Jupyter

Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text. It provides an interactive computing environment where users can combine executable code, rich text, mathematics, plots, and multimedia resources all in a single document. Jupyter Notebook supports over 40 programming languages, including Python, R, Julia, and Scala, making it versatile for various data science and computational tasks.

Jupyter Notebook and its more advanced counterpart, JupyterLab, are primarily designed for data scientists, researchers, educators, and students. These tools are particularly useful for those working in fields such as data analysis, machine learning, scientific computing, and computational journalism. They allow users to create and share documents that contain live code, equations, visualizations, and explanatory text, making it easier to collaborate and communicate complex ideas and analyses.

In practice, Jupyter Notebooks are most often used for exploratory data analysis, data cleaning and transformation, statistical modeling, machine learning, and data visualization. They excel at "showing the work" that data teams have done through a combination of code, markdown, links, and images. Notebooks can be run cell by cell, allowing users to better understand what the code does and iterate on their analyses. Additionally, Jupyter Notebooks can be converted to various output formats like HTML, PDF, and slideshows, facilitating easy sharing of results and insights.

Jupyter Notebook was developed as part of Project Jupyter, a non-profit, open-source project that evolved from the IPython project. The project aims to develop open-source software, open standards, and services for interactive computing across dozens of programming languages. While Jupyter Notebook provides a classic interface, JupyterLab offers a more flexible and powerful environment, allowing users to work with notebooks, text editors, terminals, and custom components in a single interface. Both tools continue to be widely adopted in academia, research institutions, and industries where data analysis and scientific computing play crucial roles.