Learning IPython for Interactive Computing and Data Visualization

Medizin, Wissenschaft & Technik

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Buchbeschreibung zu „Learning IPython for Interactive Computing and Data Visualization“

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.

Über Cyrille Rossant

Cyrille Rossant is a researcher in neuroinformatics, and is a graduate of Ecole Normale Superieure, Paris, where he studied mathematics and computer science. He has worked at Princeton University, University College London, and College de France.
As part of his data science and software engineering projects, he gained experience in machine learning, high-performance computing, parallel computing, and big data visualization. He is one of the developers of Vispy, a high-performance visualization package in Python. He is the author of Learning IPython for Interactive Computing and Data Visualization, Packt Publishing, a beginner-level introduction to data analysis in Python, and the prequel of this book.


Packt Publishing




ca. 118

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