yfiles-jupyter-graphs

yFiles Graphs for Jupyter logo

Using Neo4j, Kuzu, or SPARQL? Use our dedicated integrations instead: Neo4j · Kuzu · SPARQL

A graph visualization widget for Jupyter Notebook and JupyterLab powered by yFiles for HTML.

Visualize graphs from many sources out of the box:

NetworkX ✅
igraph ✅
neo4j ✅ (use the dedicated neo4j package)
pygraphviz ✅
Native Python dicts/lists ✅

Many more formats are supported via NetworkX imports.

Try the showcase notebook in Colab: https://colab.research.google.com/github/yWorks/yfiles-jupyter-graphs/blob/main/examples/feature-releases/showcase.ipynb

Key Features

Fast, production-grade graph rendering (yFiles engine)
Multiple automatic layouts (hierarchical, organic, circular, ...)
Data-driven styling (colors, sizes, labels, ...) from node/edge attributes
Sidebars for search, data inspection, and neighborhood exploration
Heatmaps and map visualization

Choose your path

There are extensions built on top of yfiles-jupyter-graphs that provide tailored APIs and convenient data mapping for their respective ecosystems:

If you work with Neo4j: use yfiles-jupyter-graphs-for-neo4j
If you work with Kuzu: use yfiles-jupyter-graphs-for-kuzu
If you query via SPARQL: use yfiles-jupyter-graphs-for-sparql

Otherwise, continue below with yfiles-jupyter-graphs.

Where it runs

JupyterLab or Jupyter Notebook
Visual Studio Code
Google Colab
Gemini Enterprise Agent Platform
Managed Service for Apache Spark
Azure Machine Learning Studio Notebooks
Amazon SageMaker
Kaggle
Just try it in your preferred platform for Jupyter notebooks

AI Coding Assistant

Working with an AI coding agent? Our agent guide contains instructions and best practices for coding agents.

Example start prompt:

Read the instructions at https://raw.githubusercontent.com/yWorks/yfiles-jupyter-graphs/refs/heads/main/agent-guide.md. Then, create a new Jupyter notebook that step-by-step explores the and visualizes it using yfiles-jupyter-graphs. Be creative and design visualizations that highlight interesting insights and aspects of the data.

Install

Prerequisites:

python >= 3.7
jupyter notebook or lab
ipywidgets >= 7.6.0

pip install yfiles-jupyter-graphs

Important: When installing from inside a notebook, prefer %pip install yfiles-jupyter-graphs over !pip install to ensure the package is installed into the running kernel’s environment.

Docker (optional)

If you want to start with a clean Jupyter environment that has the widget preinstalled:

mkdir yfiles-jupyter && cd yfiles-jupyter
echo -e "FROM jupyter/scipy-notebook\nRUN pip install yfiles-jupyter-graphs" > Dockerfile
docker build -t yfiles-jupyter-graphs-on-docker .
docker run -it -p 8888:8888 --name yfiles-jupyter yfiles-jupyter-graphs-on-docker

This approach has been verified with scipy-notebook:lab-3.4.7 and yfiles-jupyter-graphs==1.2.1. If you encounter issues with a newer image, please open an issue.

Quickstart

Display a simple graph:

from yfiles_jupyter_graphs import GraphWidget, Node, Edge
w = GraphWidget(
    nodes=[
        Node(id=0, properties={"firstName": "Alpha", "label": "Person A"}),
        Node(id=1, properties={"firstName": "Bravo", "label": "Person B"}),
        Node(id=2, properties={"firstName": "Charlie", "label": "Person C", "has_hat": False}),
        Node(id=3, properties={"firstName": "Delta", "label": "Person D", "likes_pizza": True})
    ],
    edges=[
        Edge(start=0, end=1, properties={"since": "1992", "label": "knows"}),
        Edge(start=1, end=3, properties={"label": "knows", "since": "1992"}),
        Edge(start=2, end=3, properties={"label": "knows", "since": "1992"}),
        Edge(start=0, end=2, properties={"label": "knows", "since": 234})
    ],
    directed=True
)
display(w)

Use the toolbar and sidebar to inspect data, search graph, explore neighborhood and change layouts.

Next steps

Check out more examples for the different features.
Jump directly to the API documentation.

Feature Gallery

See Node Neighborhood	Choose Graph Layout
Investigate Nodes or Edges Data	Search for Nodes or Edges
Import Graph Data	Make Data Dependent Property Changes
Define a Heatmap Background	Use a Map Background
Visualize nested data

Documentation

Code of Conduct

This project is governed by the Code of Conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to contact@yworks.com.

Feedback

This widget is by no means perfect. If you find something is not working as expected we are glad to receive an issue report from you. Please make sure to search for existing issues first and check if the issue is not an unsupported feature or known issue. If you did not find anything related, report a new issue with necessary information. Please also provide a clear and descriptive title and stick to the issue templates. See issues.

License

See LICENSE.md for license information.

Dependencies

Release files for yfiles-jupyter-graphs 2.0.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for yfiles-jupyter-graphs 2.0.0
File	Size	Uploaded
yfiles_jupyter_graphs-2.0.0.tar.gz	13.0 MB	Aug 10, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for yfiles-jupyter-graphs 2.0.0
File	Interpreter	ABI	Platform	Reset
yfiles_jupyter_graphs-2.0.0-py3-none-any.whl 16.3 MB Aug 10, 2026	Python 3	none	any	Details

Total release size:29.4 MB

Release files / yfiles_jupyter_graphs-2.0.0.tar.gz

Download URL	yfiles_jupyter_graphs-2.0.0.tar.gz
Size	13.0 MB
Tags	Source
SHA-256 checksum How to use checksums	`94b845c41060169b5c9e6a7eee8735610bfce17aa09e36bf25dd1f366df19f7f`
BLAKE2b-256 checksum How to use checksums	`2ca33184b145ed84a107b4adb99673c4a864177244a15bb334b3f247c573f52c`
Upload date	Aug 10, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.6`

Release files / yfiles_jupyter_graphs-2.0.0-py3-none-any.whl

Download URL	yfiles_jupyter_graphs-2.0.0-py3-none-any.whl
Size	16.3 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`dd49b25e267d2ea212a597a0f9042a4829e72a6d5b02e27bbe22054847daf63b`
BLAKE2b-256 checksum How to use checksums	`0dc4583f94de7024d2a092491d0eadcd5cecf4db7f3aa4076428d65a00ad0ce1`
Upload date	Aug 10, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.6`