Publishing

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First published: April 2016

Production reference: 1220416

Published by Packt Publishing Ltd.

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ISBN 978-1-78439-367-0

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Credits

Authors Leo (Liang-Huan) Chin Tanmay Dutta	Copy Editor Sonia Cheema
Reviewers Miklós Prisznyák Pruthuvi Maheshakya Wijewardena	Project Coordinator Izzat Contractor
Commissioning Editor Kartikey Pandey	Proofreader Safis Editing
Acquisition Editor Larissa Pinto	Indexer Rekha Nair
Content Development Editor Rohit Singh	Graphics Kirk D'Penha Disha Haria Jason Monteiro
Technical Editor Murtaza Tinwala	Production Coordinator Melwyn Dsa

About the Authors

Leo (Liang-Huan) 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 spatial-temporal 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.

I would like to thank my wife and my brother for invaluable technical guidance and the rest of my family for supporting and encouraging me to write this book. I would like to express my gratitude to the editors, who provided me with support, encouragement, and valuable comments regarding the content and format and assisted me in the editing of this book. I would like to thank Packt Publishing for giving me the opportunity to coauthor this book.

About the Reviewers

Miklós Prisznyák is a senior software engineer with a scientific background. He graduated as a physicist and worked on his MSc thesis on Monte Carlo simulations of non-Abelian lattice quantum field theories in 1992. Having worked for 3 years at the Central Research Institute for Physics in Hungary, he joined MultiRáció Kft. in Budapest, a company founded by other physicists, which specialized in mathematical data analysis and the forecasting of economic data. It was here that he discovered the Python programming language in 2000. He set up his own consulting company in 2002 and worked on various projects for insurance, pharmacy, and e-commerce companies, using Python whenever he could. He also worked for a European Union research institute in Italy, testing, debugging, and developing a distributed, Python-based Zope/Plone web application. He moved to Great Britain in 2007, and at first, he worked for a Scottish start-up using Twisted Python. He then worked in the aerospace industry in England using, among others, the PyQt windowing toolkit, the Enthought application framework, and the NumPy and SciPy libraries. He returned to Hungary in 2012 and rejoined MultiRáció. Since then, he's mainly worked on a Python extension to OpenOffice/EuroOffice using NumPy and SciPy again, which allows users to solve nonlinear and stochastic optimization problems with the spreadsheet software Calc. He has also used Django, which is the most popular Python web framework currently. Miklós likes to travel and read books, and he is interested in the sciences, mathematics, linguistics, history, politics, go (the board game), and a few other topics. Besides this, he enjoys a good cup of coffee. However, he thinks nothing beats spending time with his brilliant, maths-savvy, Minecraft-programming, 13-year-old son, Zsombor, who also learned English on his own.

Pruthuvi Maheshakya Wijewardena holds a bachelor's degree in engineering from University of Moratuwa, Sri Lanka. He has contributed to the scikit-learn machine learning library as a Google Summer of Code participant and has experience working with the Python language, especially the NumPy, SciPy, pandas, and statsmodels libraries. While studying for his undergraduate degree, he was able to publish his thesis on machine learning. Currently, he works as a software engineer at WSO2, as a part of the data analytics team.

I would like to thank my mother, brothers, teachers, and friends.

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Preface

Whether you are new to scientific/analytic programming, or a seasoned expert, this book will provide you with the skills you need to successfully create, optimize, and distribute your Python/NumPy analytical modules.

Starting from the beginning, this book will cover the key features of NumPy arrays and the details of tuning the data format to make it most fit to your analytical needs. You will then get a walkthrough of the core and submodules that are common to various multidimensional, data-typed analysis. Next, you will move on to key technical implementations, such as linear algebra and Fourier analysis. Finally, you will learn about extending your NumPy capabilities for both functionality and performance by using Cython and the NumPy C API. The last chapter of this book also provides advanced materials to help you learn further by yourself.

This guide is an invaluable tutorial if you are planning to use NumPy in analytical p...