pygraphistry

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

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	-0078D4?style=flat&logo=microsoftazure&logoColor=white)
  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 without requiring a database. It supports friendly Cypher syntax and declarative graph semantics through g.gfql("MATCH ..."), with the same 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

Install

Common configurations:

• Minimal core

  Includes: The GFQL dataframe-native graph query language, built-in layouts, Graphistry visualization server client

  python复制

  pip install graphistry

  Does not include graphistry[ai], plugins

• No dependencies and user-level

  python复制

  pip install --no-deps --user graphistry

• GPU acceleration - Optional

  Local GPU: Install RAPIDS and/or deploy a GPU-ready Graphistry server

  Remote GPU: Use the remote endpoints.

For further options, see the installation guides

Visualization quickstart

Quickly go from raw data to a styled and interactive Graphistry graph visualization:

…

Explore 10 Minutes to Graphistry Visualization for more visualization examples and options

PyGraphistry[AI] & GFQL quickstart - CPU & GPU

CPU graph pipeline combining graph ML, AI, mining, and visualization:

from graphistry import n, e, e_forward, e_reverse

# Graph analytics
g2 = g1.compute_igraph('pagerank')
assert 'pagerank' in g2._nodes.columns

# Graph ML/AI
g3 = g2.umap()
assert ('x' in g3._nodes.columns) and ('y' in g3._nodes.columns)

# Graph querying with GFQL
g4 = g3.gfql([
    n(query='pagerank > 0.1'), e_forward(), n(query='pagerank > 0.1')
])
assert (g4._nodes.pagerank > 0.1).all()

# Upload for GPU server visualization session
g4.plot()

The automatic GPU modes require almost no code changes:

import cudf
from graphistry import n, e, e_forward, e_reverse

# Modified -- Rebind data as a GPU dataframe and swap in a GPU plugin call
g1_gpu = g1.edges(cudf.from_pandas(df))
g2 = g1_gpu.compute_cugraph('pagerank')

# Unmodified -- Automatic GPU mode for all ML, AI, GFQL queries, & visualization APIs
g3 = g2.umap()
g4 = g3.gfql([
    n(query='pagerank > 0.1'), e_forward(), n(query='pagerank > 0.1')
])
g4.plot()

Explore 10 Minutes to PyGraphistry for a wider variety of graph processing.

PyGraphistry documentation

• Main PyGraphistry documentation
• 10 Minutes to: PyGraphistry, Visualization, GFQL
• Get started: Install, UI Guide, Notebooks
• Performance: PyGraphistry CPU+GPU & GFQL CPU+GPU
• API References
  • PyGraphistry API Reference: Visualization & Compute, PyGraphistry Cheatsheet
  • GFQL Documentation: [GFQL Cheats