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Mastering Python Scientific Computing

You're reading from   Mastering Python Scientific Computing A complete guide for Python programmers to master scientific computing using Python APIs and tools

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781783288823
Length 300 pages
Edition 1st Edition
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Author (1):
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 Kumar Mehta Kumar Mehta
Author Profile Icon Kumar Mehta
Kumar Mehta
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Table of Contents (12) Chapters Close

Preface 1. The Landscape of Scientific Computing – and Why Python? 2. A Deeper Dive into Scientific Workflows and the Ingredients of Scientific Computing Recipes FREE CHAPTER 3. Efficiently Fabricating and Managing Scientific Data 4. Scientific Computing APIs for Python 5. Performing Numerical Computing 6. Applying Python for Symbolic Computing 7. Data Analysis and Visualization 8. Parallel and Large-scale Scientific Computing 9. Revisiting Real-life Case Studies 10. Best Practices for Scientific Computing Index

IPython


IPython is designed and developed with the aim of providing an enhanced Python shell that makes it possible to perform interactive distributed and parallel computing. IPython also has a set of tools for building special-purpose interactive environments for scientific computing. It has two components that help fulfill the aim of IPython:

  • An enhanced interactive IPython shell

  • Architecture for interactive parallel computing

In this section, we will first discuss the components that enhance the interactive IPython shell. We will cover the other component for interactive parallel computing in Chapter 8, Parallel and Large-scale Scientific Computing.

The IPython console and system shell

The interface provided by IPython is shown in the next screenshot. We can apply a different coloring scheme to this console; the default coloring scheme is NoColor. We have other options such as Linux and LightBG. An important feature of IPython is that it is stateful, as it maintains the state of the computations...

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