Project Jupyter
Open notebooks for code, data, text, and visual results.
Project Jupyter develops open-source tools for interactive computing, centered on notebooks that combine executable code, written explanation, equations, media, and outputs. Its ecosystem includes JupyterLab, the classic Notebook interface, JupyterHub for shared deployments, and Voilà for turning notebooks into web apps.
Why it stands out
- Uses an open JSON notebook format, making notebooks portable beyond one vendor or hosted service.
- Supports many programming languages through kernels rather than being tied to Python alone.
- Covers both individual work and institution-scale deployments through separate but connected subprojects.
- Designed around extensibility, with JupyterLab’s modular interface and a broader ecosystem of widgets and add-ons.
- Can sit close to organizational data and compute infrastructure instead of forcing work into a third-party SaaS notebook.
Good to know
- Running notebooks generally requires a local install, hosted environment, or institutional deployment rather than just visiting the project homepage.
- Language support depends on available kernels and environment setup, so not every language works out of the box.
- Large classroom or company use typically requires JupyterHub administration, authentication setup, and infrastructure planning.
- Notebook files can contain executable code, so shared notebooks should be treated with the same security caution as scripts.
Under the radar: If you only know the classic Notebook, try JupyterLab for multi-file projects—it lets you arrange notebooks, terminals, editors, data views, and outputs in one workspace.
Photos
Data scientists, researchers, and instructors who need reproducible documents that combine analysis, explanation, and results. Prototype machine learning or scientific workflows in Python, R, Julia, Scala, and other languages. Share computational reports through GitHub, email, Dropbox, or notebook viewers. Run a shared notebook environment for a classroom, lab, or company with centralized authentication. Publish notebook-based dashboards or apps without exposing the underlying notebook editor.
Web-based notebook interfaces; JupyterLab workspace for notebooks, code, and data; classic Jupyter Notebook document editor; JupyterHub for multi-user deployments; Voilà for notebook-to-app publishing; rich interactive outputs including HTML, images, video, LaTeX, and custom MIME types; support for big-data and data-science tools such as Spark, pandas, scikit-learn, ggplot2, and TensorFlow.
Technical Notes
- Jupyter Notebook files use an open JSON-based document format that records code, text, equations, and outputs.
- Jupyter kernels communicate through an open interactive computing protocol using JSON over ZMQ and WebSockets.
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