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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 9 years ago)
Default Branch: master
Last Pushed: 2024-10-29T04:41:05.000Z (3 months ago)
Last Synced: 2024-10-29T09:18:11.690Z (2 months 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: 27.5 MB
Stars: 2,153
Watchers: 51
Forks: 206
Open Issues: 175
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.txt
- Security: SECURITY.md

Awesome Lists containing this project

awesome-starts - graphistry/pygraphistry - PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer (Python)
awesome-list - PyGraphistry - A Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer. (Data Visualization / Data Management)
starred-awesome - pygraphistry - PyGraphistry is library to extract, transform, and visually explore big graphs (Python)
awesome-starred - graphistry/pygraphistry - PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer (python)
StarryDivineSky - graphistry/pygraphistry

README

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

![Build Status](https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg)

[![CodeQL](https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg)](https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL)

[![Documentation Status](https://readthedocs.org/projects/pygraphistry/badge/?version=latest)](https://pygraphistry.readthedocs.io/en/latest/)

[![Latest Version](https://img.shields.io/pypi/v/graphistry.svg)](https://pypi.python.org/pypi/graphistry)

[![Latest Version](https://img.shields.io/pypi/pyversions/graphistry.svg)](https://pypi.python.org/pypi/graphistry)

[![License](https://img.shields.io/pypi/l/graphistry.svg)](https://pypi.python.org/pypi/graphistry)

![PyPI - Downloads](https://img.shields.io/pypi/dm/graphistry)

[![Uptime Robot status](https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com)](https://status.graphistry.com/) [](https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g)

[![Twitter Follow](https://img.shields.io/twitter/follow/graphistry)](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) 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

    

        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