Jupyter Notebooks let you combine code, comments, multimedia, and visualizations into an interactive document that can be shared, re-used, and re-worked.
Originally developed for data science applications written in Python, R, and Julia, Jupyter Notebooks are useful in all kinds of ways for all kinds of projects. You can use Jupyter Notebooks to share Python code and its output with third parties, to run code with live interactive feedback, or to systematically track and document the progress of your work.
In this article, we’ll walk through setting up Jupyter Notebook for Python, working with Jupyter’s various features, and sharing the results with others, whether they have Jupyter installed or not.
Jupyter Notebook installation and setup
The easiest way to create and work with Jupyter Notebooks for Python is to set up an instance of the Anaconda distribution of Python. Anaconda was created to make it easy to work with Python and its galaxy of data science tools, and it includes the Jupyter Notebook software as a standard-issue pack-in.