Garphield for Python

The garphield package moves a graph into an interactive notebook view and returns the edited project or NetworkX graph to the same Python session.

Garphield supports Python 3.10 through 3.14.

Install

pip install 'garphield[networkx]'

Use 'garphield[tables]' for pandas without NetworkX, or install both extras.

From NetworkX

import garphield as gph
import networkx as nx

graph = nx.karate_club_graph()
view = gph.show(
    graph,
    node_color="club",
    node_size=graph.degree,
    node_label=str,
)

show() displays the graph and returns a synchronous GraphView. Move between the notebook view and ordinary Python without a separate event loop:

view.select([0, 1, 2]).fit()
view.bind("node_color", gph.algorithm("louvain", resolution=1.1))

edited_graph = view.to_networkx()
edited_project = view.to_project()

Bindings accept familiar Python values:

Python value	Example	Use
Attribute name	`node_color="club"`	Bind an existing attribute.
Mapping or view	`node_size=graph.degree`	Match values by node identity.
Iterable	`node_size=centrality.values()`	Assign in graph iteration order.
Set	`node_color=important`	Encode membership.
Partition	`node_color=[group_a, group_b]`	Encode categories.
Callable	`node_label=lambda node: str(node)`	Compute a value.
Garphield algorithm	`gph.algorithm("louvain")`	Compute inside the view.

Pass positions and an initial layout directly:

view = gph.show(graph, pos=nx.spring_layout(graph), layout="force")

From pandas

An edge table is enough:

import pandas as pd
import garphield as gph

edges = pd.DataFrame(
    {
        "source": ["ada", "ada", "grace"],
        "target": ["grace", "alan", "alan"],
        "weight": [1.5, 0.5, 2.0],
    }
)

view = gph.show(edges)
tables = view.to_project().to_pandas()

Pass a node table when you have node attributes:

project = gph.Project.from_pandas(
    edges,
    nodes,
    source="from_id",
    target="to_id",
    node_id="person_id",
)

Round-trip guarantees

The adapters preserve direction, multigraph state, edge keys, attributes, and typed identities. Values such as True, 1, 1.0, and "1" remain distinct. The adapter escapes reserved attribute names in the project manifest and restores them on the way back.

multi = nx.MultiDiGraph()
multi.add_edge("ada", "grace", key="mentor", weight=1.5)

restored = gph.Project.from_networkx(multi).to_networkx()
assert restored.edges["ada", "grace", "mentor"]["weight"] == 1.5

An encoding collision raises IdentityCollisionError instead of merging nodes.

Open the full workbench

Choose Open in Garphield in the notebook view for the full workbench. Return to notebook sends the edited project back to the same GraphView.