A Brief History of Jupyter Notebooks

William Horton

Data Science Ipython Jupyter Open-Source Scientific Libraries (Numpy/Pandas/SciKit/...)

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The Jupyter Notebook: many Python users love it, many other Python users love to hate it. But where did it come from? How did we come to have a tool that combines code execution, visualization, Markdown, and more? In this talk, we will dive into the development of the Jupyter Notebook and the older ideas that it built upon.

To start, we will look at tools that popularized the “computational notebook” interface. In 1988, Mathematica introduced this interface to the scientific community. In the 90s, tools like Maple competed with Mathematica to provide the best scientific programming environment. The early 2000s saw the rise in popularity of open-source scientific tools in Python, including IPython, leading to IPython Notebook and then Jupyter.

Turning to the present, we look at the expanding ecosystem beyond the Notebook. JupyterLab provides a richer programming environment. Voilà and Binder give users better options for sharing their notebooks. And increased language support has led to Jupyter being a tool not only for Julia, Python, and R, but for dozens of other languages.

Finally: what is still to come? JupyerLab 2.0 promises even greater IDE-like capabilities, while IDEs increase their own Notebook support. Projects like Deepnote and CoCalc promise real-time collaboration on top of the Notebook interface. And the frustrations of working with Git are the source of a growing number of possible solutions. These efforts point us toward what the Jupyter Notebook could become.

Type: Talk (30 mins); Python level: Beginner; Domain level: Beginner

William Horton


William Horton is a Senior Software Engineer on the AI Services team at Compass, a tech-powered real estate brokerage. He works on applying machine learning to improve real estate agents' productivity for tasks like contact organization and outreach, and to streamline the home buying process with easier inventory discovery and personalized recommendations.