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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
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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

Python data access in Jupyter

Now that we have seen how Python works in Jupyter, including the underlying encoding, how does Python access a large dataset of work in Jupyter?

I started another view for pandas, using Python data access as the name. From here, we will read in a large dataset and compute some standard statistics on the data. We are interested in seeing how we use pandas in Jupyter, how well the script performs, and what information is stored in the metadata (especially if it is a larger dataset).

Our script accesses the iris dataset that is built-in to one of the Python packages. All we are looking to do is read in a slightly large number of items and calculate some basic operations on the dataset. We are really interested to see how much of the data is cached in the .pynb file.

The Python code is as follows: 

# import the datasets package 
from sklearn import...
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