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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

Versioning a notebook


A common practice in the programming world is to maintain a history of the changes made to a program. Over time the different versions of the program are maintained in a software repository where the programmer can retrieve prior versions to return to an older, working state of their program.

In the previous section we mentioned placing your notebook on GitHub. Git is a software repository in wide use. GitHub is an internet-based instance of Git. Once you have any software in Git it will automatically be versioned. The next time you update your notebook in GitHub. Git will take the current instance, store it as a version in your history, and place the new instance as the current—where anyone accessing your GitHub repository will see the latest version by default.

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