The graphing extension that makes your data more usable
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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.
You can use this extension in the default Jupyter environments, but also in other environments like VSCode or GoogleColab.
Looking for an easy way to visualize your Neo4j database? Our thin, open-source wrapper Python widget yFiles Jupyter Graphs for Neo4j provides an easy to use API for this use case.
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, igraph, PyGraphviz, Neo4j, 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.
Looking for an easy way to visualize your Neo4j database? Our thin, open-source wrapper Python widget yFiles Jupyter Graphs for Neo4j provides an easy to use API for this use case.
What to expect on this page
yFiles Graphs for Jupyter
Why use itFeaturesSamplesFree licenseTutorials
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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 stylehighlights 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 item neighborhood
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Check the sidebar's Neighborhood tab to explore node connections and view their adjacent items.
Choose graph layout
Select a graph layout from the toolbar to rearrange your graph items automatically.
Investigate node or edge data
Explore node and edge properties in the data tab of the sidebar to inspect your item's data.
Search for nodes or edges
Find specific nodes and edges in your graph with the integrated graph search.
Import graph data
Import your graph data from popular Python packages like NetworkX, igraph, PyGraphviz, Neo4j, or any structured list of nodes and edges.
Change properties based on data
Use your business data to adjust the visualization of nodes and edges with versatile data mapping functions.
Dark theme
The widget automatically matches your preferred theme of the Jupyter notebook or your browser.
Use selected items
Extract the data of interactively selected items back to your Jupyter notebook cell.
Visualize data as heatmap
Visualize data as a heatmap overlay for an additional information layer.
Feedback?
Let us know about your use case and what features you would like to see in the future.
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More Features
Visualization mappings
Map your data properties to different visualization properties per item, to highlight specific structures.
Geometry mappings
Map your data properties to different item geometries.
Export items to a Jupyter cell
Obtain interactively selected graph items in a Jupyter cell.
Export to yEd Live
Export the diagram to our free online diagram editor yEd Live, for extensive further editing and export features.
Easily visualize
Neo4j graphs
The yFiles Jupyter Graphs for Neo4j widget is a thin, open-source wrapper for yFiles Jupyter Graphsthat provides an easy way to visualize Cypher queries resolved against Neo4j databases.
Simply provide the driver and a query to show the diagram!
Sample projects
Explore our numerous sample Notebooks that showcase the most popular features:
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. Besides the default Jupyter environments, the extension is also supported in other environments like VSCode or GoogleColab.
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!
Supported Environments
You can use yFiles Graphs for Jupyter in many environments that support Jupyter notebooks:
Just try it in your preferred platform for Jupyter notebooks
In case you need a specific activation for your domain, contact us.
Video guides and tutorials
Quick start tutorial
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Webinar yFiles Graphs for Jupyter
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Frequently Asked Questions
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Getting started
Step 1
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!
The enterprise solution for data visualization and graph drawing
Integrable into your application
Suitable for any use case
Highly customizable to fit your needs
Compatible with any type of data
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