ipysigma
A Jupyter widget using sigma.js to render interactive networks.
Decision gist · record as of 2026-08-14
Yes, if you work with networks in Jupyter notebooks and want interactive visualization without leaving the cell. The low install friction, permissive license, and stable maintenance make it practical. The aging status (262 days since last release) is not a blocker for stable use, but verify compatibility with your Jupyter version before relying on it for production workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Jupyter Notebook or Lab with widget support enabled; on Google Colab, must call output.enable_custom_widget_manager() first.
- Low friction installation with a single runtime dependency (ipywidgets).
- The package is aging—last release was 262 days ago—but the repository remains active with 308 stars, suggesting stable maintenance rather than abandonment.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute ipysigma with minimal restrictions, making it suitable for both open and closed-source projects.
last release 2025-11-25 (262 days) · last repo commit 2025-11-25 · 308 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 107,632 downloads/mo, #12,606 on PyPI
Alternatives
Verify before relying
pip install ipysigma
from ipysigma import Sigma
Sigma(g, node_size='degree', node_color='category')- Compatibility with current Jupyter versions (classifiers list Python 3.4–3.7, which are EOL).
- Performance characteristics when rendering very large graphs (node/edge count thresholds).
- Whether widget-side metric computation (e.g., Louvain) works reliably in all Jupyter environments.
What it is and what it does
ipysigma is a Jupyter widget that embeds interactive network visualizations directly into notebook cells. It wraps sigma.js and graphology to display graphs with rich visual customization—you can map node and edge properties to color, size, shape, labels, and other visual attributes. The widget supports both static exploration and synchronized "small multiples" for comparing graph features side-by-side.
The package is designed for exploratory network analysis in notebooks. You pass a graph object and optionally specify which attributes or computed metrics should drive the visual encoding. It can compute metrics on the widget side without round-tripping to Python, and integrates with ipywidgets for interactivity.
Use it for
- Explore community structure in a network by coloring nodes by computed partition.
- Compare two layouts or metrics of the same graph side-by-side using SigmaGrid's small multiples.
- Visualize a knowledge graph or ontology with custom node shapes, colors, and labels mapped to attributes.
- Interactively inspect node degree, centrality, or other metrics as node size or color.
- Render a directed graph with edge weights as visual size in an interactive notebook cell.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with networks in Jupyter notebooks and want interactive visualization without leaving the cell.
The low install friction, permissive license, and stable maintenance make it practical. The aging status (262 days since last release) is not a blocker for stable use, but verify compatibility with your Jupyter version before relying on it for production workflows.
Install
ipysigma on PyPI
Before you install
Low friction installation with a single runtime dependency (ipywidgets). The package is aging—last release was 262 days ago—but the repository remains active with 308 stars, suggesting stable maintenance rather than abandonment.
Requires Jupyter Notebook or Lab with widget support enabled; on Google Colab, must call output.enable_custom_widget_manager() first.
License in practice
MIT license is permissive; you can use, modify, and distribute ipysigma with minimal restrictions, making it suitable for both open and closed-source projects.
Quickstart
pip install ipysigma
from ipysigma import Sigma
Sigma(g, node_size='degree', node_color='category')Verify before relying
- Compatibility with current Jupyter versions (classifiers list Python 3.4–3.7, which are EOL).
- Performance characteristics when rendering very large graphs (node/edge count thresholds).
- Whether widget-side metric computation (e.g., Louvain) works reliably in all Jupyter environments.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.6 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packageipywidgets |
| Maintenance | Aging 262 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 107,632 / month, #12,606 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Framework :: JupyterIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7 |
Evidence: ipysigma-0.24.6-py3-none-any.whl
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See also ipytree · ipympl · ipyvue · ipydagred3 · py3Dmol · pythreejs · ipywidgets · ipyevents · igraph · pySigma