Learning IPython for Interactive Computing and Data Visualization

Cyrille Rossant

• Publisher: Packt Publishing
• Year: 2013
• eBook ISBN: 9781782169932
• Edition: 1
• Topic: Informatica
• Subtopic: Informatica generale

In Detail

You already use Python as a scripting language, but did you know it is also increasingly used for scientific computing and data analysis? Interactive programming is essential in such exploratory tasks and IPython is the perfect tool for that. Once you've learnt it, you won't be able to live without it.

"Learning IPython for Interactive Computing and Data Visualization" is a practical, hands-on, example-driven tutorial to considerably improve your productivity during interactive Python sessions, and shows you how to effectively use IPython for interactive computing and data analysis.

This book covers all aspects of IPython, from the highly powerful interactive Python console to the numerical and visualization features that are commonly associated with IPython.

You will learn how IPython lets you perform efficient vectorized computations, through examples covering numerical simulations with NumPy, data analysis with Pandas, and visualization with Matplotlib. You will also discover how IPython can be conveniently used to optimize your code using parallel computing and dynamic compilation in C with Cython.

"Learning IPython for Interactive Computing and Data Visualization" will allow you to optimize your productivity in interactive Python sessions.

Approach

A practical hands-on guide which focuses on interactive programming, numerical computing, and data analysis with IPython.

Who this book is for

This book is for Python developers who use Python as a scripting language or for software development, and are interested in learning IPython for increasing their productivity during interactive sessions in the console. Knowledge of Python is required, whereas no knowledge of IPython is necessary.

Learning IPython for Interactive Computing and Data Visualization

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Table of Contents

Learning IPython for Interactive Computing and Data Visualization

Credits

About the Author

About the Reviewer

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 IPython

Installing IPython and the recommended packages

Prerequisites for IPython

Installing an all-in-one distribution

Installing the packages one by one

Packages websites

Getting binary installers

Windows

OS X

Linux

Table of binary packages

Using the Python packaging system

Optional dependencies for IPython

Installing the development versions

Ten IPython essentials

Running the IPython console

Using IPython as a system shell

Using the history

Tab completion

Executing a script with the %run command

Quick benchmarking with the %timeit command

Quick debugging with the %debug command

Interactive computing with Pylab

Using the IPython Notebook

Customizing IPython

Summary

2. Interactive Work with IPython

The extended shell

Navigating through the filesystem

Accessing the system shell from IPython

The extended Python console

Exploring the history

Import/export of Python code

Importing code in IPython

Exporting code to a file

Dynamic introspection

Tab completion

An example of tab completion NetworkX

Tab completion with custom classes

Source code introspection

Using the interactive debugger

Interactive benchmarking and profiling

Controlling the execution time of a command

Profiling a script

Using the IPython notebook

Installation

The notebook dashboard

Working with cells

Cell magics

Managing notebooks

Multimedia and rich text editing

Graph plotting

Summary

3. Numerical Computing with IPython

A primer to vector computing

An example of computation with Python loops

What an array is

Reimplementing the example with arrays

Creating and loading arrays

Creating arrays

From scratch, element by element

From scratch, using predefined templates

From random values

Loading arrays

From a native Python object

From a buffer or an external file

Using Pandas

Working with arrays

Selection

Using Pandas

Using NumPy

More indexing possibilities

Manipulation

Reshaping

Repeating and concatenating

Broadcasting

Permuting

Computation

Advanced mathematical processing

Summary

4. Interactive Plotting and Graphical Interfaces

Figures with Matplotlib

Setting up IPython for interactive visualization

Using Matplotlib

Interactive navigation

Matplotlib in the IPython notebook

Standard plots

Curves

Scatter plots

Bar graphs

Plot customization

Styles and colors

Grid, axes, and legends

Interaction from IPython

Drawing multiple plots

Advanced figures and graphics

Image processing

Loading images

Showing images

Using PIL

Advanced image processing – color quantization

Maps

3D plots

Animations

Other visualization packages

Graphical User Interfaces (GUI)

Setting up IPython for interactive GUIs

A "Hello World" example

Summary

5. High-Performance and Parallel Computing

Interactive task parallelization

Parallel computing in Python

Distributing tasks on multiple cores

Starting the engines

Creating a Client instance

Using the parallel magic

Parallel map

Creating a view

Synchronous map

Asynchronous map

A practical example – Monte Carlo simulations

Using MPI with IPython

Advanced parallel computing features of IPython

Using C in IPython with Cython

Installing and configuring Cython

Using Cython from IPython

Accelerating a pure Python algorithm with Cython

Pure Python version

Naïve Cython conversion

Adding C types

Using NumPy and Cython

Python version

Cython version

More advanced options for accelerating Python code

Summary

6. Customizing IPython

IPython profiles

Profile locations

The IPython configuration files

Loading scripts when IPython starts

IPython extensions

Example – line-by-line profiling

Creating new extensions

Example – executing C++ code in IPython

Rich representations in the frontend

Embedding IPython

Final words

Summary

Index

Learning IPython for Interactive Computing and Data Visualization

Copyright © 2013 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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Table of contents

• Learning IPython for Interactive Computing and Data Visualization