graphistry/pygraphistry

Health v2 · maintenance only

Adoption not part of the score

2551 stars · 230 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyGraphistry is a Python library for loading, shaping, and visually exploring large graphs with GPU-accelerated rendering and analytics via the Graphistry platform. It integrates with dataframes (Pandas, cuDF, Arrow, Spark) and graph tools like NetworkX, igraph, Neo4j, and TigerGraph.

Use cases

• visualize large graphs interactively in jupyter notebooks
• plot networkx graphs with gpu acceleration
• explore relationships in pandas dataframes as a graph
• analyze security logs as a graph from splunk
• run graph analytics on cudf dataframes with rapids
• connect to neo4j and visualize query results
• detect fraud patterns in transaction networks

When to choose

• you need interactive visualization of graphs too large for typical tools
• your workflow is python/dataframe-based and you want GPU acceleration
• you want to combine graph analytics with visual exploration in notebooks

When to avoid

• you need only static chart plotting without graph semantics
• you cannot use the Graphistry server or GPU resources
• your data is small tabular data with no relational structure

Facets

library · maturity active

data-visualization machine-learning etl database-driver data-visualization data-science graph-processing analytics security python cross-platform graph-visualization gpu-accelerated network-analysis rapids cugraph networkx neo4j webgl notebooks gpu jupyter

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markdown · JSON · MCP: product_card(name="graphistry/pygraphistry")