PyGraphistry Connectors

Doc routing (local + canonical)

• First route with ../pygraphistry/references/pygraphistry-readthedocs-toc.md .

• Use ../pygraphistry/references/pygraphistry-readthedocs-top-level.tsv for section-level shortcuts.

• Only scan ../pygraphistry/references/pygraphistry-readthedocs-sitemap.xml when a needed page is missing.

• Use one batched discovery read before deep-page reads; avoid cat * and serial micro-reads.

• In user-facing answers, prefer canonical https://pygraphistry.readthedocs.io/en/latest/... links.

Strategy

• Prefer dataframe-first ingestion when practical, then bind with edges()/nodes() .

• Use connector-specific notebook patterns when auth/query semantics are specialized.

• For very large datasets, push filtering/aggregation upstream before plotting.

• Keep connector and Graphistry credentials in env vars or secret stores; no hardcoded keys.

• Never use placeholder literals like username='user' / password='pass' / username='...' ; use os.environ[...] or os.environ.get(...) .

• For concise tasks, respond with a single compact code block and minimal prose.

• In concise snippets, prefer explicit privacy literals ('private' or 'organization' ) over placeholder variables.

Connector triage rubric

• Use native graph-db connectors (cypher , Neptune/TigerGraph flows) when traversal is best expressed upstream.

• Use SQL/log source extraction when your source is tabular or SIEM-centric, then bind in PyGraphistry.

• If unsure, start with source-native query -> dataframe -> edges()/nodes() , then optimize connector depth.

Connector families

• Graph DBs: Neo4j, Neptune, TigerGraph, Memgraph, Arango.

• Data/SQL: Databricks, PostgreSQL, Spanner, warehouse-style pipelines.

• Logs/SIEM: Splunk, Kusto, AlienVault.

• Compute/layout plugins: networkx, graphviz, cugraph, igraph, hypernetx.

Minimal examples

Neo4j-style cypher path (example)

g = graphistry.cypher('MATCH (a)-[r]->(b) RETURN a,b,r') g.plot()

Graphistry org/service-account auth before connector workflows

graphistry.register( api=3, org_name=os.environ.get('GRAPHISTRY_ORG_NAME'), personal_key_id=os.environ.get('GRAPHISTRY_PERSONAL_KEY_ID'), personal_key_secret=os.environ.get('GRAPHISTRY_PERSONAL_KEY_SECRET') )

Generic dataframe path after source-specific query/extract

edges_df: src,dst,...

g = graphistry.edges(edges_df, 'src', 'dst') graphistry.privacy(mode='private') plot_url = g.plot(render=False)

Connector-oriented flow with explicit nodes + focused GFQL slice

Example source can be Neo4j/Splunk -> dataframe extraction

g = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_df, 'id') g_focus = g.gfql([...]).name('connector-slice') graphistry.privacy(mode='organization') plot_url = g_focus.plot(render=False)

Canonical docs

• Plugins overview: https://pygraphistry.readthedocs.io/en/latest/plugins.html

• Connector notebooks: https://pygraphistry.readthedocs.io/en/latest/notebooks/plugins.connectors.html

• Compute/layout plugin notebooks: https://pygraphistry.readthedocs.io/en/latest/notebooks/plugins.compute.html

• Notebooks index: https://pygraphistry.readthedocs.io/en/latest/notebooks/index.html