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

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Demo: Interactive visualization of 80,000+ Facebook friendships (source 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.

| | Graph Databases |

| | Python Tools & Libraries |

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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]:](https://pygraphistry.readthedocs.io/en/latest/gfql/combo.html#) 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.

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

Twitter Botnet	Edit Wars on Wikipedia (data)	100,000 Bitcoin Transactions
Port Scan Attack	Protein Interactions (data)	Programming Languages (data)

Install

Common configurations:

• Minimal core

pip install graphistry

Does not include graphistry[ai], plugins

• No dependencies and user-level

pip install --no-deps --user graphistry

• GPU acceleration - Optional

For further options, see the installation guides

Visualization quickstart

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

import graphistry
import pandas as pd
Raw data as Pandas CPU dataframes, cuDF GPU dataframes, Spark, ...
df = pd.DataFrame({
    'src': ['Alice', 'Bob', 'Carol'],
    'dst': ['Bob', 'Carol', 'Alice'],
    'friendship': [0.3, 0.95, 0.8]
})
Bind
g1 = graphistry.edges(df, 'src', 'dst')
Override styling defaults
g1_styled = g1.encode_edge_color('friendship', ['blue', 'red'], as_continuous=True)
Connect: Free GPU accounts and self-hosting @ graphistry.com/get-started
graphistry.register(api=3, username='your_username', password='your_password')
Upload for GPU server visualization session
g1_styled.plot()

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

Graphistry ecosystem

• Graphistry server:

• Graphistry client APIs:

• Additional projects:

Community and support

• Blog for tutorials, case studies, and updates
• Slack: Join the Graphistry Community Slack for discussions and support
• Twitter & LinkedIn: Follow for updates
• GitHub Issues open source support
• Graphistry ZenDesk dedicated enterprise support

Contribute

See CONTRIBUTING and DEVELOP for participating in PyGraphistry development, or reach out to our team