While many more detailed introductions to Juypter Notebooks exist, this is just a breif introduction geared towards someone who would run analyses from an exsisting Juypter Notebook rather than start from scratch. For example, you maybe a wet lab scientist who is colaborating with a dry lab scientist.
If you plan to write your own Jupyter Notebooks or would like to take a deeper look here are some helpful resources:
Real Python's Jupyter Notebook: An Introduction
Data Quest's How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial
A Jupyter Notebook is a robust tool yet user friendly tool to get started with if you are new to data science. It is a blend between an integrated development environment(IDE) and an electronic notebook.
According to their documentation:
The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. The Jupyter notebook combines two components:
A web application: a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output.
Notebook documents: a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects.
As a browser based tool they are accessible. The notebook structure makes analyses approcable, in a step by step fashion and the results are easily repoducible.
Jupyter notebooks especially helpful in the following scenarios:
- Showing code to other people
- Running an analysis and show the output in the same place
- Repeating the same analyses with different input the files
- During live demos