Learning NumPy Array

Ivan Idris

• Éditeur: Packt Publishing
• Année: 2014
• ISBN:
  9781783983902
• Édition: 1
• Sujet: Computer Science
• Sous-sujet: Databases

In Detail

NumPy is an extension of Python, which provides highly optimized arrays and numerical operations. NumPy replaces a lot of the functionality of Matlab and Mathematica specifically vectorized operations, but in contrast to those products is free and open source. In today's world of science and technology, it is all about speed and flexibility.

This book will teach you about NumPy, a leading scientific computing library. This book enables you to write readable, efficient, and fast code, which is closely associated to the language of mathematics. Save thousands of dollars on expensive software, while keeping all the flexibility and power of your favorite programming language.

You will learn about installing and using NumPy and related concepts. At the end of the book we will explore related scientific computing projects. This book will give you a solid foundation in NumPy arrays and universal functions. Learning NumPy Array will help you be productive with NumPy and write clean and fast code.

Approach

A step-by-step guide, packed with examples of practical numerical analysis that will give you a comprehensive, but concise overview of NumPy.

Who this book is for

This book is for programmers, scientists, or engineers, who have basic Python knowledge and would like to be able to do numerical computations with Python.

Learning NumPy Array

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Table of Contents

Learning NumPy Array

Credits

About the Author

About the Reviewers

www.PacktPub.com

Support files, eBooks, discount offers, and more

Why subscribe?

Free access for Packt account holders

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Getting Started with NumPy

Python

Installing NumPy, Matplotlib, SciPy, and IPython on Windows

Installing NumPy, Matplotlib, SciPy, and IPython on Linux

Installing NumPy, Matplotlib, and SciPy on Mac OS X

Building from source

NumPy arrays

Adding arrays

Online resources and help

Summary

2. NumPy Basics

The NumPy array object

The advantages of using NumPy arrays

Creating a multidimensional array

Selecting array elements

NumPy numerical types

Data type objects

Character codes

dtype constructors

dtype attributes

Creating a record data type

One-dimensional slicing and indexing

Manipulating array shapes

Stacking arrays

Splitting arrays

Array attributes

Converting arrays

Creating views and copies

Fancy indexing

Indexing with a list of locations

Indexing arrays with Booleans

Stride tricks for Sudoku

Broadcasting arrays

Summary

3. Basic Data Analysis with NumPy

Introducing the dataset

Determining the daily temperature range

Looking for evidence of global warming

Comparing solar radiation versus temperature

Analyzing wind direction

Analyzing wind speed

Analyzing precipitation and sunshine duration

Analyzing monthly precipitation in De Bilt

Analyzing atmospheric pressure in De Bilt

Analyzing atmospheric humidity in De Bilt

Summary

4. Simple Predictive Analytics with NumPy

Examining autocorrelation of average temperature with pandas

Describing data with pandas DataFrames

Correlating weather and stocks with pandas

Predicting temperature

Autoregressive model with lag 1

Autoregressive model with lag 2

Analyzing intra-year daily average temperatures

Introducing the day-of-the-year temperature model

Modeling temperature with the SciPy leastsq function

Day-of-year temperature take two

Moving-average temperature model with lag 1

The Autoregressive Moving Average temperature model

The time-dependent temperature mean adjusted autoregressive model

Outliers analysis of average De Bilt temperature

Using more robust statistics

Summary

5. Signal Processing Techniques

Introducing the Sunspot data

Sifting continued

Moving averages

Smoothing functions

Forecasting with an ARMA model

Filtering a signal

Designing the filter

Demonstrating cointegration

Summary

6. Profiling, Debugging, and Testing

Assert functions

The assert_almost_equal function

Approximately equal arrays

The assert_array_almost_equal function

Profiling a program with IPython

Debugging with IPython

Performing Unit tests

Nose tests decorators

Summary

7. The Scientific Python Ecosystem

Numerical integration

Interpolation

Using Cython with NumPy

Clustering stocks with scikit-learn

Detecting corners

Comparing NumPy to Blaze

Summary

Index

Learning NumPy Array

Copyright © 2014 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

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First published: June 2014

Production Reference: 1060614

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78398-390-2

www.packtpub.com

Cover Image by Duraid Fatouhi (<[email protected]>)

Credits

Author

Ivan Idris

Reviewers

Jonathan Bright

Jaidev Deshpande

Mark Livingstone

Miklós Prisznyák

Commissioning Editor

Kartikey Pandey

Acquisition Editor

Mohammad Rizvi

Content Development Editor

Akshay Nair

Technical Editors

Shubhangi H. Dhamgaye

Shweta S. Pant

Copy Editor

Sarang Chari

Project Coordinator

Lima Danti

Proofreaders

Maria Gould

Kevin McGowen

Indexer

Hemangini Bari

Production Coordinator

Arvindkumar Gupta

Cover Work

Arvindkumar Gupta

About the Author

Ivan Idris has an MSc in Experimental Physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are Business Intelligence, Big Data, and Cloud Computing. He enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy 1.5 Beginner's Guide and NumPy Cookbook, Packt Publishing. You can find more information and a blog with a few NumPy examples at ivanidris.net.

I would like to take this opportunity to thank the reviewers and the team at Packt Publishing for making this book possible. Also, I would like ...