What is a Jupyter Notebook and Why is it Useful?

Jupyter Notebooks are locally run web applications which contain live code, equations, figures, interactive apps, and Markdown text. The standard language is Python, and that's what we'll be using for this book; however, note that a variety of alternatives are supported. This includes the other dominant data science language, R:

Those familiar with R will know about R Markdown. Markdown documents allow for Markdown-formatted text to be combined with executable code. Markdown is a simple language used for styling text on the web. For example, most GitHub repositories have a README.md Markdown file. This format is useful for basic text formatting. It's comparable to HTML but allows for much less customization.

Commonly used symbols in Markdown include hashes (#) to make text into a heading, square and round brackets to insert hyperlinks, and stars to create italicized or bold text:

Having seen the basics of Markdown, let's come back to R Markdown, where Markdown text can be written alongside executable code. Jupyter Notebooks offer the equivalent functionality for Python, although, as we'll see, they function quite differently than R Markdown documents. For example, R Markdown assumes you are writing Markdown unless otherwise specified, whereas Jupyter Notebooks assume you are inputting code. This makes it more appealing to use Jupyter Notebooks for rapid development and testing.

From a data science perspective, there are two primary types for a Jupyter Notebook depending on how they are used: lab-style and deliverable.

Lab-style Notebooks are meant to serve as the programming analog of research journals. These should contain all the work you've done to load, process, analyze, and model the data. The idea here is to document everything you've done for future reference, so it's usually not advisable to delete or alter previous lab-style Notebooks. It's also a good idea to accumulate multiple date-stamped versions of the Notebook as you progress through the analysis, in case you want to look back at previous states.

Deliverable Notebooks are intended to be presentable and should contain only select parts of the lab-style Notebooks. For example, this could be an interesting discovery to share with your colleagues, an in-depth report of your analysis for a manager, or a summary of the key findings for stakeholders.

In either case, an important concept is reproducibility. If you've been diligent in documenting your software versions, anyone receiving the reports will be able to rerun the Notebook and compute the same results as you did. In the scientific community, where reproducibility is becoming increasingly difficult, this is a breath of fresh air.