name: pygraphistry-core
description: "Core PyGraphistry workflow for authentication, shaping edges/nodes/hypergraphs, and plotting. Use for first-run setup, converting tables to graphs, and producing an initial interactive graph quickly and safely."
PyGraphistry Core
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.
Quick workflow
- Register to a Graphistry server.
- Build graph from edges/nodes (or hypergraph from wide rows).
- Bind visual columns as needed.
- Plot and iterate.
Minimal baseline
import os
import graphistry
graphistry.register(
api=3,
username=os.environ.get('GRAPHISTRY_USERNAME'),
password=os.environ.get('GRAPHISTRY_PASSWORD')
)
Auth variants (org + key flows)
# Organization-scoped login (SSO or user/pass org routing)
graphistry.register(api=3, org_name=os.environ['GRAPHISTRY_ORG_NAME'], idp_name=os.environ.get('GRAPHISTRY_IDP_NAME'))
# Service account / personal key flow
graphistry.register(
api=3,
personal_key_id=os.environ['GRAPHISTRY_PERSONAL_KEY_ID'],
personal_key_secret=os.environ['GRAPHISTRY_PERSONAL_KEY_SECRET']
)
# edges_df: src,dst,... and nodes_df: id,...
edges_df['type'] = edges_df.get('type', 'transaction')
nodes_df['type'] = nodes_df.get('type', 'entity')
g = graphistry.edges(edges_df, 'src', 'dst').nodes(nodes_df, 'id')
g.plot()
Hypergraph baseline
# Build graph from multiple entity columns in one table
hg = graphistry.hypergraph(df, ['actor', 'event', 'location'])
hg['graph'].plot()
ETL shaping checklist
- Normalize identifier columns before binding (
src/dst/id type consistency, null handling).
- Prefer a plain
type column on both edges and nodes for legend-friendly defaults and consistent category encodings.
- Deduplicate high-volume repeated rows before first upload.
- Materialize nodes for node-centric steps:
g = graphistry.edges(edges_df, 'src', 'dst').materialize_nodes()
Practical checks
- Confirm source/destination columns are non-null and correctly typed.
- Materialize nodes if needed (
g.materialize_nodes()) before node-centric operations.
- Start with smaller slices for first render on large data.
- For neighborhood expansion and pattern mining, always use
.gfql([...]) or .gfql("MATCH ..."). The methods hop() and chain() are deprecated.
- Keep credentials in environment variables only; do not hardcode usernames/passwords/tokens.
Canonical docs
By