A curated list of awesome Jupyter projects, libraries, and resources for data science and interactive computing.
Awesome Jupyter is a curated GitHub repository that aggregates and categorizes a wide array of projects, libraries, and resources related to the Jupyter ecosystem. It helps data scientists, researchers, and developers discover tools for extending Jupyter notebooks, JupyterLab, and JupyterHub for tasks like visualization, collaboration, publishing, and education.
Data scientists, researchers, educators, and developers who use Jupyter for interactive computing and want to enhance their workflow with extensions, libraries, and best practices.
It saves time by providing a single, well-organized source for discovering high-quality Jupyter tools, avoiding the need to search scattered resources, and is maintained by the community to ensure relevance and coverage.
A curated list of awesome Jupyter projects, libraries and resources
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Organizes hundreds of Jupyter projects into clear sections like 'Runtimes/Frontends', 'Visualization', and 'JupyterLab Extensions', making it easy to browse specific tool types without scattered searches.
Accepts contributions via GitHub pull requests, as noted in the 'Contributing' section, helping keep the list updated with emerging tools and preventing staleness.
Links to tutorials, talks, and documentation under 'Articles/Guides/Tutorials' and 'Community Resources', offering learning aids beyond mere tool listings.
Spans from core runtimes like JupyterLab to domain-specific projects (e.g., geospatial analysis) and hosted solutions, serving as a one-stop hub for Jupyter enhancements.
The list catalogs tools without ratings, reviews, or usage metrics, forcing users to independently vet each project's stability and community support.
It merely provides links without installation help, compatibility checks, or integration guidance; users must navigate to external repos for practical details.
With dozens of entries per category, the sheer volume can be intimidating without curated recommendations or beginner-friendly filters.