IPython Notebook Essentials

More Information
Learn
  • Quickly install and get started with IPython Notebook
  • Create interactive widgets in the Notebook
  • Master the Notebook's interface and navigation features
  • Create publication-quality graphs and displays of data with matplotlib
  • Add media to the Notebook with IPython's Rich Display System
  • Accelerate code using NumbaPro and concurrent computing
  • Perform advanced scientific computations with SciPy
  • Work with data in the Notebook with pandas
About

In data science, it is difficult to present interesting visual or technical content, as it involves scientific notations that are not easy to type in a normal document format. IPython provides a web-based UI called Notebook, which creates a working environment for interactive computing that combines code execution with computational documents. IPython Notebook makes the task simpler as it was developed for scientific programming to solve larger problems through a series of smaller programs. IPython Notebook is used to learn Python in a fun and interactive way and to do some serious parallel / technical computing.

The book begins with an introduction to the efficient use of IPython Notebook for interactive computation. The book then focuses on the integration of technologies such as matplotlib, pandas, and SciPy. The book is aimed at empowering you to work with IPython Notebook for interactive computing, configuring it, creating your own notebooks / research documents. You will learn how IPython lets you perform efficient computations through examples with NumPy, data analysis with pandas, and visualization with matplotlib.

Features
  • Perform Computational Analysis interactively
  • Create quality displays using matplotlib and Python Data Analysis
  • Step-by-step guide with a rich set of examples and a thorough presentation of The IPython Notebook
Page Count 190
Course Length 5 hours 42 minutes
ISBN 9781783988341
Date Of Publication 21 Nov 2014

Authors

L. Felipe Martins

L. Felipe Martins has a PhD in applied mathematics from Brown University and is currently an associate professor in the Department of Mathematics at Cleveland State University. His main research areas are applied probability and scientific computing. He has taught applied mathematics courses at all levels, including linear algebra, differential equations, probability, and optimization, and uses Python as an instructional tool in all courses. He is the author of two books, IPython Notebook Essentials and Mastering Python Data Analysis.