Data notebooks are a powerful tool for data analysis and exploration. They allow users to run code, examine the output, and iterate on their work in a single document. Notebooks have become an integral part of data workflows at companies like Netflix, Lyft, PayPal, and Bloomberg. The key features of notebooks include: 1. Running code: Users can write and execute Python code within the notebook interface, using all the same libraries as in a regular Python environment. 2. Exploring data: Notebooks allow users to run lines of code in isolated cells, making it easier to see the output of just those lines and iterate on their analysis. 3. Presenting results: Users can incorporate visuals from the data outputs directly into the notebook document, alongside text and other images, to present a full story. Notebooks are designed for an iterative process of running code, exploring data, and presenting results. They enable users to take everything they do in a data exploratory pipeline and bundle it into a single document: the notebook. Notebooks can be used with various programming languages by changing the kernel, making them versatile tools for data analysis.