Interactive chart visualization extension for DuckDB - bringing pandas + matplotlib style data visualization directly to SQL, without leaving your database.

"Replace pandas + matplotlib with just DuckDB"

No more switching between pandas and matplotlib for data visualization.

• 🔄 Complete C++-only rewrite - Removed Rust dependency for better compatibility
• 🌐 Browser-based rendering - Charts open in your default browser
• 📦 Lightweight build - Uses Plotly.js CDN (no large embedded libraries)
• 🎨 Interactive features - Zoom, pan, hover tooltips, export to PNG
• 🚀 Worker thread compatible - Works in DuckDB Community Extensions
• ⚡ Single binary - No external dependencies at runtime

• 📊 Multiple chart types: Bar, Line, Scatter, and Area charts
• 🖥️ Browser-based rendering: Charts open in your default web browser
• 🚀 Simple SQL interface: Visualize data directly from SQL queries
• 💻 Cross-platform: Works on macOS, Linux, and Windows
• 🌐 Internet required: Initial chart rendering requires internet connection (Plotly.js loads from CDN, then cached by browser)
• ✨ Interactive: Zoom, pan, hover, and export capabilities

INSTALL miniplot FROM community;
LOAD miniplot;

That's it! No additional setup required.

SELECT bar_chart(
    ['Q1', 'Q2', 'Q3', 'Q4'],
    [100.0, 150.0, 200.0, 180.0],
    'Quarterly Sales'
);

SELECT line_chart(
    ['Jan', 'Feb', 'Mar', 'Apr', 'May'],
    [5.2, 7.1, 12.5, 15.8, 20.3],
    'Monthly Temperature'
);

SELECT scatter_chart(
    [1.0, 2.0, 3.0, 4.0, 5.0],
    [2.5, 5.0, 7.5, 10.0, 12.5],
    'Correlation Analysis'
);

SELECT area_chart(
    ['Week1', 'Week2', 'Week3', 'Week4'],
    [1000.0, 1500.0, 1300.0, 1800.0],
    'Weekly Revenue'
);

-- Create sample data
CREATE TABLE sales AS
SELECT 'Mon' as day, 100.0 as amount UNION ALL
SELECT 'Tue', 150.0 UNION ALL
SELECT 'Wed', 120.0 UNION ALL
SELECT 'Thu', 180.0 UNION ALL
SELECT 'Fri', 140.0;

-- Visualize directly from table
SELECT bar_chart(
    list(day ORDER BY day),
    list(amount ORDER BY day),
    'Weekly Sales Report'
) FROM sales;

-- Load CSV data
CREATE TABLE stocks AS
SELECT * FROM read_csv_auto('stocks.csv');

-- Visualize stock prices
SELECT line_chart(
    list(CAST(date AS VARCHAR) ORDER BY date),
    list(price ORDER BY date),
    'Stock Price Trend'
)
FROM stocks;

SQL Query → Data Extraction → HTML Generation → Browser Opens → Interactive Chart

1. SQL Query → Execute chart function with your data
2. Data Extraction → Extension processes your data
3. HTML Generation → Creates HTML with Plotly.js CDN link
4. Browser Opens → Opens in your default browser
5. Interactive Chart → Zoom, pan, hover, export

Note: Charts require internet connection on first use. After initial load, Plotly.js is cached by your browser and works offline.

┌─────────────────────────────────┐
│   DuckDB SQL Query              │
│   SELECT bar_chart(...)         │
└────────────┬────────────────────┘
             ↓
┌─────────────────────────────────┐
│   Extension (C++)               │
│   - Data extraction             │
│   - HTML generation             │
│   - Plotly.js CDN link          │
└────────────┬────────────────────┘
             ↓
┌─────────────────────────────────┐
│   Browser Opens                 │
│   - Loads Plotly.js from CDN    │
│   - Interactive chart           │
│   - Caches for offline use      │
└─────────────────────────────────┘

No external dependencies. No configuration. Just install and use.

All charts include:

• 🔍 Zoom - Click and drag to zoom into regions
• 👆 Pan - Shift + drag to pan around
• 📊 Hover - Hover over points to see values
• 💾 Export - Download as PNG image
• 🔄 Reset - Double-click to reset view

• Internet connection: Required for first-time chart rendering (Plotly.js loads from CDN)
• Browser: Any modern web browser (Chrome, Firefox, Safari, Edge)
• After initial use, charts work offline (browser caches Plotly.js)

MIT License - see LICENSE file

• Built with DuckDB
• Powered by Plotly.js
• Written in C++

• Community Extensions
• Issue Tracker
• DuckDB Documentation