Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
- machine-learning
- models
- ui
- ui-components
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Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
Open source, privacy-first web analytics. Lightweight, cookie-free Google Analytics alternative. Self-hosted or cloud.
Performant financial charts built with HTML5 canvas
📈 A small, fast chart for time series, lines, areas, ohlc & bars
Agentic BI. Analytics at the speed of code ⚡️
Create delightful software with Jupyter Notebooks
Lightning ⚡️ fast forecasting with statistical and econometric models.
Translated📈Lightweight k-line chart that can be highly customized. Zero dependencies. Support mobile.
Go package to make lightweight ASCII line graph ╭┈╯ in command line apps with no other dependencies.
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
A library for debugging/inspecting machine learning classifiers and explaining their predictions
Lightweight analytics abstraction layer for tracking page views, custom events, & identifying visitors
Lightweight and extensible compatibility layer between dataframe libraries!
XChart is a light-weight Java library for plotting data.
Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models
Scalable machine 🤖 learning for time series forecasting.
skimpy is a light weight tool that provides summary statistics about variables in data frames within the console.
A web-based, lighter version of Gephi
Easily bring your Python applications online with the world's lightest GUI toolkit.
Svelte Gantt chart component – lightweight, customizable, interactive, and fast.
liwan - Lightweight Analytics
A lightweight component abstraction for D3.js.
A lightweight charting tool
A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.