garphield for Python

Explore graphs visually from a notebook. Pass a NetworkX graph or pandas DataFrame to show() and get an interactive Garphield view inline - then open the full workbench to build stories, apply layouts, and return the result to Python.

import garphield as gf
import networkx as nx

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

Once the view is displayed, drive it from Python:

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

Install

pip install garphield

With NetworkX and pandas adapters:

pip install 'garphield[tables,networkx]'

From pandas

import pandas as pd
from garphield import Project

edges = pd.DataFrame({"source": ["a"], "target": ["b"], "weight": [1.5]})
project = Project.from_pandas(edges)
tables = project.to_pandas()

Project files

Read, validate, and write .gph project files:

from garphield import Project

project = Project.load("project.gph")
print(project.semantic_fingerprint())
project.save("project-copy.gph")

Project.save() writes deterministic, human-readable JSON. Semantic and project fingerprints use RFC 8785/JCS canonical bytes.

Notebook round-trip

The wheel carries a version-matched notebook renderer, so the view works without a hosted renderer or CDN. Use Open in Garphield to continue in the full workbench at garphield.com, then Return to notebook to bring the result back to Python.

Use chrome=["minimap"] or chrome=["toolbar"] to keep embeds lightweight. project.save_html("graph.html", chrome=["minimap"]) writes a standalone interactive HTML file.

Environments

Local Jupyter, VS Code notebooks, JupyterHub, Google Colab, and Snowflake Snowpark notebooks are resolved automatically. For Snowpark, use show_snowpark() which materializes selected tables in the kernel before handoff.

Links

• garphield.com
• GitHub