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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st 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 (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Caching your notebook


Caching is a common programming practice to speed up performance. If the computer does not have to reload a section of code or variable or file, but can just access directly from a cache this will improve performance.

There is a mechanism to cache your notebook if you are deploying into a Docker space. Docker is a mechanism for virtualizing code over many instances in one machine. It has become common practice to do so in the Java programming world. Luckily, Docker is very flexible and a method has been determined to use Jupyter in Docker as well. Once in Docker, it is a minor adjustment to automatically cache your pages in Docker. The underlying tool used is memcached, yet another widespread common tool for caching anything, in this case Jupyter Notebooks.

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