Cyrille Rossant - Learning IPython for Interactive Computing and Data Visualization

Cyrille Rossant is a French researcher in quantitative neuroscience. A graduate of the Ecole Normale Suprieure, 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).

I am grateful to the vibrant Python community for developing this great open platform for computational science. Devoting hard work to open-source software sometimes requires personal sacrifice, but it's worth the effort. In particular, I would like to thank Fernando Perez, creator of IPython, and all the development team for their awesome work on this library. Also, we regular Matplotlib users are all deeply grateful to its creator John Hunter, whose untimely passing in 2012 is a tragedy for the whole community and beyond.

I would also like to thank the reviewers for their helpful comments and suggestions. Finally, I am grateful to my family and Claire for their support during the writing of this book.

Matthias Bussonnier is a young French physicist working in biophysics. He has been a core developer of IPython since 2011.

I'd like to thank all my family, colleagues, as well as the IPython core team for their help and the fun moments spent developing for the open source community.

Dr. Francisco J. Blanco-Silva , the owner of a scientific consulting companyTizona Scientific Solutionsand adjunct faculty in the Department of Mathematics of the University of South Carolina has obtained his formal training as an applied mathematician at Purdue University. He enjoys problem solving, learning, and teaching. An avid programmer and blogger, when it comes to writing he relishes finding that common denominator among his passions and skills, and making it available to everyone.

He has written the technical book Learning SciPy for Numerical and Scientific Computing, Packt Publishing .

He has also co-authored Chapter 5 of the book Modeling Nanoscale Imaging in Electron Microscopy, Springer 201, Thomas Vogt and Wolfgang Dahmen, Springer .

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Preface

You are a programmer using Python as a scripting language, maybe for software development. Learning IPython will let you use Python interactively in a highly efficient way, for example, when exploring algorithms or analyzing data. In addition, it is the best way to be introduced to the most advanced capabilities of the platform, namely numerical computing, interactive visualization, and parallel programming.

What this book covers

, Getting Started with IPython , is a short, hands-on introduction to the key features of IPython. It will give you a broad overview of what IPython offers. All features introduced in this chapter will be covered in the subsequent chapters.

, Interactive Work with IPython , will show you how to use Python interactively from the IPython command-line interface, and how the numerous magic commands will help you considerably improve your productivity. This chapter will also introduce you to the IPython notebook, a modern tool for reproducible and collaborative interactive programming.

, Numerical Computing with IPython , contains an introduction to the numerical computing features of Numpy and Pandas, which can be conveniently used from IPython. These tools are essential as soon as you need to analyze large amounts of data, or more generally when you need to perform efficient numerical computations.

, Interactive Plotting and Graphical Interfaces , covers the graphical capabilities of Matplotlib, and shows how they integrate smoothly in IPython. Matplotlib is a very powerful graphical library, which allows you to either generate high-quality figures or to visualize data interactively.

, High-Performance and Parallel Computing