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. - For local Cypher-style queries on in-memory PyGraphistry graphs (no external DB), use
g.gfql("MATCH..."). Note: graphistry.cypher() is a distinct Neo4j/Memgraph/Neptune connector, not the same as local GFQL Cypher. - 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/Memgraph/Neptune connector (runs query on external DB server)
g = graphistry.cypher('MATCH (a)-[r]->(b) RETURN a,b,r')
g.plot()
# Local Cypher via GFQL (no external DB needed — preferred for local graphs)
g2 = g.gfql("MATCH (a)-[r]->(b) WHERE a.score > 10 RETURN a.id, b.id")
# 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
