{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jupyter-scatter-interactive-exploration-of","title":"Jupyter Scatter: Interactive Exploration of Large-Scale Datasets","arxiv_id":"2406.14397","date":"2024-06-20","proceeding":null,"authors":["Fritz Lekschas","Trevor Manz"],"abstract":"Jupyter Scatter is a scalable, interactive, and interlinked scatterplot widget for exploring datasets in Jupyter Notebook/Lab, Colab, and VS Code. Its goal is to simplify the visual exploration, analysis, and comparison of large-scale bivariate datasets. Jupyter Scatter can render up to twenty million points, supports fast point selections, integrates with Pandas DataFrame and Matplotlib, uses perceptually-effective default settings, and offers a user-friendly API.","url_abs":"https://arxiv.org/abs/2406.14397v2","url_pdf":"https://arxiv.org/pdf/2406.14397v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"jupyter-scatter-interactive-exploration-of","repo_url":"https://github.com/flekschas/jupyter-scatter","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}