orange3

Orange is an open-source data mining and visualization toolbox designed for both novices and experts. Its central idea is that workflow-based data science tools can democratize data science by hiding complex underlying mechanics and exposing intuitive concepts, so users need no programming or in-depth mathematical knowledge to explore data. The project provides a visual canvas where analysis is built as a workflow of connected widgets. On Windows and macOS, users can download a standalone installer; add-ons are managed from the Options -> Add-ons menu. On Linux or for development, installation is supported via conda, pip, or uv, and winget is available on Windows. The README documents conda environment creation, pip installation with PyQt6 and PyQt6-WebEngine, uv tool installation, and running the application with `python -m Orange.canvas` or `orange-canvas`. Development is split across three core repositories: orange-canvas-core implements the canvas, orange-widget-base provides the widget GUI library, and orange3 brings them together with the base data mining toolbox. Add-ons extend functionality for specific use cases, and anyone can write one using the example add-on template. First-party add-ons listed include text, bioinformatics, timeseries, single-cell, image analytics, educational, geo, associate, network, and explain. The README also covers contributor setup: forking repositories, creating a conda environment, cloning forks, installing PyQt requirements, and installing packages in editable mode. It notes that orange-widget-base and orange-canvas-core should be installed before orange3 when developing all components. Running options include skipping the splash screen and welcome window, increasing debug output, clearing widget settings, and selecting a dark style. Tests can be run with unittest. Community support is available via Discord, and documentation is hosted on Read the Docs.