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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 2. Data Munging FREE CHAPTER 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

Using REPL

Read-Eval-Print-Loop is an interactive shell or the language shell that provides the functionality to test out pieces of code. Julia provides an interactive shell with a Just-in-Time compiler at the backend. We can give inputs in a line, it is compiled and evaluated, and the result is given in the next line.

Using REPL

The benefit of using the REPL is that we can test out our code for possible errors. Also, it is a good environment for beginners. We can type in the expressions and press Enter to evaluate.

A Julia library, or custom-written Julia program, can be included in the REPL using include. For example, I have a file called hello.jl, which I will include in the REPL by doing the following:

julia> include ("hello.jl") 

Julia also stores all the commands written in the REPL in the .julia_history. This file is located at /home/$USER on Ubuntu, C:\Users\username on Windows, or ~/.julia_history on OS X.

As with a Linux Terminal, we can reverse-search using Ctrl + R in Julia's shell. This is a really nice feature as we can go back in the history of typed commands.

Typing ? in the language shell will change the prompt to:

help?>  

Using REPL

To clear the screen, press Ctrl + L. To come out of the REPL press Ctrl + D or type the following:

julia> exit().

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