vega/altair
Altair
Altair is a declarative data visualization library for Python that generates Vega-Lite specifications. It functions as a tool for mapping data to graphical marks using a high-level syntax, allowing users to describe the desired visual outcome instead of writing imperative drawing commands.
The framework enables the creation of interactive charts and graphics, including linked views and filtered displays that respond to user input in real time. It supports the design of multi-view dashboards by combining visualizations into layered or faceted layouts.
The library provides capabilities for statistical graphics production, data transformation for analysis, and the ability to export visualizations to various image and web formats. It renders natively within notebooks and integrated development environments to support iterative data analysis.
Features
- Declarative Visualization Grammars - Uses a formal declarative grammar to map data fields to visual properties like position and color.
- Vega-Lite Specifications - Acts as a Python interface for creating statistical visualizations by generating compatible Vega-Lite specifications.
- Data Exploration - Enables interactive browsing and filtering of data through linked views to identify patterns in real time.
- Declarative Visualization Languages - Provides a declarative language for defining visual charts and graphs through configuration rather than imperative code.
- Visualization Specification Generation - Translates Python class structures into standardized JSON specifications that describe a visualization's visual and data mappings.
- Interactive Data Charting - Enables the creation of interactive charts with linked views and filtered displays that respond to user input.
- Notebook Rendering Utilities - Serializes visual specifications into HTML and JavaScript for interactive rendering directly within notebook cells.
- Multi-View Layouts - Combines multiple visualizations into layered or faceted layouts to show different dimensions of a single dataset.
- View Layering & Faceting - Implements layering and faceting logic to combine multiple independent chart specifications into a single multi-view display.
- Statistical Graphics Production - Maps complex datasets to visual marks with automatic scaling and axes for scientific or business reporting.
- Declarative Statistical Plotting - Maps data to graphical marks using a declarative syntax to produce statistical charts with automatic axes and scales.
- Data Reshaping - Performs data reshaping, including aggregation and binning, specifically for visualization purposes via query expressions.
- Code Editors & IDEs - Integrates with code editors and IDEs to display visualizations natively during the development process.
- In-Specification Transformations - Defines data aggregations and filters within the visualization specification for the rendering engine to execute.
- Notebook Integrations - Renders visualizations natively within Jupyter and other integrated development environments to support iterative data analysis.
- Client Side Rendering - Delegates the rendering of generated visualization specifications to the browser's JavaScript engine.
- Dashboard Charting Tools - Provides tools for configuring visual representations and layouts used in multi-view analytical dashboards.
- Interactive Graphics - Implements selection components that allow users to manipulate views and filter data in real time.
- Data Visualization - Declarative statistical visualization library based on Vega-Lite.
- Visualization and Analysis - Declarative statistical visualization library for data analysis.
- Data Visualization - Declarative statistical visualization library for Python.
- Visualization - Declarative statistical visualization library.
Star history
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