https://github.com/graphistry/pygraphistry
PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
https://github.com/graphistry/pygraphistry
csv cudf cugraph gpu graph graph-visualization graphistry igraph jupyter neo4j network-analysis network-visualization networkx pandas python rapids splunk tigergraph visualization webgl
Last synced: 5 days ago
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PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer
- Host: GitHub
- URL: https://github.com/graphistry/pygraphistry
- Owner: graphistry
- License: bsd-3-clause
- Created: 2015-06-02T20:28:42.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2026-01-11T21:31:51.000Z (10 days ago)
- Last Synced: 2026-01-11T23:59:43.757Z (10 days ago)
- Topics: csv, cudf, cugraph, gpu, graph, graph-visualization, graphistry, igraph, jupyter, neo4j, network-analysis, network-visualization, networkx, pandas, python, rapids, splunk, tigergraph, visualization, webgl
- Language: Python
- Homepage:
- Size: 106 MB
- Stars: 2,388
- Watchers: 44
- Forks: 218
- Open Issues: 239
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
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README
# PyGraphistry: Leverage the power of graphs & GPUs to visualize, analyze, and scale your data
[](https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL)
[](https://pygraphistry.readthedocs.io/en/latest/)
[](https://pypi.python.org/pypi/graphistry)
[](https://pypi.python.org/pypi/graphistry)
[](https://pypi.python.org/pypi/graphistry)
[](https://status.graphistry.com/) [
](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)
[](https://twitter.com/graphistry)
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:**](https://pygraphistry.readthedocs.io/en/latest/10min.html) Quickly ingest & prepare data in many formats, shapes, and scales as graphs. Use tools like Pandas, Spark, [RAPIDS (GPU)](https://www.rapids.ai), and [Apache Arrow](https://arrow.apache.org/).
* [**Integrations:**](https://pygraphistry.readthedocs.io/en/latest/plugins.html) Connect to graph databases, data platforms, Python tools, and more.
| Category | Connector Tutorials |
|----------|---------------------|
| **Data Platforms, SQL & Logs** | [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/splunk/splunk_demo_public.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/sql/postgres.html) [-0078D4?style=flat&logo=microsoftazure&logoColor=white)](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/microsoft/kusto/graphistry_ADX_kusto_demo.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/spanner/google_spanner_finance_graph.html) |
| **Graph Databases** | [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/neptune/neptune_cypher_viz_using_bolt.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/tigergraph/tigergraph_pygraphistry_bindings.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/arango/arango_tutorial.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/memgraph/visualizing_iam_dataset.html) |
| **Python Tools & Libraries** | [](https://pygraphistry.readthedocs.io/en/latest/demos/upload_csv_miniapp.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/gpu_rapids/part_i_cpu_pandas.html) [](https://pygraphistry.readthedocs.io/en/latest/performance.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/gpu_rapids/cugraph.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/networkx/networkx.html) [](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/graphviz/graphviz.html) |
*[View all connectors →](https://pygraphistry.readthedocs.io/en/latest/notebooks/plugins.connectors.html)*
* [**Prototype locally and deploy remotely:**](https://www.graphistry.com/get-started) 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:**](https://pygraphistry.readthedocs.io/en/latest/gfql/index.html) Use GFQL, the first dataframe-native graph query language, to ask relationship questions that are difficult for tabular tools and without requiring a database.
* [**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:**](https://pygraphistry.readthedocs.io/en/latest/visualization/10min.html#) 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:**](https://pygraphistry.readthedocs.io/en/latest/performance.html) 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.
## Gallery
The [notebook demo gallery](https://pygraphistry.readthedocs.io/en/latest/demos/for_analysis.html) 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**
Includes: The GFQL dataframe-native graph query language, built-in layouts, Graphistry visualization server client
```python
pip install graphistry
```
Does not include `graphistry[ai]`, plugins
* **No dependencies and user-level**
```python
pip install --no-deps --user graphistry
```
* **GPU acceleration** - Optional
Local GPU: Install [RAPIDS](https://www.rapids.ai) and/or deploy a GPU-ready [Graphistry server](https://www.graphistry.com/get-started)
Remote GPU: Use the [remote endpoints](https://www.graphistry.com/blog/graphistry-2-41-3).
For further options, see the [installation guides](https://pygraphistry.readthedocs.io/en/latest/install/index.html)
## Visualization quickstart
Quickly go from raw data to a styled and interactive Graphistry graph visualization:
```python
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](https://pygraphistry.readthedocs.io/en/latest/visualization/10min.html) for more visualization examples and options
## PyGraphistry[AI] & GFQL quickstart - CPU & GPU
**CPU graph pipeline** combining graph ML, AI, mining, and visualization:
```python
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.chain([
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:
```python
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.chain([
n(query='pagerank > 0.1'), e_forward(), n(query='pagerank > 0.1')
])
g4.plot()
```
Explore [10 Minutes to PyGraphistry](https://pygraphistry.readthedocs.io/en/latest/10min.html) for a wider variety of graph processing.
## PyGraphistry documentation
* [Main PyGraphistry documentation](https://pygraphistry.readthedocs.io/en/latest/)
* 10 Minutes to: [PyGraphistry](https://pygraphistry.readthedocs.io/en/latest/10min.html), [Visualization](https://pygraphistry.readthedocs.io/en/latest/visualization/10min.html), [GFQL](https://pygraphistry.readthedocs.io/en/latest/gfql/about.html)
* Get started: [Install](https://pygraphistry.readthedocs.io/en/latest/install/index.html), [UI Guide](https://hub.graphistry.com/docs/ui/index/), [Notebooks](https://pygraphistry.readthedocs.io/en/latest/demos/for_analysis.html)
* Performance: [PyGraphistry CPU+GPU](https://pygraphistry.readthedocs.io/en/latest/performance.html) & [GFQL CPU+GPU](https://pygraphistry.readthedocs.io/en/latest/gfql/performance.html)
* API References
* [PyGraphistry API Reference](https://pygraphistry.readthedocs.io/en/latest/api/index.html): [Visualization & Compute](https://pygraphistry.readthedocs.io/en/latest/visualization/index.html), [PyGraphistry Cheatsheet](https://pygraphistry.readthedocs.io/en/latest/cheatsheet.html)
* [GFQL Documentation](https://pygraphistry.readthedocs.io/en/latest/gfql/index.html): [GFQL Cheatsheet](https://pygraphistry.readthedocs.io/en/latest/gfql/quick.html) and [GFQL Operator Cheatsheet](https://pygraphistry.readthedocs.io/en/latest/gfql/predicates/quick.html)
* [Plugins](https://pygraphistry.readthedocs.io/en/latest/plugins.html): Databricks, Splunk, Neptune, Neo4j, RAPIDS, and more
* Web: [iframe](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions), [JavaScript](https://hub.graphistry.com/static/js-docs/index.html?path=/docs/introduction--docs), [REST](https://hub.graphistry.com/docs/api/1/rest/auth/)
## Graphistry ecosystem
* **Graphistry server:**
* Launch - [Graphistry Hub, Graphistry cloud marketplaces, and self-hosting](https://www.graphistry.com/get-started)
* Self-hosting: [Administration (including Docker)](https://github.com/graphistry/graphistry-cli) & [Kubernetes](https://github.com/graphistry/graphistry-helm)
* **Graphistry client APIs:**
* Web: [iframe](https://hub.graphistry.com/docs/api/1/rest/url/#urloptions), [JavaScript](https://hub.graphistry.com/static/js-docs/index.html?path=/docs/introduction--docs), [REST](https://hub.graphistry.com/docs/api/1/rest/auth/)
* [PyGraphistry](https://pygraphistry.readthedocs.io/en/latest/index.html)
* [Graphistry for Microsoft PowerBI](https://hub.graphistry.com/docs/powerbi/pbi/)
* **Additional projects**:
* [Louie.ai](https://louie.ai/): GenAI-native notebooks & dashboards to talk to your databases & Graphistry
* [graph-app-kit](https://github.com/graphistry/graph-app-kit): Streamlit Python dashboards with batteries-include graph packages
* [cu-cat](https://chat.openai.com/chat): Automatic GPU feature engineering
## Community and support
* [Blog](https://www.graphistry.com/blog) for tutorials, case studies, and updates
* [Slack](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g): Join the Graphistry Community Slack for discussions and support
* [Twitter](https://twitter.com/graphistry) & [LinkedIn](https://www.linkedin.com/company/graphistry): Follow for updates
* [GitHub Issues](https://github.com/graphistry/pygraphistry/issues) open source support
* [Graphistry ZenDesk](https://graphistry.zendesk.com/) dedicated enterprise support
## Contribute
See [CONTRIBUTING](https://pygraphistry.readthedocs.io/en/latest/CONTRIBUTING.html) and [DEVELOP](https://pygraphistry.readthedocs.io/en/latest/DEVELOP.html) for participating in PyGraphistry development, or reach out to our team