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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

Publishing a notebook


You can publish a notebook/dashboard using markdown. Markdown involves adding annotations to cells in your notebook that are interpreted by Jupyter and converted into the more standard HTML representations that you see in other published materials.

In all cases, we create the cell with the markdown type. We then enter the syntax for markdown in the cell. Once we run markdown cells the display of the cell changes to the effective markdown representation. You should also note there is no line number designation for markdown cells, as there is no code executing in markdown cells.

Font markdown

You can adjust font style information using italic and bold HTML notations. For example, if we have the code format of a cell as follows. You can use markdown that has markdown tags for italics (<i>) and bold (<b>):

When we run the cell we see the effective markdown as:

List markdown

We can use lists such as the following, where we start an un-numbered list (we could have used...

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