Jupyter
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| Jupyter | |
|---|---|
| Name | Jupyter |
| Developer | Fernando Pérez, Project Jupyter |
| Released | 2014 |
| Programming language | Python, Julia, R, JavaScript, TypeScript |
| Operating system | Linux, macOS, Microsoft Windows |
| License | BSD |
Jupyter
Jupyter is an open-source interactive computing platform that integrates IPython, Python, Julia, R and many other programming language kernels into executable documents. It provides a document format, kernel protocol, and multi-language ecosystem used across research, education, and industry, enabling literate programming with executable code, rich media, and narrative text. The project grew from efforts led by scientists and institutions to bridge reproducible research, data science, and computational notebooks.
History
The origins trace to IPython development by Fernando Pérez and collaborations with contributors from Lawrence Berkeley National Laboratory, NumFOCUS, and academic groups tied to University of California, Berkeley and Harvard University. Major milestones include splitting the original IPython notebook into a language-agnostic architecture influenced by work at MIT, formal launches at conferences such as SciPy, and adoption by projects like Binder and platforms from Microsoft, Google, and IBM. Funding and support came from organizations including Alfred P. Sloan Foundation, Gordon and Betty Moore Foundation, and Charles and Helen Schwab Foundation, while governance transitioned toward an open community model with ties to NumFOCUS and contributor networks from GitHub.
Architecture and Components
The architecture centers on a messaging protocol and document format interoperable across kernels developed for Python, Julia, R, Haskell, Scala and others. Core components include the notebook document format influenced by JSON, the kernel gateway and ZeroMQ-based kernel messaging used in server implementations, and storage/backends compatible with Amazon Web Services, Google Cloud Platform, and on-premise HPC systems like those at Argonne National Laboratory and Oak Ridge National Laboratory. Additional pieces comprise server processes used by integrations with JupyterHub for multi-user deployment, JupyterLab as the next-generation interface, and extensions that leverage WebSockets and HTTP for interactive widgets and outputs.
Interfaces and Clients
Multiple clients implement the protocol: classic notebook UIs developed from IPython, modern interfaces such as JupyterLab, embeddable viewers like nbviewer, and cloud-hosted services from Google Colaboratory, Microsoft Azure Notebooks, and Binder. IDE integrations exist for Visual Studio Code, PyCharm, and RStudio, while command-line tools and APIs enable programmatic access via nbconvert and integrations with Apache Airflow, Kubernetes, and Docker. Frontend widget frameworks link to projects including React, Vue.js, and D3.js to render interactive visualizations within documents.
Usage and Applications
Adoption spans academic research in fields represented by Stanford University, Massachusetts Institute of Technology, Princeton University, and ETH Zurich; industry teams at Netflix, Spotify, Google, Microsoft, and IBM; and public science initiatives like NASA and European Space Agency. Use cases include data analysis pipelines adopted by groups using pandas and NumPy, machine learning workflows with TensorFlow, PyTorch, and scikit-learn, reproducible publishing in venues associated with arXiv and PLOS, and computational teaching in courses at Coursera, edX, and university curricula. Notebooks support workflows for bioinformatics projects at Broad Institute, climate modeling at NOAA, and finance teams at firms like Goldman Sachs.
Ecosystem and Extensions
A rich ecosystem surrounds the platform: packaging and sharing tools like nbformat and nbgrader, interactive widget libraries such as ipywidgets, visualization integrations including Matplotlib, Plotly, and Bokeh, and distribution systems exemplified by Conda and pip. Community extensions and labs plugins provide features from real-time collaboration (leveraging work by Google and academic partners) to conversion pipelines connecting with LaTeX and Pandoc. Education-focused tooling has been developed with contributions from Harvard University, MIT, and organizations running MOOCs on edX and Coursera.
Development and Governance
Development is coordinated via open-source workflows on GitHub, with contributors from corporations like Microsoft, Google, IBM, and academic institutions including University of California, Berkeley and ETH Zurich. Governance emphasizes meritocratic contribution, steering by elected contributors and foundations such as NumFOCUS and collaborations with funding bodies including Sloan Foundation and Moore Foundation. Release processes, security advisories, and interoperability efforts engage standards bodies and community working groups, and documentation and training are maintained by volunteers and organizations offering workshops at conferences like SciPy, PyCon, and OpenCon.