Altair
What it is: Declarative visualization in Python using Vega-Lite. Describe what you want, not how to draw it.
What It Does Best
Simple, consistent API. Chart specification as JSON-like Python objects. Same pattern for every chart type.
Interactive by default. Tooltips, zooming, panning built-in. No extra code.
Reproducible. Declarative specs mean same code = same output. Easy to share and version control.
Key Features
Declarative syntax: Describe what you want, not how to draw it
Vega-Lite grammar: Built on powerful visualization grammar
Automatic interactivity: Pan, zoom, tooltips without configuration
Data transformations: Filter, aggregate, bin data in visualization spec
Multi-view composition: Layer, concatenate, repeat charts easily
Pricing
Free. Open source, BSD license.
When to Use It
✅ Want simple, clean syntax
✅ Need quick exploratory plots
✅ Jupyter notebooks and data science workflows
✅ Prefer declarative over imperative approach
✅ Need interactive charts with minimal code
When NOT to Use It
❌ Extremely custom visualizations (use D3.js)
❌ Very large datasets (performance limits)
❌ Need publication-quality static images (Matplotlib better)
❌ Require real-time streaming visualizations
❌ Complex 3D visualizations needed
Common Use Cases
Exploratory data analysis: Quick interactive charts in Jupyter notebooks
Statistical visualizations: Distribution plots, correlation matrices, regression lines
Time series analysis: Line charts with interactive zoom and pan
Dashboard prototyping: Fast iteration on chart designs
Data science reporting: Reproducible visualizations for analysis reports
Altair vs Alternatives
vs Matplotlib: Altair simpler syntax, interactive by default; Matplotlib more control, better for print
vs Plotly: Altair cleaner API, lighter weight; Plotly more chart types, better dashboards
vs Seaborn: Altair more flexible, interactive; Seaborn better statistical defaults
Unique Strengths
Declarative clarity: Most readable Python visualization code
Vega ecosystem: Access to entire Vega-Lite grammar and tooling
Minimal code: Complex charts in just a few lines
JSON serialization: Save chart specs, share across languages
Bottom line: Matplotlib made declarative. Perfect middle ground between simplicity and interactivity. Great for data scientists who want clean code.