Start reading on your Kindle in under a minute. Don't have a Kindle? Get your Kindle here or start reading now with a free Kindle Reading App.

Deliver to your Kindle or other device


Try it free

Sample the beginning of this book for free

Deliver to your Kindle or other device

Learning IPython for Interactive Computing and Data Visualization

Learning IPython for Interactive Computing and Data Visualization [Kindle Edition]

Cyrille Rossant
3.0 out of 5 stars  See all reviews (2 customer reviews)

Print List Price: £18.99
Kindle Price: £9.59 includes VAT* & free wireless delivery via Amazon Whispernet
You Save: £9.40 (49%)
* Unlike print books, digital books are subject to VAT.

Free Kindle Reading App Anybody can read Kindle books—even without a Kindle device—with the FREE Kindle app for smartphones, tablets and computers.

To get the free app, enter your e-mail address or mobile phone number.


Amazon Price New from Used from
Kindle Edition £9.59  
Paperback £18.99  
Kindle Daily Deal
Kindle Daily Deal: Up to 70% off
Each day we unveil a new book deal at a specially discounted price--for that day only. Learn more about the Kindle Daily Deal or sign up for the Kindle Daily Deal Newsletter to receive free e-mail notifications about each day's deal.

Product Description

Product Description

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.


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.

About the Author

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).

Product details

  • Format: Kindle Edition
  • File Size: 2059 KB
  • Print Length: 138 pages
  • Publisher: Packt Publishing (25 April 2013)
  • Sold by: Amazon Media EU S.à r.l.
  • Language: English
  • ISBN-10: 1782169946
  • ISBN-13: 978-1782169949
  • Text-to-Speech: Enabled
  • X-Ray:
  • Word Wise: Not Enabled
  • Average Customer Review: 3.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: #300,864 Paid in Kindle Store (See Top 100 Paid in Kindle Store)
  •  Would you like to give feedback on images?

More About the Author

Discover books, learn about writers, and more.

What Other Items Do Customers Buy After Viewing This Item?

Customer Reviews

5 star
4 star
2 star
1 star
3.0 out of 5 stars
3.0 out of 5 stars
Most Helpful Customer Reviews
1 of 1 people found the following review helpful
Format:Paperback|Verified Purchase
A valuable set of inter-related topics are covered in this book. It covers ipython, pandas, cython, numpy, parallel computing, etc. These are all really important parts of the scientific python ecosystem, and there are few or no books out there that properly deal with them all together. For that reason, this book is well recommended. However, I was disappointed in how thin and minimal the coverage of these topic was. Altogether, the book was 119 pages. For this set of topics, this meant that all we got was a brief introduction to each of these topics. That introduction was good, but that's all it was.

Another thing to mention is that this book is titled as if it is a book about IPython. However, it really is not. Ipython is just one of the set of tools it covers. A more appropriate title might be "A good but brief introduction to scientific and numerical python, including ipython, matplotlib, numpy, cython, pandas, parallel python, etc. Essentially, an appetiser".
Comment | 
Was this review helpful to you?
2 of 8 people found the following review helpful
3.0 out of 5 stars Not sure about this one 2 Sep 2013
Format:Paperback|Verified Purchase
I guess I am not sure that I really see the point of IPython. I have followed the examples, and get them to work, but I am left asking why I don't just use standard Python in a Visual Studio framework, and simply use the debugging facilities that supplies. I know it is meant to be more of a Matlab replacement, than a Program development environment; but having used both, I am not completely convinced that IPython is all that useful, and hence the need for this book. I will continue to explore IPython, and if I change my mind, I will comeback and revamp this review.

Additional Points. I guess I have to agree with Jean-Luc Ricard's comment about this being more of a statement on Python than a review of the book. I still maintain the 3 star rating. The book is not as well put together as Wes McKinney's book "Python for Data Analysis", but then it is approximately half the length. The book failed to explain to me the advantages of using IPython as against a more conventional approach detailed in the earlier section. Although perhaps this is a purely personal quirk.

Having purchased both this book and the McKinney book; knowing what I know now, I think my recommendation would be if you need to learn IPython, go for the McKinney book.

Many thanks to Jean-Luc for making a very helpful comment.
Was this review helpful to you?
Most Helpful Customer Reviews on (beta) 4.1 out of 5 stars  14 reviews
13 of 13 people found the following review helpful
4.0 out of 5 stars valuable but traditional 4 Jun 2013
By Catherine Devlin - Published on
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
7 of 7 people found the following review helpful
5.0 out of 5 stars One of the best available guide on Ipython 21 May 2013
By Francesco Grigoli - Published on
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.
4 of 4 people found the following review helpful
5.0 out of 5 stars Great introduction to IPython and other tools of the ecosystem 25 May 2013
By Amit Saha - Published on
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 of 4 people found the following review helpful
5.0 out of 5 stars Excellent introduction to IPython workflows for scientific computation 16 May 2013
By Dan Goodman - Published on
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 of 2 people found the following review helpful
5.0 out of 5 stars Concise introduction to IPython 7 Nov 2013
By JustGlowing - Published on
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.
Were these reviews helpful?   Let us know
Search Customer Reviews
Only search this product's reviews

Customer Discussions

This product's forum
Discussion Replies Latest Post
No discussions yet

Ask questions, Share opinions, Gain insight
Start a new discussion
First post:
Prompts for sign-in

Search Customer Discussions
Search all Amazon discussions

Look for similar items by category