yFiles Graphs for Jupyter
Visualize graph networks with Python

Jupyter connected to yFiles Graphs for Jupyter generates a beautiful graph

The graphing extension that makes your data more usable

For Everyone
For Techies

yFiles Graphs for Jupyter is a free diagram visualization extension for JupyterLab and Jupyter Notebook. You can easily load structures from your favorite Python graph package and benefit from the superior visualization and automatic layouts of our established yFiles SDK.

Gain new insights into your data and create readable representations of your network by utilizing the automatic layout algorithms, inspecting the item’s neighbors and associated data, mapping data to colors, and more.

yFiles Graphs for Jupyter is a free diagram visualization extension for JupyterLab and Jupyter Notebook. It can import structured data from popular Python graph packages like NetworkX, graph-tool, igraph, PyGraphviz, or any structured list of nodes and edges.

Powerful layout algorithms from our established yFiles SDK are included. You can easily apply the whole range – organic, hierarchic, tree, orthogonal, circular, and radial – to your graph structure. A suitable, clear visualization helps you gain a better understanding of your data.

The embedded extension provides interactive features like automatic layouts, item neighborhood and data views, and search capabilities, as well as an API to integrate the high-performance algorithms and specify data-driven mappings for item color or geometry.

yFiles Graphs for Jupyter 1.2.0

GitHub page

Install

pip install yfiles_jupyter_graphs

See at pypi.org
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Why use yFiles Graphs for Jupyter?

Import and Visualize

Import from popular Python graph packages and create revealing yet concise visualizations. Just pass the graph data of NetworkX, graph-tool, igraph, PyGraphviz, or structured node and edge lists to the widget and interactively explore your network.

Automatic Layouts

Benefit of yFiles' superior automatic layout algorithms. Easily arrange your graph with different layout styles: Hierarchic, organic (force-directed), tree, orthogonal, circular, or radial. Each layout style highlights different structural features of the graph and helps you gain new insights into the data.

Data-driven mappings

yFiles offers customizable, data-driven mappings for nodes and edges. These mappings let you adjust visual aspects of the diagram - like color, scale, and edge thickness - as well as structural aspects like an item label or position.

Features

See node neighborhood

Hide Description
Open in yEd LiveDownload
node neighborhoodSee how it works

Choose graph layout

graph layoutsSee how it works

Investigate node or edge data

Investigate nodes or edges dataSee how it works

Search for nodes or edges

Search for nodes or edgesSee how it works

Import graph data

Import Graph DataSee how it works

Change properties based on data

properties depending on dataSee how it works

Free license

Valuable visualizations – at no cost


We are pleased to offer you a perpetual, free, non-transferable license to install and dynamically use this extension in your browser on top of your Jupyter system.

Technical information

yFiles Graphs for Jupyter is an extension for JupyterLab and Jupyter Notebook.

It is based on yFiles - the superior diagramming SDK. You can try a fully-functional version of yFiles free of charge. Explore the whole scope of graph drawing and integrate interactive visualizations into your own software products!

Requirements

To use yFiles Graphs for Jupyter , you must have the following installed:

Help

Have more questions?
Find helpful information here:

Getting started

Step 1
Install Jupyter

Install JupyterLab or Jupyter Notebook on your system.

Step 2
Install the extension

Install the yFiles Graphs for Jupyter extension with

pip install yfiles_jupyter_graphs

Step 3
Explore your graph

Instantiate the extension, import structured graph data, and start exploring!

Install yFiles Graphs for Jupyter

The enterprise solution for data visualization and graph drawing

Integrable into your application
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Highly customizable to fit your needs
Compatible with any type of data

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