Jupyter is an open-source project that provides an interactive development environment that has become popular with Data Scientists, Data Engineers, software developers, and analysts. Jupyter runs special documents (called notebooks) which integrate code, documentation, equations, and other information into a single page. Such notebooks -- because they can be used to fully describe all of the steps of an analysis or workflow along with code, visualizations, and data -- have become the defacto standard for Data Science and Engineering development.
JupyterLab is the current iteration of the Jupyter interface. It provides a robust IDE which allows users to work with multiple notebooks simultaneously, view the results and associated data, run supporting commands in programming terminals, or access the shell of the environment where the IDE is running. The environment is configurable, which allows for users to have a custom workspace that is flexible to their development needs.
This tutorial introduces Jupyter, its features, and some of the types of tasks that it allows.