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

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.

    Category

    Connector Tutorials

    Data Platforms, SQL & Logs

    Databricks Splunk PostgreSQL Azure Data Explorer (Kusto) Google Cloud Spanner

    Graph Databases

    Neo4j Amazon Neptune TigerGraph ArangoDB Memgraph

    Python Tools & Libraries

    CSV Pandas Apache Arrow NVIDIA RAPIDS NetworkX Graphviz

    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 dataframe-native graph query language, to ask relationship questions that are difficult for tabular tools and without requiring a database.

  • 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.

Install#

Common configurations:

  • Minimal core

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

    pip install graphistry

    Does not include graphistry[ai], plugins

  • No dependencies and user-level

    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:

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.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:

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 for a wider variety of graph processing.

PyGraphistry documentation#

Graphistry ecosystem#

Community and support#

Contribute#

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

Indices and tables#