Learning IPython for Interactive Computing and Data Visualization, Second Edition E-Book
Cyrille Rossant
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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors.
This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.
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Seitenzahl: 194
Veröffentlichungsjahr: 2015
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Table of Contents
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.
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First published: April 2013
Second edition: October 2015
Production reference: 1151015
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78398-698-9
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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:
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:
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.
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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:
Any command-line input or output is written as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "To create a new notebook, click on the New button, and select Notebook (Python 3)."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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Chapter 1. Getting Started with IPython
In this chapter, we will cover the following topics:
What are Python, IPython, and Jupyter?
Python is an open source general-purpose language created by Guido van Rossum in the late 1980s. It is widely-used by system administrators and developers for many purposes: for example, automating routine tasks or creating a web server. Python is a flexible and powerful language, yet it is sufficiently simple to be taught to school children with great success.
In the past few years, Python has also emerged as one of the leading open platforms for data science and high-performance numerical computing. This might seem surprising as Python was not originally designed for scientific computing. Python's interpreted nature makes it much slower than lower-level languages like C or Fortran, which are more amenable to number crunching and the efficient implementation of complex mathematical algorithms.
However, the performance of these low-level languages comes at a cost: they are hard to use and they require advanced knowledge of how computers work. In the late 1990s, several scientists began investigating the possibility of using Python for numerical computing by interoperating it with mainstream C/Fortran scientific libraries. This would bring together the ease-of-use of Python with the performance of C/Fortran: the dream of any scientist!
Consequently, the past 15 years have seen the development of widely-used libraries such as NumPy (providing a practical array data structure), SciPy (scientific computing), matplotlib (graphical plotting), pandas (data analysis and statistics), scikit-learn (machine learning), SymPy (symbolic computing), and Jupyter/IPython (efficient interfaces for interactive computing). Python, along with this set of libraries, is sometimes referred to as the SciPy stack or PyData platform.
Tip
Competing platforms
Python has several competitors. For example, MATLAB (by Mathworks) is a commercial software focusing on numerical computing that is widely-used in scientific research and engineering. SPSS (by IBM) is a commercial software for statistical analysis. Python, however, is free and open source, and that's one of its greatest strengths. Alternative open source platforms include R (specialized in statistics) and Julia (a young language for high-performance numerical computing).
More recently, this platform has gained popularity in other non-academic communities such as finance, engineering, statistics, data science, and others.
This book provides a solid introduction to the whole platform by focusing on one of its main components: Jupyter/IPython.
Jupyter and IPython
IPythonwas created in 2001 by Fernando Perez (the I in IPython stands for "interactive"). It was originally meant to be a convenient command-line interface to the scientific Python platform. In scientific computing, trial and error is the rule rather than the exception, and this requires an efficient interface that allows for interactive exploration of algorithms, data, and graphs.
In 2011, IPython introduced the interactive Notebook. Inspired by commercial software such as Maple (by Maplesoft) or Mathematica (by Wolfram Research), the Notebook runs in a browser and provides a unified web interface where code, text, mathematical equations, plots, graphics, and interactive graphical controls can be combined into a single document. This is an ideal interface for scientific computing. Here is a screenshot of a notebook:
Example of a notebook
It quickly became clear that this interface could be used with languages other than Python such as R, Julia, Lua, Ruby, and many others. Further, the Notebook is not restricted to scientific computing: it can be used for academic courses, software documentation, or book writing thanks to conversion tools targeting Markdown, HTML, PDF, ODT, and many other formats. Therefore, the IPython developers decided in 2014 to acknowledge the general-purpose nature of the Notebook by giving a new name to the project: Jupyter.
Jupyter features a language-independent Notebook platform that can work with a variety of kernels. Implemented in any language, a kernel is the backend of the Notebook interface. It manages the interactive session, the variables, the data, and so on. By contrast, the Notebook interface is the frontend of the system. It manages the user interface, the text editor, the plots, and so on. IPython is henceforth the name of the Python kernel for the Jupyter Notebook. Other kernels include IR, IJulia, ILua, IRuby, and many others (50 at the time of this writing).
In August 2015, the IPython/Jupyter developers achieved the "Big Split" by splitting the previous monolithic IPython codebase into a set of smaller projects, including the language-independent Jupyter Notebook (see https://blog.jupyter.org/2015/08/12/first-release-of-jupyter/). For example, the parallel computing features of IPython are now implemented in a standalone Python package named ipyparallel, the IPython widgets are implemented in ipywidgets, and so on. This separation makes the code of the project more modular and facilitates third-party contributions. IPython itself is now a much smaller project than before since it only features the interactive Python terminal and the Python kernel for the Jupyter Notebook.
Note
You will find the list of changes in IPython 4.0 at http://ipython.readthedocs.org/en/latest/whatsnew/version4.html. Many internal IPython imports have been deprecated due to the code reorganization. Warnings are raised if you attempt to perform a deprecated import. Also, the profiles have been removed and replaced with a unique default profile. However, you can simulate this functionality with environment variables. You will find more information at http://jupyter.readthedocs.org.
What this book covers
This book covers the Jupyter Notebook 1.0 and focuses on its Python kernel, IPython 4.0. In this chapter, we will introduce the platform, the Python language, the Jupyter Notebook interface, and IPython. In the remaining chapters, we will cover data analysis and scientific computing in Jupyter/IPython with the help of mainstream scientific libraries such as NumPy, pandas, and matplotlib.
Note
This book gives you a solid introduction to Jupyter and the SciPy platform. The IPython Interactive Computing and Visualization Cookbook (http://ipython-books.github.io/cookbook/) is the sequel of this introductory-level book. In 15 chapters and more than 500 pages, it contains a hundred recipes covering a wide range of interactive numerical computing techniques and data science topics. The IPython Cookbook is an excellent addition to the present IPython minibook if you're interested in delving into the platform in much greater detail.
References
Here are a few references about IPython and the Notebook:
Installing Python with Anaconda
Although Python is an open-source, cross-platform language, installing it with the usual scientific packages used to be overly complicated. Fortunately, there is now an all-in-one scientific Python distribution, Anaconda
