PyGraphistry GFQL

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 start

from graphistry import n, e_forward

g2 = g.gfql([ n({'type': 'person'}), e_forward({'relation': 'transfers_to'}, min_hops=1, max_hops=3), n({'risk': True}) ])

Targeted patterns (high signal)

Edge query filtering

g2 = g.gfql([n(), e_forward(edge_query="type == 'replied_to' and submolt == 'X'"), n()])

Same-path constraints with where + compare/col

from graphistry import col, compare g2 = g.gfql([n(name='a'), e_forward(name='e'), n(name='b')], where=[compare(col('a', 'owner_id'), '==', col('b', 'owner_id'))])

Traverse 2-4 hops but only return hops 3-4

g2 = g.gfql([e_forward(min_hops=2, max_hops=4, output_min_hops=3, output_max_hops=4)])

High-value patterns

• When user explicitly asks for GFQL (or says gfql ), final snippets must include explicit .gfql([...]) ; do not substitute chain() /hop() as the primary answer.

• Only show chain() /hop() when the user explicitly asks for that shorthand; otherwise keep snippets in .gfql([...]) form.

• When the task explicitly says remote execution/dataset, use gfql_remote(...) as the primary query call; do not substitute chain() /hop() in that case.

• Use name= labels for intermediate matches when you need constraints.

• Use where=[...] for cross-step/path constraints.

• Use min_hops /max_hops and output_min_hops /output_max_hops for traversal vs returned slice.

• Use predicates (is_in , numeric/date predicates) for concise filtering.

• Use engine='auto' by default; force cudf /pandas only when needed.

• For neighborhood-mining tasks without full pattern logic, mention hop() / chain() as optional alternates after providing the primary .gfql([...]) answer.

Remote mode

Existing remote dataset

rg = graphistry.bind(dataset_id='my-dataset') res = rg.gfql_remote([n(), e_forward(), n()], engine='auto')

Remote slim payload (only required columns)

res = rg.gfql_remote([n(), e_forward(), n()], output_type='nodes', node_col_subset=['node_id', 'time'])

Post-process on remote side when you want trimmed transfer payloads

res = rg.python_remote_table(lambda g: g._edges[['src', 'dst']].head(1000))

Validation and safety

• Validate user-derived query fragments before execution.

• Normalize datetime columns before temporal predicates.

• Prefer small column subsets for remote result transfer.

Canonical docs

• GFQL index: https://pygraphistry.readthedocs.io/en/latest/gfql/index.html

• GFQL overview: https://pygraphistry.readthedocs.io/en/latest/gfql/overview.html

• GFQL quick reference: https://pygraphistry.readthedocs.io/en/latest/gfql/quick.html

• Predicate quick reference: https://pygraphistry.readthedocs.io/en/latest/gfql/predicates/quick.html

• GFQL remote mode: https://pygraphistry.readthedocs.io/en/latest/gfql/remote.html

• GFQL validation: https://pygraphistry.readthedocs.io/en/latest/gfql/validation/index.html

• GFQL + loaders/AI patterns: https://pygraphistry.readthedocs.io/en/latest/gfql/combo.html