What Is a Computational Notebook in JupyterLab?

A computational notebook is a shareable document that combines computer code, plain-language descriptions, data, and rich visualizations in one place. In JupyterLab, that document becomes an interactive workspace: you can write and run code, see the output immediately, and mix it with explanatory text, charts, 3D models, and interactive controls. This format is useful whenever you need to prototype code, explore and visualize data, or explain an idea to someone else without switching between separate tools.

What a computational notebook contains

According to the JupyterLab documentation, a notebook can hold several kinds of content side by side:

• Computer code that you can execute
• Plain language descriptions that explain what the code does
• Data used by the code
• Rich visualizations, including 3D models, charts, graphs, and figures
• Interactive controls that let you adjust what you see

Because these elements live in the same document, the notebook works as both a working environment and a record of what you did.

What JupyterLab adds

JupyterLab is the editing environment that opens and runs these notebooks. The documentation describes it as a highly extensible, feature-rich notebook authoring application and editing environment, part of Project Jupyter. Within that project, JupyterLab sits alongside other notebook authoring applications such as Jupyter Notebook and Jupyter Desktop.

The documentation notes that JupyterLab offers a more advanced, feature-rich, and customizable experience compared to Jupyter Notebook. So the notebook is the document format, and JupyterLab is one of the interfaces you can use to work with it.

What you can do with one

The documentation frames the notebook-plus-editor combination as a fast interactive environment for:

• Prototyping and explaining code — write a piece of code, run it, and describe it in the same file
• Exploring and visualizing data — load data and produce charts or figures next to the code that generated them
• Sharing ideas with others — hand over a single document that contains the code, the explanation, and the results

Where to go next

If you want to try it without installing anything, the documentation points to running JupyterLab on Binder. For learning the interface and day-to-day tasks, the documentation's main sections include Get Started, User Guide, and the JupyterLab Interface, with further topics such as Notebooks, Working with Files, and Extensions.