PyGraphistry: Leverage the power of graphs & GPUs to visualize, analyze, and scale your data

<img src="https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack">

<table style="width:100%;"> <tr valign="top"> <td align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank"><img src="https://i.imgur.com/z8SIh2E.png" title="Click to open."></a> <a href="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank">Demo: Interactive visualization of 80,000+ Facebook friendships</a> (<a href="http://snap.stanford.edu" target="_blank">source data</a></em>) </td> </tr> </table>

PyGraphistry is an open source Python library for data scientists and developers to leverage the power of graph visualization, analytics, AI, including with native GPU acceleration:

• Python dataframe-native graph processing: Quickly ingest & prepare data in many formats, shapes, and scales as graphs. Use tools like Pandas, Spark, RAPIDS (GPU), and Apache Arrow.

• Integrations: Connect to graph databases, data platforms, Python tools, and more.

  | Category | Connector Tutorials | |----------|---------------------| | Data Platforms, SQL & Logs | | | Graph Databases | | | Python Tools & Libraries | |

  View all connectors →

• Prototype locally and deploy remotely: Prototype from notebooks like Jupyter and Databricks using local CPUs & GPUs, and then power production dashboards & pipelines with Graphistry Hub and your own self-hosted servers.

• Query graphs with GFQL: Use GFQL, the first fully vectorized dataframe-native graph query language with an open-source GPU runtime, to ask relationship questions that are difficult for tabular tools and without requiring a database, including friendly Cypher syntax and declarative graph semantics through g.gfql("MATCH ..."), with the same GFQL execution model available on the current bound graph or remotely via g.gfql_remote([...]).

• graphistry[ai]: Call streamlined graph ML & AI methods to benefit from clustering, UMAP embeddings, graph neural networks, automatic feature engineering, and more.

• Visualize & explore large graphs: In just a few minutes, create stunning interactive visualizations with millions of edges and many point-and-click built-ins like drilldowns, timebars, and filtering. When ready, customize with Python, JavaScript, and REST APIs.

• Columnar & GPU acceleration: CPU-mode ingestion and wrangling is fast due to native use of Apache Arrow and columnar analytics, and the optional RAPIDS-based GPU mode delivers 100X+ speedups.

From global 10 banks, manufacturers, news agencies, and government agencies, to startups, game companies, scientists, biotechs, and NGOs, many teams are tackling their graph workloads with Graphistry.

AI Assistant Integration

For LLM coding assistants (Claude Code, Cursor, Codex, etc.), install the official graphistry-skills package for better PyGraphistry code generation:

npx skills add graphistry/graphistry-skills

Skills improve AI success rates from ~50% to ~90% on PyGraphistry tasks by providing context-aware guidance for graph ETL, visualization, GFQL queries, and AI workflows.

Gallery

The notebook demo gallery shares many more live visualizations, demos, and integration examples

<table> <tr valign="top"> <td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=Twitter&splashAfter=true" target="_blank">Twitter Botnet<br><img width="266" src="https://i.imgur.com/qm5MCqS.jpg"></a></td> <td width="33%" align="center">Edit Wars on Wikipedia<br><a href="https://i.imgur.com/074zFve.png" target="_blank"><img width="266" src="https://i.imgur.com/074zFve.png"></a><em>(<a href="https://snap.stanford.edu" target="_blank">data</a></em>)</td> <td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=bitC&splashAfter=true" target="_blank">100,000 Bitcoin Transactions<br><img width="266" height="266" src="https://i.imgur.com/axIkjfd.png"></a></td> </tr> <tr valign="top"> <td width="33%" align="center">Port Scan Attack<br><a href="http://i.imgur.com/vKUDySw.png" target="_blank"><img width="266" src="http://i.imgur.com/vKUDySw.png"></a></td> <td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=PyGraphistry/M9RL4PQFSF&usertag=github&info=true&static=true&contentKey=Biogrid_Github_Demo&play=3000&center=false&menu=true&goLive=false&left=-2.58e+4&right=4.35e+4&top=-1.72e+4&bottom=2.16e+4&legend={%22title%22:%22%3Ch3%3EBioGRID%20Repository%20of%20Protein%20Interactions%3C/h3%3E%22,%22subtitle%22:%22%3Cp%3EEach%20color%20represents%20an%20organism.%20Humans%20are%20in%20