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Learning IPython for Interactive Computing and Data Visualization (Englisch) Taschenbuch – 25. April 2013

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  • Learning IPython for Interactive Computing and Data Visualization
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Über den Autor und weitere Mitwirkende

Cyrille Rossant

Cyrille Rossant is a French researcher in quantitative neuroscience. A graduate of the Ecole Normale Supérieure, Paris, he holds a Master's degree and a Ph.D. in Mathematics and Computer Science. He uses IPython every day to model and simulate the brain and to analyze experimental data. He is the creator of a few scientific Python packages, including Playdoh (parallel computing) and Galry (high-performance interactive visualization).

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Format: Kindle Edition Verifizierter Kauf
This book tries to introduce you to a wide range of topics from ipython, numpy, scipy, matplotlib over pandas to cython etc. It's not just on ipython as the title might suggest and it's mainly targeted at the beginner who was solid Python knowledge though. It is only able to scratch the surface of the discussed packages and I would have preferred an in depth look at just some of these. A good chunk of the book is on ipython and I learned a few good tricks from that section. However overall, if you've ever played around with any of the other libraries for longer than 15 minutes, you probably already know everything that you will learn from this book.
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Amazon.com: 4.1 von 5 Sternen 16 Rezensionen
15 von 15 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen valuable but traditional 4. Juni 2013
Von Catherine Devlin - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Packt Publishing recently asked if I could review their new title, Learning IPython for Interactive Computing and Data Visualization. (I got the e-book free for doing the review, but they don't put any conditions on what I say about it.) I don't often do reviews like that, but I couldn't pass one this up because I'm so excited about the IPython Notebook.

It's a mini title, but it does contain a lot of information I was very pleased to see. First and foremost, this is the first book to focus on the IPython Notebook. That's huge. Also:

The installation section is thorough and goes well beyond the obvious, discussing options like using prepackaged all-in-one Python distributions like Anaconda.
Some of the improvements IPython can make to a programming workflow are nicely introduced, like the ease of debugging, source code inspection, and profiling with the appropriate magics.
The section on writing new IPython extensions is extremely valuable - it contains more complete examples than the official documentation does and would have saved me lots of time and excess code if I'd had it when I was writing ipython-sql.
There are introductions to all the classic uses that scientists doing numerical simulations value IPython for: convenience in array handling, Pandas integration, plotting, parallel computing, image processing, Cython for faster CPU-bound operations, etc. The book makes no claim to go deeply into any of these, but it gives introductory examples that at least give an idea of how the problems are approached and why IPython excels at them.

So what don't I like? Well, I wish for more. It's not fair to ask for more bulk in a small book that was brought to market swiftly, but I can wish for a more forward-looking, imaginative treatment. The IPython Notebook is ready to go far beyond IPython's traditional core usership in the SciPy community, but this book doesn't really make that pitch. It only touches lightly on how easily and beautifully IPython can replace shell scripting. It doesn't get much into the unexplored possibilities that IPython Notebook's rich display capabilities open up. (I'm thinking of IPython Blocks as a great example of things we can do with IPython Notebook that we never imagined at first glance). This book is a good introduction to IPython's uses as traditionally understood, but it's not the manifesto for the upcoming IPython Notebook Revolution.

The power of hybrid documentation/programs for learning and individual and group productivity is one more of IPython Notebook's emerging possibilities that this book only mentions in passing, and passes up a great chance to demonstrate. The sample code is downloadable as IPython Notebook .ipynb files, but the bare code is alone in the cells, with no use of Markdown cells to annotate or clarify. Perhaps this is just because Packt was afraid that more complete Notebook files would be pirated, but it's a shame.

Overall, this is a short book that achieves its modest goal: a technical introduction to IPython in its traditional uses. You should get it, because IPython Notebook is too important to sit around waiting for the ultimate book - you should be using the Notebook today. But save space on your bookshelf for future books, because there's much more to be said on the topic, some of which hasn't even been imagined yet.

(copy of the review posted at http://catherinedevlin.blogspot.com/2013/05/review-of-learning-ipython-for.html)
7 von 7 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen One of the best available guide on Ipython 21. Mai 2013
Von Francesco Grigoli - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This is a concise book (only 138 pages) that introduce you Ipython, a very powerful tool for computing and data visualization. The book easily exaplain: 1) how to manipulate arrays using python numerical libraries, 2) how to plot data, maps and create animations and 3) how to parallelize codes using Ipython. The book is easy to read and full of practical examples, It does not require to be a python "guru", even if the reader it is supposed to have a basic knowledge of the language. The large number of examples within the book allow to learn Ipython basics quikly and without much efford. This book is a must for who would like to use python for scientific applications.
5 von 5 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Great introduction to IPython and other tools of the ecosystem 25. Mai 2013
Von Amit Saha - Veröffentlicht auf Amazon.com
Format: Kindle Edition
I received a review copy of Packt Publishing's Learning IPython for Interactive Computing and Data Visualization by Cyrille Rossant. Although the book title mentions only IPython, the book looks into using a number of other Python tools and libraries of potential use to the intended audience. Here is my review.

(The book uses Python 2).


The book has six chapters, so it's a quick read. In the first two chapters, the author helps the reader getting started with using IPython (installation, basic things to do, using IPython as a shell) and also using IPython notebook for interactive python programming. He demonstrates how to perform basic profiling, measuring the run time of your scripts/statements and also discusses plotting with matplotlib (via pylab) from IPython notebook.

Chapter 3 introduces vector operations and using NumPy for performing the same. Topics such as indexing, reshaping are introduced in this chapter. This chapter also introduces the Pandas tool and demonstrates using it using a publicly available data set.

Chapter 4 discusses plotting, graphing and visualization in detail using matplotlib and others.

Chapter 5 discusses two main of concepts. One, running your programs on multiple cores and basics of using the Message Passing Interface (MPI). The second main concept discussed is using Cython. At the end, the chapter also mentions libraries such as Blaze and Numba which are of potential usefulness to the intended audience.

The final chapter of the book discusses customizing IPython (creating profiles, etc.), and also shows you can create an extension that introduces a new cell magic.

Interesting Features

Hands-on style
Up -to-date information and references
Just enough information for the reader to learn and explore more

The book is interesting and pleasant to read and follow. It does well in introducing features of IPython and other tools of interest to the book's audience. Definitely worth buying.
4 von 4 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Excellent introduction to IPython workflows for scientific computation 16. Mai 2013
Von Dan Goodman - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Recently, IPython has really become an excellent tool for scientific computation, growing far beyond its roots as an enhanced interactive shell for Python. It now supports the IPython notebook (which lets you mix text, mathematics, code and results, like in Mathematica notebooks), and a parallel computing engine to use multiple cores or machines. Unfortunately, the online documentation is not as easy to follow as it could be, which is where Dr Rossant's book comes in handy.

He covers everything from installation to advanced topics like high performance computing and customizing IPython, using clear, worked examples with publicly available datasets. In addition to IPython, he also briefly covers using important scientific computing Python packages such as NumPy, SciPy, Cython and Pandas.

If you haven't yet tried IPython or if you've only just started using it, I'd highly recommend it. There's even plenty of stuff in there for more experiened users too.
2 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen Concise introduction to IPython 7. November 2013
Von JustGlowing - Veröffentlicht auf Amazon.com
Format: Taschenbuch
The book introduces the IPython basics and then focuses on how to combine IPython with some of the most useful libraries for data analysis such as Numpy, Matplotlib, Basemap and Pandas. Every topic is covered with examples and the code presented is also available online. The references proposed are always up-to-date and give the reader the opportunity to discovery resources not covered in the book.

In conclusion, this book definitely achieves its goal to provide a technical introduction to IPython. It is intended for Python users who want an easy to follow introduction to IPython, but also experienced users will find this book useful. It is to notice that, at the moment, this is the only book about IPython.
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