{"categories":[{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"}],"enrichment":{"capability":"Plots area-weighted two- and three-circle Venn diagrams using Matplotlib, with optional support for cost-based layout optimization via Shapely.","skillfed_tags":["set-visualization","matplotlib-extension"],"use_cases":["Visualize overlaps between two or three datasets (e.g., genes in different experiments, users across platforms)","Compare membership of sets in scientific or statistical analysis to show intersection sizes at a glance","Generate publication-ready Venn diagrams with customizable colors, labels, and styling via Matplotlib","Explore set relationships interactively by tuning layout algorithms and cost functions for better visual clarity","Embed Venn diagrams in multi-panel figures or dashboards using Matplotlib's subplot and axes API"],"what_it_does":"matplotlib-venn provides four main functions\u2014venn2, venn2_circles, venn3, and venn3_circles\u2014that render two- and three-circle Venn diagrams with region areas proportional to subset sizes. You pass either a tuple of subset counts or a tuple of set objects, and the package draws colored patches with text labels showing the overlaps. The functions return objects that expose the underlying Matplotlib patches and text elements for further customization.\n\nThe package includes two layout algorithms: a default pairwise algorithm that balances the representation of individual set sizes and pairwise intersections, and an optional cost-based algorithm (requiring Shapely) that optimizes circle placement against a user-provided cost function. The description notes that exact area correspondence is not always achievable for three-circle diagrams, and provides tuning options via fixed_subset_sizes and alternative layout algorithms.","worth_installing":"Yes, if you already have Matplotlib, NumPy, and SciPy in your environment and need to visualize two- or three-way set overlaps. The package is actively maintained, has no known vulnerabilities, and is widely used (top 15000 on PyPI). High install friction is a minor concern only if you lack the core scientific stack; the MIT license poses no restrictions."},"id":"matplotlib-venn","links":{"html":"https://skillfed.io/packages/matplotlib-venn","md":"https://skillfed.io/packages/matplotlib-venn.md","pypi":"https://pypi.org/project/matplotlib-venn/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-02-25","license_spdx":null,"license_treatment":"permissive","name":"matplotlib-venn","python_support":"unspecified","summary":"Functions for plotting area-proportional two- and three-way Venn diagrams in matplotlib."},"popularity":{"monthly_downloads":285813,"position":8053,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.1.2"}