# 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/) [<img src="https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack">](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)
[](https://twitter.com/graphistry)
<table style="width:100%;">
<tr valign="top">
<td align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank"><img src="https://i.imgur.com/z8SIh2E.png" title="Click to open."></a>
<a href="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" target="_blank">Demo: Interactive visualization of 80,000+ Facebook friendships</a> (<a href="http://snap.stanford.edu" target="_blank">source data</a></em>)
</td>
</tr>
</table>
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) Plug into [Amazon Neptune](https://docs.aws.amazon.com/neptune/latest/userguide/visualization-graphistry.html) ([notebook](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/neptune/neptune_cypher_viz_using_bolt.html)), [cuGraph](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/gpu_rapids/cugraph.html), [Databricks](https://www.databricks.com/solutions/accelerators/incident-investigation-using-graphistry) ([notebook](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.html)), [graphviz](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/graphviz/graphviz.html), [Neo4j](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.html), [Splunk](https://www.splunk.com/en_us/blog/security/supercharge-cybersecurity-investigations-with-splunk-and-graphistry-a-powerful-combination-for-interactive-graph-exploration.html) ([notebook](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/splunk/splunk_demo_public.html)), [TigerGraph](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/tigergraph/tigergraph_pygraphistry_bindings.html), and many more in the [notebook data provider demo gallery](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
<table>
<tr valign="top">
<td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=Twitter&splashAfter=true" target="_blank">Twitter Botnet<br><img width="266" src="https://i.imgur.com/qm5MCqS.jpg"></a></td>
<td width="33%" align="center">Edit Wars on Wikipedia<br><a href="https://i.imgur.com/074zFve.png" target="_blank"><img width="266" src="https://i.imgur.com/074zFve.png"></a><em>(<a href="https://snap.stanford.edu" target="_blank">data</a></em>)</td>
<td width="33%" align="center"><a href="https://hub.graphistry.com/graph/graph.html?dataset=bitC&splashAfter=true" target="_blank">100,000 Bitcoin Transactions<br><img width="266" height="266" src="https://i.imgur.com/axIkjfd.png"></a></td>
</tr>
<tr valign="top">
<td width="33%" align="center">Port Scan Attack<br><a href="http://i.imgur.com/vKUDySw.png" target="_blank"><img width="266" src="http://i.imgur.com/vKUDySw.png"></a></td>
<td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?dataset=PyGraphistry/M9RL4PQFSF&usertag=github&info=true&static=true&contentKey=Biogrid_Github_Demo&play=3000¢er=false&menu=true&goLive=false&left=-2.58e+4&right=4.35e+4&top=-1.72e+4&bottom=2.16e+4&legend={%22title%22:%22%3Ch3%3EBioGRID%20Repository%20of%20Protein%20Interactions%3C/h3%3E%22,%22subtitle%22:%22%3Cp%3EEach%20color%20represents%20an%20organism.%20Humans%20are%20in%20light%20blue.%3C/p%3E%22,%22nodes%22:%22Proteins/Genes%22,%22edges%22:%22Interactions%20reported%20in%20scientific%20publications%22}" target="_blank">Protein Interactions <br><img width="266" src="http://i.imgur.com/nrUHLFz.png" target="_blank"></a><em>(<a href="http://thebiogrid.org" target="_blank">data</a>)</em></td>
<td width="33%" align="center"><a href="http://hub.graphistry.com/graph/graph.html?&dataset=PyGraphistry/PC7D53HHS5&info=true&static=true&contentKey=SocioPlt_Github_Demo&play=3000¢er=false&menu=true&goLive=false&left=-236&right=265&top=-145&bottom=134&usertag=github&legend=%7B%22nodes%22%3A%20%22%3Cspan%20style%3D%5C%22color%3A%23a6cee3%3B%5C%22%3ELanguages%3C/span%3E%20/%20%3Cspan%20style%3D%5C%22color%3Argb%28106%2C%2061%2C%20154%29%3B%5C%22%3EStatements%3C/span%3E%22%2C%20%22edges%22%3A%20%22Strong%20Correlations%22%2C%20%22subtitle%22%3A%20%22%3Cp%3EFor%20more%20information%2C%20check%20out%20the%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//lmeyerov.github.io/projects/socioplt/viz/index.html%5C%22%3ESocio-PLT%3C/a%3E%20project.%20Make%20your%20own%20visualizations%20with%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//github.com/graphistry/pygraphistry%5C%22%3EPyGraphistry%3C/a%3E.%3C/p%3E%22%2C%20%22title%22%3A%20%22%3Ch3%3ECorrelation%20Between%20Statements%20about%20Programming%20Languages%3C/h3%3E%22%7D" target="_blank">Programming Languages<br><img width="266" src="http://i.imgur.com/0T0EKmD.png"></a><em>(<a href="http://lmeyerov.github.io/projects/socioplt/viz/index.html" target="_blank">data</a>)</em></td>
</tr>
</table>
## 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', as_continuous=True, ['blue', 'red'])
# 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 [CONTRIBUTE](https://pygraphistry.readthedocs.io/en/latest/CONTRIBUTE.html) and [DEVELOP](https://pygraphistry.readthedocs.io/en/latest/DEVELOP.html) for participating in PyGraphistry development, or reach out to our team
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"description": "# PyGraphistry: Leverage the power of graphs & GPUs to visualize, analyze, and scale your data\n\n\n[](https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL)\n[](https://pygraphistry.readthedocs.io/en/latest/)\n[](https://pypi.python.org/pypi/graphistry)\n[](https://pypi.python.org/pypi/graphistry)\n[](https://pypi.python.org/pypi/graphistry)\n\n\n[](https://status.graphistry.com/) [<img src=\"https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack\">](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)\n[](https://twitter.com/graphistry)\n\n\n<table style=\"width:100%;\">\n <tr valign=\"top\">\n <td align=\"center\"><a href=\"https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true\" target=\"_blank\"><img src=\"https://i.imgur.com/z8SIh2E.png\" title=\"Click to open.\"></a>\n <a href=\"https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true\" target=\"_blank\">Demo: Interactive visualization of 80,000+ Facebook friendships</a> (<a href=\"http://snap.stanford.edu\" target=\"_blank\">source data</a></em>)\n </td>\n </tr>\n</table>\n\nPyGraphistry 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:\n\n* [**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/).\n\n* [**Integrations:**](https://pygraphistry.readthedocs.io/en/latest/plugins.html) Plug into [Amazon Neptune](https://docs.aws.amazon.com/neptune/latest/userguide/visualization-graphistry.html) ([notebook](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/neptune/neptune_cypher_viz_using_bolt.html)), [cuGraph](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/gpu_rapids/cugraph.html), [Databricks](https://www.databricks.com/solutions/accelerators/incident-investigation-using-graphistry) ([notebook](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.html)), [graphviz](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/graphviz/graphviz.html), [Neo4j](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/neo4j/official/graphistry_bolt_tutorial_public.html), [Splunk](https://www.splunk.com/en_us/blog/security/supercharge-cybersecurity-investigations-with-splunk-and-graphistry-a-powerful-combination-for-interactive-graph-exploration.html) ([notebook](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/splunk/splunk_demo_public.html)), [TigerGraph](https://pygraphistry.readthedocs.io/en/latest/demos/demos_databases_apis/tigergraph/tigergraph_pygraphistry_bindings.html), and many more in the [notebook data provider demo gallery](https://pygraphistry.readthedocs.io/en/latest/notebooks/plugins.connectors.html).\n\n\n* [**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.\n\n* [**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.\n\n* [**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.\n\n* [**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.\n\n* [**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.\n\n\nFrom 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.\n\n\n\n## Gallery\n\n\nThe [notebook demo gallery](https://pygraphistry.readthedocs.io/en/latest/demos/for_analysis.html) shares many more live visualizations, demos, and integration examples\n\n<table>\n <tr valign=\"top\">\n <td width=\"33%\" align=\"center\"><a href=\"https://hub.graphistry.com/graph/graph.html?dataset=Twitter&splashAfter=true\" target=\"_blank\">Twitter Botnet<br><img width=\"266\" src=\"https://i.imgur.com/qm5MCqS.jpg\"></a></td>\n <td width=\"33%\" align=\"center\">Edit Wars on Wikipedia<br><a href=\"https://i.imgur.com/074zFve.png\" target=\"_blank\"><img width=\"266\" src=\"https://i.imgur.com/074zFve.png\"></a><em>(<a href=\"https://snap.stanford.edu\" target=\"_blank\">data</a></em>)</td>\n <td width=\"33%\" align=\"center\"><a href=\"https://hub.graphistry.com/graph/graph.html?dataset=bitC&splashAfter=true\" target=\"_blank\">100,000 Bitcoin Transactions<br><img width=\"266\" height=\"266\" src=\"https://i.imgur.com/axIkjfd.png\"></a></td>\n </tr>\n <tr valign=\"top\">\n <td width=\"33%\" align=\"center\">Port Scan Attack<br><a href=\"http://i.imgur.com/vKUDySw.png\" target=\"_blank\"><img width=\"266\" src=\"http://i.imgur.com/vKUDySw.png\"></a></td>\n <td width=\"33%\" align=\"center\"><a href=\"http://hub.graphistry.com/graph/graph.html?dataset=PyGraphistry/M9RL4PQFSF&usertag=github&info=true&static=true&contentKey=Biogrid_Github_Demo&play=3000¢er=false&menu=true&goLive=false&left=-2.58e+4&right=4.35e+4&top=-1.72e+4&bottom=2.16e+4&legend={%22title%22:%22%3Ch3%3EBioGRID%20Repository%20of%20Protein%20Interactions%3C/h3%3E%22,%22subtitle%22:%22%3Cp%3EEach%20color%20represents%20an%20organism.%20Humans%20are%20in%20light%20blue.%3C/p%3E%22,%22nodes%22:%22Proteins/Genes%22,%22edges%22:%22Interactions%20reported%20in%20scientific%20publications%22}\" target=\"_blank\">Protein Interactions <br><img width=\"266\" src=\"http://i.imgur.com/nrUHLFz.png\" target=\"_blank\"></a><em>(<a href=\"http://thebiogrid.org\" target=\"_blank\">data</a>)</em></td>\n <td width=\"33%\" align=\"center\"><a href=\"http://hub.graphistry.com/graph/graph.html?&dataset=PyGraphistry/PC7D53HHS5&info=true&static=true&contentKey=SocioPlt_Github_Demo&play=3000¢er=false&menu=true&goLive=false&left=-236&right=265&top=-145&bottom=134&usertag=github&legend=%7B%22nodes%22%3A%20%22%3Cspan%20style%3D%5C%22color%3A%23a6cee3%3B%5C%22%3ELanguages%3C/span%3E%20/%20%3Cspan%20style%3D%5C%22color%3Argb%28106%2C%2061%2C%20154%29%3B%5C%22%3EStatements%3C/span%3E%22%2C%20%22edges%22%3A%20%22Strong%20Correlations%22%2C%20%22subtitle%22%3A%20%22%3Cp%3EFor%20more%20information%2C%20check%20out%20the%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//lmeyerov.github.io/projects/socioplt/viz/index.html%5C%22%3ESocio-PLT%3C/a%3E%20project.%20Make%20your%20own%20visualizations%20with%20%3Ca%20target%3D%5C%22_blank%5C%22%20href%3D%5C%22https%3A//github.com/graphistry/pygraphistry%5C%22%3EPyGraphistry%3C/a%3E.%3C/p%3E%22%2C%20%22title%22%3A%20%22%3Ch3%3ECorrelation%20Between%20Statements%20about%20Programming%20Languages%3C/h3%3E%22%7D\" target=\"_blank\">Programming Languages<br><img width=\"266\" src=\"http://i.imgur.com/0T0EKmD.png\"></a><em>(<a href=\"http://lmeyerov.github.io/projects/socioplt/viz/index.html\" target=\"_blank\">data</a>)</em></td>\n </tr>\n</table>\n\n\n\n## Install\n\nCommon configurations:\n\n* **Minimal core**\n\n Includes: The GFQL dataframe-native graph query language, built-in layouts, Graphistry visualization server client\n\n ```python\n pip install graphistry\n ```\n\n Does not include `graphistry[ai]`, plugins\n\n* **No dependencies and user-level**\n\n ```python\n pip install --no-deps --user graphistry\n ```\n\n* **GPU acceleration** - Optional\n\n Local GPU: Install [RAPIDS](https://www.rapids.ai) and/or deploy a GPU-ready [Graphistry server](https://www.graphistry.com/get-started)\n \n Remote GPU: Use the [remote endpoints](https://www.graphistry.com/blog/graphistry-2-41-3).\n\nFor further options, see the [installation guides](https://pygraphistry.readthedocs.io/en/latest/install/index.html)\n\n\n## Visualization quickstart\n\nQuickly go from raw data to a styled and interactive Graphistry graph visualization:\n\n```python\nimport graphistry\nimport pandas as pd\n\n# Raw data as Pandas CPU dataframes, cuDF GPU dataframes, Spark, ...\ndf = pd.DataFrame({\n 'src': ['Alice', 'Bob', 'Carol'],\n 'dst': ['Bob', 'Carol', 'Alice'],\n 'friendship': [0.3, 0.95, 0.8]\n})\n\n# Bind\ng1 = graphistry.edges(df, 'src', 'dst')\n\n# Override styling defaults\ng1_styled = g1.encode_edge_color('friendship', as_continuous=True, ['blue', 'red'])\n\n# Connect: Free GPU accounts and self-hosting @ graphistry.com/get-started\ngraphistry.register(api=3, username='your_username', password='your_password')\n\n# Upload for GPU server visualization session\ng1_styled.plot()\n```\n\nExplore [10 Minutes to Graphistry Visualization](https://pygraphistry.readthedocs.io/en/latest/visualization/10min.html) for more visualization examples and options\n\n\n## PyGraphistry[AI] & GFQL quickstart - CPU & GPU\n\n**CPU graph pipeline** combining graph ML, AI, mining, and visualization:\n\n```python\nfrom graphistry import n, e, e_forward, e_reverse\n\n# Graph analytics\ng2 = g1.compute_igraph('pagerank')\nassert 'pagerank' in g2._nodes.columns\n\n# Graph ML/AI\ng3 = g2.umap()\nassert ('x' in g3._nodes.columns) and ('y' in g3._nodes.columns)\n\n# Graph querying with GFQL\ng4 = g3.chain([\n n(query='pagerank > 0.1'), e_forward(), n(query='pagerank > 0.1')\n])\nassert (g4._nodes.pagerank > 0.1).all()\n\n# Upload for GPU server visualization session\ng4.plot()\n```\n\nThe **automatic GPU modes** require almost no code changes:\n\n```python\nimport cudf\nfrom graphistry import n, e, e_forward, e_reverse\n\n# Modified -- Rebind data as a GPU dataframe and swap in a GPU plugin call\ng1_gpu = g1.edges(cudf.from_pandas(df))\ng2 = g1_gpu.compute_cugraph('pagerank')\n\n# Unmodified -- Automatic GPU mode for all ML, AI, GFQL queries, & visualization APIs\ng3 = g2.umap()\ng4 = g3.chain([\n n(query='pagerank > 0.1'), e_forward(), n(query='pagerank > 0.1')\n])\ng4.plot()\n```\n\nExplore [10 Minutes to PyGraphistry](https://pygraphistry.readthedocs.io/en/latest/10min.html) for a wider variety of graph processing.\n\n\n## PyGraphistry documentation\n\n* [Main PyGraphistry documentation](https://pygraphistry.readthedocs.io/en/latest/)\n* 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)\n* 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)\n* Performance: [PyGraphistry CPU+GPU](https://pygraphistry.readthedocs.io/en/latest/performance.html) & [GFQL CPU+GPU](https://pygraphistry.readthedocs.io/en/latest/gfql/performance.html)\n* API References\n - [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)\n - [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)\n - [Plugins](https://pygraphistry.readthedocs.io/en/latest/plugins.html): Databricks, Splunk, Neptune, Neo4j, RAPIDS, and more\n - 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/)\n\n## Graphistry ecosystem\n\n- **Graphistry server:**\n - Launch - [Graphistry Hub, Graphistry cloud marketplaces, and self-hosting](https://www.graphistry.com/get-started)\n - Self-hosting: [Administration (including Docker)](https://github.com/graphistry/graphistry-cli) & [Kubernetes](https://github.com/graphistry/graphistry-helm)\n\n- **Graphistry client APIs:**\n - 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/)\n - [PyGraphistry](https://pygraphistry.readthedocs.io/en/latest/index.html)\n - [Graphistry for Microsoft PowerBI](https://hub.graphistry.com/docs/powerbi/pbi/)\n\n- **Additional projects**:\n - [Louie.ai](https://louie.ai/): GenAI-native notebooks & dashboards to talk to your databases & Graphistry\n - [graph-app-kit](https://github.com/graphistry/graph-app-kit): Streamlit Python dashboards with batteries-include graph packages\n - [cu-cat](https://chat.openai.com/chat): Automatic GPU feature engineering\n\n\n## Community and support\n\n- [Blog](https://www.graphistry.com/blog) for tutorials, case studies, and updates\n- [Slack](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g): Join the Graphistry Community Slack for discussions and support\n- [Twitter](https://twitter.com/graphistry) & [LinkedIn](https://www.linkedin.com/company/graphistry): Follow for updates\n- [GitHub Issues](https://github.com/graphistry/pygraphistry/issues) open source support\n- [Graphistry ZenDesk](https://graphistry.zendesk.com/) dedicated enterprise support\n\n## Contribute\n\nSee [CONTRIBUTE](https://pygraphistry.readthedocs.io/en/latest/CONTRIBUTE.html) and [DEVELOP](https://pygraphistry.readthedocs.io/en/latest/DEVELOP.html) for participating in PyGraphistry development, or reach out to our team\n\n",
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