altair-upset 0.5.0

Create interactive UpSet plots using Altair

Altair UpSet

Create beautiful and interactive UpSet plots using Altair. UpSet plots are a powerful alternative to Venn diagrams for visualizing set intersections, especially when dealing with many sets.

Features

• 🎨 Beautiful, interactive visualizations powered by Altair/Vega-Lite
• 🔄 Dynamic sorting by frequency or degree
• 🎯 Interactive highlighting and filtering
• 📱 Responsive design that works in Jupyter notebooks and web browsers
• 🎨 Customizable colors, sizes, and themes
• 🔍 Tooltips with detailed intersection information
• 🚀 Support for both Pandas and Polars DataFrames
• 📐 Horizontal and vertical layout orientations for different use cases

Installation

pip install altair-upset

Or with conda:

conda install -c conda-forge altair-upset

Quick Start

import altair_upset as au
import pandas as pd
# Or use Polars
import polars as pl

# Create sample data with Pandas
data = pd.DataFrame({
    'set1': [1, 0, 1, 1],
    'set2': [1, 1, 0, 1],
    'set3': [0, 1, 1, 0]
})

# Create UpSet plot
chart = au.UpSetAltair(
    data=data,  # or data_pl.to_pandas()
    sets=["set1", "set2", "set3"],
    title="Sample UpSet Plot"
)

# Display the chart
chart.show()

Example Gallery

The package includes a comprehensive gallery of examples demonstrating various features and use cases:

Basic Examples

• Basic UpSet Plot: Simple visualization of streaming service subscriptions
• Sorting and Filtering: Different ways to organize and present set intersections
• Custom Styling: Examples of color schemes, themes, and layout customization

Real-World Examples

• Gene Set Analysis: Visualizing intersections of biological pathways
• Survey Response Analysis: Understanding multiple-choice survey patterns
• Social Media Usage: Exploring platform usage overlaps with demographics
• Movie Genre Analysis: Investigating genre combinations in film datasets

Advanced Features

• Interactive Selection: Enhanced interaction and filtering capabilities
• Custom Tooltips: Rich tooltips with additional information
• Responsive Design: Adapting to different display sizes
• Theme Examples: Using built-in and custom themes

To run the examples:

git clone https://github.com/edmundmiller/altair-upset.git
cd altair-upset
pip install -e ".[examples]"
python examples/basic_upset.py

Each example includes:

• Sample data generation or loading
• Plot creation with different features
• Analysis and statistics
• Detailed comments explaining each step

Advanced Usage

Sorting and Filtering

# Sort by degree (number of sets in intersection)
chart = au.UpSetAltair(
    data=data,
    sets=["set1", "set2", "set3"],
    sort_by="degree",
    sort_order="descending"
)

Customizing Appearance

# Custom colors and sizes
chart = au.UpSetAltair(
    data=data,
    sets=["set1", "set2", "set3"],
    color_range=["#1f77b4", "#ff7f0e", "#2ca02c"],
    highlight_color="#d62728",
    width=800,
    height=500
)

Using Abbreviations

# Use abbreviations for long set names
chart = au.UpSetAltair(
    data=data,
    sets=["Very Long Set Name 1", "Very Long Set Name 2", "Very Long Set Name 3"],
    abbre=["S1", "S2", "S3"]
)

Vertical Orientation

# Use vertical orientation for interactive plots that can be scrolled
# Vertical layouts display cardinality horizontally (on the left)
# and set sizes vertically (on top)
chart = au.UpSetAltair(
    data=data,
    sets=["set1", "set2", "set3"],
    orientation="vertical"  # Default is "horizontal"
)

The matrix can be sorted in various ways - by cardinality (size), degree, or sets. UpSet works well in both orientations:

• Vertical layouts are better for interactive UpSet plots that can be scrolled
• Horizontal layouts (default) are best for figures in papers

Development

1. Clone the repository:

git clone https://github.com/edmundmiller/altair-upset.git
cd altair-upset

2. Create a virtual environment and install dependencies:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -e ".[dev,test,docs]"

3. Install pre-commit hooks:

pre-commit install

4. Run tests:

pytest

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Credits

This package is based on the UpSet: Visualization of Intersecting Sets technique. If you use an UpSet figure in a publication, please cite the original paper:

Alexander Lex, Nils Gehlenborg, Hendrik Strobelt, Romain Vuillemot, Hanspeter Pfister, UpSet: Visualization of Intersecting Sets, IEEE Transactions on Visualization and Computer Graphics (InfoVis '14), vol. 20, no. 12, pp. 1983–1992, 2014. doi: 10.1109/TVCG.2014.2346248

The original function was from hms-dbmi/upset-altair-notebook. The following updates from that are:

1. Turning it into a package
2. Snapshoting the functionality with Altair 4
3. Porting to Altair 5
4. Adding additional advanced features