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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

Matplotlib


The most popular Python package for working on two-dimensional graphics and chart plotting is matplotlib. It provides a very quick way of data visualization in the form of different types of plots/charts. It also supports exporting of these plots into various formats. We will be starting the discussion of matplotlib with the basics and architecture, and then we will discuss the plotting of various types of charts using sample programs.

The architecture of matplotlib

The most important matplotlib object is Figure. It contains and manages all the elements of the given charts/graphics. matplotlib has separated the figure representation and manipulation activity from the rendering of Figure to the user interface screen or the devices. This enables users to design and develop interesting features and logic, while the backend and device manipulation remains very simple. It supports graphics rendering for multiple devices and also supports event handling of popular user interface designing...

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