| name | pygraphistry-core |
| description | Core PyGraphistry workflow: auth, DataFrame-to-graph shaping, and first interactive plot. Use when asked to "register graphistry", "get started with pygraphistry", "plot my edges dataframe", "graphistry.register()", "bind src and dst columns", "make a hypergraph", "materialize nodes", or any first-graph / ETL-to-plot task. Also triggers on "first graphistry graph", "graphistry install", "api=3", or questions about graphistry auth credentials. Proactively suggest when the user is setting up graphistry for the first time or can't get a basic plot working from a DataFrame.
|
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)
graphistry.register(api=3, org_name=os.environ['GRAPHISTRY_ORG_NAME'], idp_name=os.environ.get('GRAPHISTRY_IDP_NAME'))
graphistry.register(
api=3,
personal_key_id=os.environ['GRAPHISTRY_PERSONAL_KEY_ID'],
personal_key_secret=os.environ['GRAPHISTRY_PERSONAL_KEY_SECRET']
)
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
hg = graphistry.hypergraph(df, ['actor', 'event', 'location'], engine='pandas')
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 GFQL execution, explicitly request
engine='polars' to retain Polars results; the automatic/default path does not select Polars. The exact engine literals are 'pandas', 'cudf', 'dask', 'dask_cudf', 'polars', 'polars-gpu', 'auto' — 'polars-gpu' is hyphenated, and there is no polars_gpu spelling.
gfql() has no strict= argument. Off-engine analytic policy is set with graphistry.compute.gfql.lazy.set_call_mode('auto'|'strict') or the GFQL_POLARS_CALL_MODE env var; see pygraphistry-gfql for the engine section.- Do not recommend
hypergraph(..., engine='polars'|'polars-gpu') yet: the current API annotation lists them, but the upstream hypergraph frame implementation still lacks their dispatch path. Use the supported pandas/cuDF hypergraph engines, then opt into Polars/Polars-GPU for subsequent GFQL work when appropriate.
- 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
