Book Image

Julia 1.0 Programming Cookbook

By : Bogumił Kamiński, Przemysław Szufel
Book Image

Julia 1.0 Programming Cookbook

By: Bogumił Kamiński, Przemysław Szufel

Overview of this book

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia. Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia. By the end of the book, you will have acquired the skills to work more effectively with your data
Table of Contents (18 chapters)
Title Page
Copyright and Credits
About Packt

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...