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Jupyter Books lets you build an online book using a collection of Jupyter Notebooks and Markdown files. Its output is similar to the excellent Bookdown tool, and adds extra functionality for people running a Jupyter stack.
For an example of a book built with Jupyter Books, see the textbook for Data 100 at UC Berkeley (or this website!)
Here are a few features of Jupyter Books
Check out other features in the Features section.
Note: Throughout this guide you may see an experimental tag that looks like this:
✨experimental✨
These are features that we are playing around with, but might change in how they behave or are controlled. Please use them and give feedback, but note that they may be a bit unstable!
This documentation is for the master branch of Jupyter Book, which means that it might be slightly out-of-date with the latest version released on pip.
To get started, you may be interested in the following links. Here are a few links of interest:
Jupyter Book features is a quick demo and overview of Jupyter Books.
The Jupyter Book Guide will step you through the process of configuring and building your own Jupyter Book.
To install the Jupyter Book command-line interface (CLI), use pip
!
pip install jupyter-book
alternatively, if you'd like to install the latest version from GitHub, you can run:
pip install git+https://github.com/jupyter/jupyter-book
This will install the master branch of Jupyter Book (what these docs are built from), though it might be a bit less-stable.
Once you've installed the CLI, create a new book using the demo book content (the website that you're viewing now) with this command:
jupyter-book create mybookname --demo
Now, build the markdown that Jekyll will use for your book. Run this command:
jupyter-book build mybookname
You can now either push your book to GitHub and serve the demo with gh-pages, or modify the book with your own content.
Jupyter Books was originally created by Sam Lau and Chris Holdgraf with support of the UC Berkeley Data Science Education Program and the Berkeley Institute for Data Science.