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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment FREE CHAPTER 2. Data Munging 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Pyplot for Julia


This package was made by Steven G. Johnson and provides Python's famous matplotlib library to Julia. If you have used matplotlib, you will be familiar with its pyplot module.

We learned about the Julia's Pycall package in the first chapter, and PyPlot makes use of the same package to make the call to the matplotlib plotting library directly from Julia. This call has very less (or no) overhead, and arrays are passed directly without making a copy.

Multimedia I/O

Only plaintext display is provided by the base Julia runtime. By loading external modules or by using graphical environments such as Jupyter notebooks, rich multimedia output can be given. Julia has a standardized mechanism to display the rich multimedia outputs (images, audio, and video). This is provided by the following:

  • display(x) is the richest multimedia display of the Julia object

  • Arbitrary multimedia representations are done by overloading the writemime of user-defined types

  • By subclassing a generic display type...

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