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Learning Jupyter 5

You're reading from   Learning Jupyter 5 Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab

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Product type Paperback
Published in Aug 2018
Publisher
ISBN-13 9781789137408
Length 282 pages
Edition 2nd Edition
Languages
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Jupyter FREE CHAPTER 2. Jupyter Python Scripting 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter Java Coding 6. Jupyter JavaScript Coding 7. Jupyter Scala 8. Jupyter and Big Data 9. Interactive Widgets 10. Sharing and Converting Jupyter Notebooks 11. Multiuser Jupyter Notebooks 12. What's Next? 13. Other Books You May Enjoy

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...

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