Learning IPython for Interactive Computing and Data Visualization, Second Edition: Get started with Python for data analysis and numerical computing in the Jupyter notebook

Table of Contents

Learning IPython for Interactive Computing and Data Visualization Second Edition

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

Downloading the color images of this book

Errata

Piracy

Questions

1. Getting Started with IPython

What are Python, IPython, and Jupyter?

Jupyter and IPython

What this book covers

References

Installing Python with Anaconda

Downloading Anaconda

Installing Anaconda

Before you get started...

Opening a terminal

Finding your home directory

Manipulating your system path

Testing your installation

Managing environments

Common conda commands

References

Downloading the notebooks

Introducing the Notebook

Launching the IPython console

Launching the Jupyter Notebook

The Notebook dashboard

The Notebook user interface

Structure of a notebook cell

Markdown cells

Code cells

The Notebook modal interface

Keyboard shortcuts available in both modes

Keyboard shortcuts available in the edit mode

Keyboard shortcuts available in the command mode

References

A crash course on Python

Hello world

Variables

String escaping

Lists

Loops

Indentation

Conditional branches

Functions

Positional and keyword arguments

Passage by assignment

Errors

Object-oriented programming

Functional programming

Python 2 and 3

Going beyond the basics

Ten Jupyter/IPython essentials

Using IPython as an extended shell

Learning magic commands

Mastering tab completion

Writing interactive documents in the Notebook with Markdown

Creating interactive widgets in the Notebook

Running Python scripts from IPython

Introspecting Python objects

Debugging Python code

Benchmarking Python code

Profiling Python code

Summary

2. Interactive Data Analysis with pandas

Exploring a dataset in the Notebook

Provenance of the data

Downloading and loading a dataset

Making plots with matplotlib

Descriptive statistics with pandas and seaborn

Manipulating data

Selecting data

Selecting columns

Selecting rows

Filtering with boolean indexing

Computing with numbers

Working with text

Working with dates and times

Handling missing data

Complex operations

Group-by

Joins

Summary

3. Numerical Computing with NumPy

A primer to vector computing

Multidimensional arrays

The ndarray

Vector operations on ndarrays

How fast are vector computations in NumPy?

How an ndarray is stored in memory

Why operations on ndarrays are fast

Creating and loading arrays

Creating arrays

Loading arrays from files

Basic array manipulations

Computing with NumPy arrays

Selection and indexing

Boolean operations on arrays

Mathematical operations on arrays

A density map with NumPy

Other topics

Summary

4. Interactive Plotting and Graphical Interfaces

Choosing a plotting backend

Inline plots

Exported figures

GUI toolkits

Dynamic inline plots

Web-based visualization

matplotlib and seaborn essentials

Common plots with matplotlib

Customizing matplotlib figures

Interacting with matplotlib figures in the Notebook

High-level plotting with seaborn

Image processing

Further plotting and visualization libraries

High-level plotting

Bokeh

Vincent and Vega

Plotly

Maps and geometry

The matplotlib Basemap toolkit

GeoPandas

Leaflet wrappers: folium and mplleaflet

3D visualization

Mayavi

VisPy

Summary

5. High-Performance and Parallel Computing

Accelerating Python code with Numba

Random walk

Universal functions

Writing C in Python with Cython

Installing Cython and a C compiler for Python

Implementing the Eratosthenes Sieve in Python and Cython

Distributing tasks on several cores with IPython.parallel

Direct interface

Load-balanced interface

Further high-performance computing techniques

MPI

Distributed computing

C/C++ with Python

GPU computing

PyPy

Julia

Summary

6. Customizing IPython

Creating a custom magic command in an IPython extension

Writing a new Jupyter kernel

Displaying rich HTML elements in the Notebook

Displaying SVG in the Notebook

JavaScript and D3 in the Notebook

Customizing the Notebook interface with JavaScript

Summary

Index

Learning IPython for Interactive Computing and Data Visualization Second Edition

Learning IPython for Interactive Computing and Data Visualization Second Edition

Copyright © 2015 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: April 2013

Second edition: October 2015

Production reference: 1151015

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78398-698-9

www.packtpub.com

Credits

Author

Cyrille Rossant

Reviewers

Damián Avila

Nicola Rainiero

G Scott Stukey

Commissioning Editor

Kartikey Pandey

Acquisition Editors

Kartikey Pandey

Richard Brookes-Bland

Content Development Editor

Arun Nadar

Technical Editor

Pranil Pathare

Copy Editor

Stephen Copestake

Project Coordinator

Shweta H Birwatkar

Proofreader

Safis Editing

Indexer

Monica Ajmera Mehta

Production Coordinator

Conidon Miranda

Cover Work

Conidon Miranda

About the Author

Cyrille Rossant is a researcher in neuroinformatics, and is a graduate of Ecole Normale Superieure, Paris, where he studied mathematics and computer science. He has worked at Princeton University, University College London, and College de France. As part of his data science and software engineering projects, he gained experience in machine learning, high-performance computing, parallel computing, and big data visualization.

He is one of the main developers of VisPy, a high-performance visualization package in Python. He is the author of the IPython Interactive Computing and Visualization Cookbook, Packt Publishing, an advanced-level guide to data science and numerical computing with Python, and the sequel of this book.

I am grateful to Nick Fiorentini for his help during the revision of the book. I would also like to thank my family and notably my wife Claire for their support.

About the Reviewers

Damián Avila is a software developer and data scientist (formerly a biochemist) from Córdoba, Argentina.

His main focus of interest is data science, visualization, finance, and IPython/Jupyter-related projects.

In the open source area, he is a core developer for several interesting and popular projects, such as IPython/Jupyter, Bokeh, and Nikola. He has also started his own projects, being RISE, an extension to enable amazing live slides in the Jupyter notebook, the most popular one. He has also written several tutorials about the Scientific Python tools (available at Github) and presented several talks at international conferences.

Currently, he is working at Continuum Analytics.

Nicola Rainiero is a civil geotechnical engineer with a background in the construction industry as a self-employed designer engineer. He is also specialized in the renewable energy field and has collaborated with the Sant'Anna University of Pisa for two European projects, REGEOCITIES and PRISCA, using qualitative and quantitative data analysis techniques.

He has an ambition to simplify his work with open software and use and develop new ones; sometimes obtaining good results, at other times, negative. You can reach Nicola on his website at http://rainnic.altervista.org.

A special thanks to Packt Publishing for this opportunity to participate in the reviewing of this book. I thank my family, especially my parents, for their physical and moral support.

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Preface

Data analysis skills are now essential in scientific research, engineering, finance, economics, journalism, and many other domains. With its high accessibility and vibrant ecosystem, Python is one of the most appreciated open source languages for data science.

This book is a beginner-friendly introduction to the Python data analysis platform, focusing on IPython (Interactive Python) and its Notebook. While IPython is an enhanced interactive Python terminal specifically designed for scientific computing and data analysis, the Notebook is a graphical interface that combines code, text, equations, and plots in a unified interactive environment.

The first edition of Learning IPython for Interactive Computing and Data Visualization was published in April 2013, several months before the release of IPython 1.0. This new edition targets IPython 4.0, released in August 2015. In addition to reflecting the novelties of this new version of IPython, the present book is also more accessible to non-programmer beginners. The first chapter contains a brand new crash course on Python programming, as well as detailed installation instructions.

Since the first edition of this book, IPython's popularity has grown significantly, with an estimated user base of several millions of people and ongoing collaborations with large companies like Microsoft, Google, IBM, and others. The project itself has been subject to important changes, with a refactoring into a language-independent interface called the Jupyter Notebook, and a set of backend kernels in various languages. The Notebook is no longer reserved to Python; it can now also be used with R, Julia, Ruby, Haskell, and many more languages (50 at the time of this writing!).

The Jupyter project has received significant funding in 2015 from the Leona M. and Harry B. Helmsley Charitable Trust, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Foundation, which will allow the developers to focus on the growth and maturity of the project in the years to come.

Here are a few references:

Home page for the Jupyter project at http://jupyter.org/

Announcement of the funding for Jupyter at https://blog.jupyter.org/2015/07/07/jupyter-funding-2015/

Detail of the project's grant at https://blog.jupyter.org/2015/07/07/project-jupyter-computational-narratives-as-the-engine-of-collaborative-data-science/

What this book covers

Chapter 1, Getting Started with IPython, is a thorough and beginner-friendly introduction to Anaconda (a popular Python distribution), the Python language, the Jupyter Notebook, and IPython.

Chapter 2, Interactive Data Analysis with pandas, is a hands-on introduction to interactive data analysis and visualization in the Notebook with pandas, matplotlib, and seaborn.

Chapter 3, Numerical Computing with NumPy, details how to use NumPy for efficient computing on multidimensional numerical arrays.

Chapter 4, Interactive Plotting and Graphical Interfaces, explores many capabilities of Python for interactive plotting, graphics, image processing, and interactive graphical interfaces in the Jupyter Notebook.

Chapter 5, High-Performance and Parallel Computing, introduces the various techniques you can employ to accelerate your numerical computing code, namely parallel computing and compilation of Python code.

Chapter 6, Customizing IPython, shows how IPython and the Jupyter Notebook can be extended for customized use-cases.

What you need for this book

The following software is required for the book:

Anaconda with Python 3

Windows, Linux, or OS X can be used as a platform

Who this book is for

This book targets anyone who wants to analyze data or perform numerical simulations of mathematical models.

Since our world is becoming more and more data-driven, knowing how to analyze data effectively is an essential skill to learn. If you're used to spreadsheet programs like Microsoft Excel, you will appreciate Python for its much larger range of analysis and visualization possibilities. Knowing this general-purpose language will also let you share your data and analysis with other programs and libraries.

In conclusion, this book will be useful to students, scientists, engineers, analysts, journalists, statisticians, economists, hobbyists, and all data enthusiasts.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: Run it with a command like bash Anaconda3-2.3.0-Linux-x86_64.sh (if necessary, replace the filename by the one you downloaded).

A block of code is set as follows:

def load_ipython_extension(ipython):

    "This function is called when the extension is loaded.

    It accepts an IPython InteractiveShell instance.

    We can register the magic with the `register_magic_function`

    method of the shell instance."

    ipython.register_magic_function(cpp, 'cell')

Any command-line input or output is written as follows:

$ python Python 3.4.3