Learning NumPy Array 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 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. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. 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 to thank my teachers, professors, and colleagues who taught me about science and programming. Last, but not least, I would like to acknowledge my parents, family, and friends for their support.