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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

Securing a notebook


Securing a notebook can be accomplished by several methods such as:

  • Manage authorization
  • Securing notebook content

Managing notebook authorization

A notebook can be secured to use username/password authorization. Authorization is on by default in your notebook. Under Jupyter it is token/password instead of username/password as a token is more open to interpretation. See Jupyter documentation on implementing authorization as this has changed slightly over time.

Securing notebook content

A notebook has possible security issues with several parts of standard content that are secured automatically by Jupyter:

  • Untrusted HTML is sanitized
  • Untrusted JavaScript is not executed
  • HTML and JavaScript in markdown cells is not trusted
  • Notebook output is not trusted
  • Other HTML or JavaScript in the notebook is not trusted

Where trust comes down to the question: Did the user do this or did the Jupyter script? Untrusted means it will not be generated.

Sanitized code is wrapped to force the values to...

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