# graphistry/pygraphistry

PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

Repository: https://github.com/graphistry/pygraphistry
Canonical: https://ross.abutalabs.com/products/pygraphistry
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: graph, visualization, gpu, graphistry, python, rapids, cugraph, networkx, neo4j, tigergraph, pandas, csv, webgl, splunk, cudf, jupyter, igraph, network-analysis, network-visualization, graph-visualization
Last push: 2026-08-21T13:42:38+00:00

## Health v2 (maintenance only)
Score: 97/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 94, longevity 100
- inputs: {"age_days": 4110, "days_push": 12, "days_rel": 42, "gap_med": 2, "n_releases_24m": 32}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2551, forks 230 (observed 2026-08-28T04:07:00.513998+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: data-visualization, machine-learning, etl, database-driver
- domain: data-visualization, data-science, graph-processing, analytics, security
- platform: python, cross-platform
- tags: graph-visualization, gpu-accelerated, network-analysis, rapids, cugraph, networkx, neo4j, webgl, notebooks, gpu, jupyter

## Member repositories
- graphistry/pygraphistry (main) score 97

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:00.513998+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:23:53.984064+00:00, confidence not recorded.
  - readme: https://github.com/graphistry/pygraphistry (fetched 2026-08-28T04:07:00.513998+00:00, sha bde346640acb)
- Data as of 2026-08-30T08:39:29.467469+00:00.