Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

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 (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Standard Julia capabilities


Similar to functions that are used in other languages, Julia can perform most of the rudimentary statistics on your data by using the describe function, as shown in the example script that follows:

using RDatasets 
describe(dataset("datasets", "iris"))

This script accesses the iris dataset and displays summary statistics on the dataset.

If we were to build a Notebook to show describe in use against the iris dataset (which we loaded in the previous example), we would end up with a display such as this: 

You can see the standard statistics that have been generated for each of the variables in the dataset. I thought it was interesting that the count and percentage of NA values in the dataset are provided. I have found that I usually have to double-check to exclude this data by using other languages. This is a quick, built-in reminder.

Note

The warning message is complaining about a compatibility issue with one of the datetime libraries used, even though it is not used...