IPython Interactive Computing and Visualization Cookbook: Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

Cyrille Rossant

€22.99 ~~€32.99~~

4.5 (13 Ratings)

eBook Sep 2014 512 pages 1st Edition

Cyrille Rossant

€22.99 ~~€32.99~~

4.5 (13 Ratings)

eBook Sep 2014 512 pages 1st Edition

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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 highquality, readable, and welltested 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 (scikitlearn)
Gain valuable insights into signals, images, and sounds with SciPy, scikitimage, 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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

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DRM FREE - Read whenever, wherever and however you want

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P. Sebastien May 28, 2017

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

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C. Smith Mar 26, 2015

This book does a great job of providing both a useful overview and introduction to python as well as demonstrating the power and advanced functionality across a broad spectrum of topics in data science, mathematic and visualization. This book includes practical examples and code. Highly recommended.

Randy Heiland Mar 29, 2015

This book was well worth purchasing, in my opinion. I follow the ipython-dev list and I've seen the author's name appear many times, with very useful contributions, so I felt he certainly had the credibility to write this book. Any book about a software project runs the risk of becoming out of date as the project evolves, but in this case, at least so far, it holds up very well.The book has considerable breadth, as can be seen from the online Table of Contents (although the last page of the ToC seems to be missing). Breadth isn't necessarily a positive feature for every book/reader, but I personally like it. If one wants more depth, it's probably better to use online resources anyway.I appreciate the author being so pro open source and that he provides a github repo for code related to the book. And I personally agree with his decision to recommend using Anaconda's Python distribution; it makes life much easier when installing packages from the larger Python ecosystem. He might have gone one step further and encourage readers to join the Anaconda mailing list, but hopefully they'll figure that out on their own.I'd say the target audience is probably college students - any student in need of doing some interactive computational or data science. However, I *wish* more high school students would pick up a book like this and use it. MATLAB is fine, but Python + matplotlib, etc is better in many ways.I appreciate being made aware of the author's own visualization package, vispy, and hope to play with it more in the future. However, I was a little surprised and disappointed he didn't also mention VTK (although VTK doesn't currently install using conda/Anaconda Python 3.4).I thought it was especially brave (and admirable) of the author to contribute Chapter 5 on HPC, especially for multiple operating systems, GPU libs (CUDA and OpenCL), and MPI. Fairly advanced stuff, but worth the read.I sincerely appreciate the author undertaking this book project and commend him on the selection of topics and the overall layout. A good read with good visuals. Thanks!

Kristbjorn Helgason Mar 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.

Michael Bright Feb 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.

IPython Interactive Computing and Visualization Cookbook: Harness IPython for powerful scientific computing and Python data visualization with this collection of more than 100 practical data science recipes

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