IPython Interactive Computing and Visualization Cookbook

by Cyrille Rossant (Author)

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ISBN-13: 978-1783284818

ISBN-10: 1783284811

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Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science with Python

About This Book

Leverage the new features of the IPython notebook for interactive web-based big data analysis and visualization
Become an expert in high-performance computing and visualization for data analysis and scientific modeling
A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations

Who This Book Is For

Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists... Basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.

What You Will Learn

Code better by writing high-quality, readable, and well-tested programs; profiling and optimizing your code, and conducting reproducible interactive computing experiments
Master all of the new features of the IPython notebook, including the interactive HTML/JavaScript widgets
Analyze data with Bayesian and frequentist statistics (Pandas, PyMC, and R), and learn from data with machine learning (scikit-learn)
Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV
Learn how to write blazingly fast Python programs with NumPy, PyTables, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA and OpenCL), parallel IPython, MPI, and many more

In Detail

IPython is at the heart of the Python scientific stack. With its widely acclaimed web-based notebook, IPython is today an ideal gateway to data analysis and numerical computing in Python.

IPython Interactive Computing and Visualization Cookbook contains many ready-to-use focused recipes for high-performance scientific computing and data analysis. The first part covers programming techniques, including code quality and reproducibility; code optimization; high-performance computing through dynamic compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics.

About the Author

Dr. Cyrille Rossant is a French researcher in computational neuroscience. A graduate of the Ecole Normale Supérieure, Paris, where he studied Mathematics and Computer Science, he has also worked at Princeton University and University College London. He is interested in all kinds of relationships between brains and computers, including models of neural processing, high-performance simulations of neural networks, and analysis of neurophysiologic data.

He has also worked on parallel computing and high-performance visualization technologies for Python, and he is a core developer of Vispy, a visualization package. He is the author of Learning IPython for Interactive Computing and Data Visualization, Packt Publishing, the prequel of this cookbook.

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Product details

Publisher ‏ : ‎ Packt Publishing (September 25, 2014)
Language ‏ : ‎ English
Paperback ‏ : ‎ 512 pages
ISBN-10 ‏ : ‎ 1783284811
ISBN-13 ‏ : ‎ 978-1783284818
Item Weight ‏ : ‎ 1.92 pounds
Dimensions ‏ : ‎ 7.5 x 1.16 x 9.25 inches

Best Sellers Rank: #3,095,597 in Books (See Top 100 in Books)
- #1,316 in Mathematical & Statistical Software
- #3,306 in Python Programming
- #4,446 in Web Design (Books)

Customer Reviews:
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Must buy

Reviewed in the United States on September 17, 2017

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Excellent book. Well written examples; easy to follow and very detailed. My only gripe with this book is the Kindle format. It's not as easy to navigate as a few others that I've bought.

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P. Sebastien

Great case studies

Reviewed in the United States on May 28, 2017

Verified Purchase

probably my preferred book in Python. Great case studies. Rashka, Rossant= fantastic books

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Matteo

Several examples to play and solve problems with IPython

Reviewed in the United States on January 16, 2015

This book provides a valid continuation from the author previous IPython book, giving further examples for visualization and advanced computing. The book is written with a simple but intermediate skill level so if you feel like reading gibberish you should try reading “Learning IPython for Interactive Computing and Data Visualization” so you can get a proper introduction to the basics of IPython(the books provide a very light introduction that may not be sufficient if you never handled the language). The book purpose is to give practical examples and code samples for giving you new ideas and maybe solve you some problems you've found previously so use it to learn by example

9 people found this helpful

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Michael Bright

An inspiring book!

Reviewed in the United States on February 23, 2015

I read a review copy, all 509 pages as this was a great read.
The book has a very broad coverage of interactive computing through the use of IPython.

Each chapter and sub-section finishes with a “There’s more …” section providing a large number of useful links for further study on that sections content, allowing the reader to investigate further.

The book steps us through all example source code, but the example source and example data are all provided in github repositories so all experiments can be reproduced by the reader. Sufficient information is provided to reproduce the experiments on Windows, Linux or OS X.

The earlier chapters introduce us to IPython, in it’s current 2.x form but also present what’s coming in the 3.0 release. It was impressive to see how IPython is evolving into a more interactive platform through the integration of Javascript capabilities - it is shown how IPython can be extended in various ways, and how widgets can be used to interact with the visualization - e.g. having a slider widget to modify an analysis and the associated plot. One example involves the implementation of a piano keyboard within IPython.
Impressive stuff showing how useful IPython is becoming for data analysis and visualization.

Nevertheless for an introduction to IPython the authors’ earlier book on PacktPub “Learning IPython for Interactive Computing and Data Visualization” is a recommended read.

The first part of the book covers high performance interactive computing, starting with IPython, its’ notebooks, profiling and optimization of code through various libraries including Numpy, Numba, Cython and even OpenCL or pyCUDA to harness GPUs. The final chapter of this section covers plotting libraries such as prettyplotlib, seaborn, Bokeh, NetworkX, D3.js, Vispy which go beyond the capabilities of the standard matplotlib.

The second part of the book enumerates how these capabilities can be applied in many domains of data science whether it be statistical data analysis, machine learning, optimization, signal processing, image and audio, deterministic and stochastic dynamic systems, graphs and geographical systems and finally symbolic mathematics.

There is a very impressive range of techniques covered in the book and the examples cover a wide range of ideas from pure statistics, to frequency domain (FFT), audio, image and graph and map plotting.

Whilst such a book can not go into great depth for so many subjects, tools and methods the book provides many realistic reproducible examples, in souce code, along with many references to be able to investigate further.

The last chapter deals with symbolic mathematics using sympy. I was quite amazed at what I was able to do by just installing one extra python module - sympy. Sympy is able to display mathematical formulas, via the Latex-capable MathJax library, to solve equations and the like.

Overall this book is a very interesting read and is packed with information, examples and very useful references. I recommend it to anyone wanting an overview of Python/IPython capabilities for data visualization.

An inspiring book?
Well yes, I'm inspired to delve deeper into IPython and its use in various data analytic domains, I'm also inspired to use the examples for teaching workshops.

3 people found this helpful

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Venkatesh Prasad Ranganath

A cookbook that seemingly targets IPython but provides very few IPython specific recipes.

Reviewed in the United States on March 28, 2015

When I pick up a cookbook for technology X, I expect the recipes to be grounded in technology X. While recipes can be about how to set up the technology or use the technology to accomplish a task, the book has to be about the technology. In this case (as with "Learning IPython for Interactive Computing and Data Visualization"), recipes focus on libraries such as NumPy, Pandas, Matplotlib, and SymPy, etc. and not specific to IPython.

Even if the book were shorter, I would have been happier if it focused on the abilities and features of IPython and left recipes specific to other technologies to cookbooks dedicated to those technologies. To elaborate, if a recipe involving NumPy can be applied independent of IPython, then the recipe is specific to NumPy. On the other hand, if a recipe involving NumPy relies on a feature of IPython and cannot be employed outside of IPython (e.g., python shell), then the recipe specific to IPython. Moreover, I suspect such a recipe would be applicable to other technologies that can be used with IPython. If so, the book should illustrate such general uses of a recipe.

In short, while I may return to this book, I will only do so for very few chapters that are specific to IPython.

7 people found this helpful

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Kristbjorn Helgason

All you need to know about doing data science in IPython

Reviewed in the United States on March 29, 2015

This book is a great source for both general data science methods and advanced interactive computing in Python. In particular, the chapter about the use of IPython Notebook is one of the best and most comprehensive text on the subject that I have seen. The introduction to different plotting libraries makes a good reference when matplotlib fails to give you the desired output.

The explanations are concisely written but each sub-chapter also offers an extensive list of links to more detailed reading material, if needed.

If you are looking for a book with extensive information about doing data science in IPython, everything from simple data munging to complicated machine learning, this is a good choice.

6 people found this helpful

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