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Learning Jupyter 5

Learning Jupyter 5 - Second Edition

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Learning Jupyter 5

Learning Jupyter 5

2.5 (2)

Overview of this book

The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
Table of Contents (14 chapters)
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Basic Julia in Jupyter


In this example, we will use the Iris dataset for some standard analysis. So, start a new Julia Notebook and call it Julia Iris. We can enter a small script to see how the steps progress for a Julia script.

This script uses another package for plotting, which is called Gadfly. You will have to go through similar steps as to the ones we went through in the previous section to install the package before operating the script.

Enter the following script into separate cells of your Notebook:

using RDatasets
using DataFrames
using Gadfly
set_default_plot_size(5inch, 5inch/golden); plot(dataset("datasets","iris"), x="SepalWidth", y="SepalLength", color="Species") 

RDatasets is a library that contains several of the commonly used R datasets, such as iris. This is a simple script—we define the libraries that we are going to use, set the size of the plot area, and plot out the iris data points (color coded to Species).

 

So, you will end up with a starting screen that looks like the...

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