A curated list of awesome open-source data visualization libraries, frameworks, and resources across multiple programming languages.
Awesome Dataviz is a curated GitHub repository listing hundreds of open-source data visualization libraries, frameworks, and educational resources. It helps developers and data professionals find the right tools for creating charts, maps, graphs, and interactive visualizations across multiple programming languages and platforms.
Data scientists, developers, and designers who need to quickly discover and evaluate open-source visualization libraries for web, mobile, or desktop projects.
It saves hours of research by aggregating the best tools in one place, is community-maintained for quality, and focuses exclusively on open-source options to avoid vendor lock-in.
:chart_with_upwards_trend: A curated list of awesome data visualization 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.
Lists tools for JavaScript, Python, R, Go, C++, Ruby, Android, and iOS, as shown in the structured Contents section, making it a one-stop shop for cross-platform projects.
Includes books, podcasts, and websites beyond libraries, such as Tufte's classics and data journalism blogs, providing a holistic learning path for visualization theory.
Follows the 'awesome list' philosophy with clear contributing guidelines, ensuring ongoing updates and quality control through community submissions.
Emphasizes transparency by listing only open-source projects, avoiding vendor lock-in and promoting community-driven development, as stated in the Philosophy.
Merely lists tools without side-by-side feature comparisons, performance metrics, or suitability ratings, forcing users to conduct additional research for decision-making.
As a manually curated list, some libraries may become outdated or deprecated over time, with no automatic version tracking or update alerts mentioned in the README.
Focuses on cataloging resources without providing code samples, integration tips, or best practices, leaving beginners to seek external tutorials for actual usage.