altair-viz/altair
Altair
Altair is a declarative data visualization library for Python based on the Vega-Lite grammar. It allows users to create statistical visualizations by mapping data fields to visual properties rather than writing imperative drawing code.
The library focuses on interactive charting through a system of linked selections and filters that update multiple visualizations based on user input. It renders charts as JSON and HTML for display in web browsers and interactive notebooks.
The project covers statistical data analysis and interactive data exploration, providing capabilities to export visuals as standalone HTML, JSON, or image files. These assets can execute directly within a web runtime to render charts without requiring a Python backend.
Features
- Declarative Visualization Languages - Provides a declarative system for creating charts and graphs in Python by defining visual specifications.
- Python Visualization - Implements a declarative Python visualization library based on the Vega-Lite grammar for statistical plotting.
- Interactive Data Charting - Provides an interactive charting library for building linked selections and filters in Python.
- Interactive Data Exploration Tools - Builds linked charts and filters that allow users to explore datasets by selecting and highlighting data points.
- Interactive Visualization Rendering - Supports linked selections and filters that automatically update multiple charts based on user input.
- Declarative Visualization Grammars - Implements a declarative grammar that maps data fields to visual properties instead of using imperative drawing commands.
- Statistical Charting Suites - Maps data fields to visual properties to generate charts that reveal patterns and trends in statistical datasets.
- Linked Selection Models - Defines interactive filters and linked views by specifying how user inputs map to data subsets.
- Browser-Based Execution Environments - Executes visualization code directly within a web runtime to render charts without requiring a backend server.
- Notebook Environments - Renders charts directly within interactive computing environments for rapid prototyping and inline data reporting.
- Notebook Rendering Utilities - Renders visualizations natively within interactive notebook interfaces and integrated development environments.
- Visualization Exporters - Provides capabilities to export charts as standalone HTML, JSON, or image files for independent rendering.
- Visualization Exporters - Enables saving charts as standalone HTML pages, JSON files, or common image formats for distribution.
- Browser-Based Charting Libraries - Exports visuals as HTML or JSON to display interactive charts in web browsers without requiring a Python backend.
- Client-Side Rendering Runtimes - Delegates the actual drawing of pixels to a web-based runtime that interprets JSON specifications.
- Data Analysis and Processing - Declarative statistical visualization.
- Data Visualization - Declarative statistical visualization library based on Vega-Lite.
Star history
How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
