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Julia 1.0 Programming Cookbook

You're reading from Julia 1.0 Programming Cookbook Over 100 numerical and distributed computing recipes for your daily data science work?ow

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781788998369

Length 460 pages

Edition 1st Edition

Languages

Julia

Tools

Boost

Concepts

Data Science

Authors (2):

Przemysław Szufel

Bogumił Kamiński

View More author details

Table of Contents (12) Chapters

Preface

1. Installing and Setting Up Julia FREE CHAPTER

2. Data Structures and Algorithms

3. Data Engineering in Julia

4. Numerical Computing with Julia

5. Variables, Types, and Functions

6. Metaprogramming and Advanced Typing

7. Handling Analytical Data

8. Julia Workflow

9. Data Science

10. Distributed Computing

11. Other Books You May Enjoy

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Calling Python from Julia

Python is a popular general-purpose programming language. From a Julia programmer's point of view, the main advantage of Python is having a large set of available libraries that can be seamlessly called and used within Julia.

In this recipe, we will use Python'sscrapypackage for parsing XML data.

Getting ready

In order to use Python from Julia, you should install and configure thePyCall.jlpackage.PyCall can be configured in one of two modes:

Using Python Anaconda, which is automatically installed within Julia
Using an external Python installation (for example, a separately installed Python Anaconda)

In this recipe, we use the second option (that is, using external Python), but we also provide comments for the built-in Julia Anaconda. Using a version of Anaconda that is separate from Julia makes it possible to use several Anaconda installations (though just one at a time) with a single Julia installation.

We assume that you have installed and configured Python Anaconda...

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Authors (2)

Kamiński

Kamiński

Bogumi Kamiski (GitHub username: bkamins) is an associate professor and head of the Decision Support and Analysis Unit at the SGH Warsaw School of Economics, as well as adjunct professor at the data science laboratory, Ryerson University, Toronto. He is coeditor of the Central European Journal of Economic Modeling and Econometrics, and of the Multiple Criteria Decision Making journal. His scientific interests center on operational research and computational social science. He has authored over 50 research articles on simulation, optimization, and prediction methods. He also has 15+ years' experience in the deployment of large-scale advanced analytics solutions for industry and public administration.

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Szufel

Szufel

Przemysaw Szufel (GitHub username: pszufe) is an assistant professor in the Decision Support and Analysis Unit at the SGH Warsaw School of Economics. His current research focuses on distributed systems and methods for the execution of large-scale simulations for numerical experiments and optimization. He is working on asynchronous algorithms for the parallel execution of large-scale computations in the cloud and distributed computational environments. He has authored, and co-authored, several open source tools for high-performance and numerical simulation.

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