anywidget-graph 0.1.0

Interactive graph visualization for Python notebooks using anywidget

anywidget-graph

Interactive graph visualization for Python notebooks.

Works with Marimo, Jupyter, VS Code, Colab, anywhere anywidget runs.

Features

• Universal — One widget, every notebook environment
• Backend-agnostic — Grafeo, Neo4j, NetworkX, pandas, or raw dicts
• Interactive — Pan, zoom, click, expand neighbors, select paths
• Customizable — Colors, sizes, shapes, layouts
• Performant — Virtualized rendering for large graphs
• Exportable — PNG, SVG, JSON

Installation

uv add anywidget-graph

Quick Start

from anywidget_graph import Graph

graph = Graph.from_dict({
    "nodes": [
        {"id": "alice", "label": "Alice", "group": "person"},
        {"id": "bob", "label": "Bob", "group": "person"},
        {"id": "paper", "label": "Graph Theory", "group": "document"},
    ],
    "edges": [
        {"source": "alice", "target": "bob", "label": "knows"},
        {"source": "alice", "target": "paper", "label": "authored"},
    ]
})

graph

Data Sources

Dictionary

from anywidget_graph import Graph

graph = Graph.from_dict({
    "nodes": [{"id": "a"}, {"id": "b"}],
    "edges": [{"source": "a", "target": "b"}]
})

Grafeo

from grafeo import GrafeoDB
from anywidget_graph import Graph

db = GrafeoDB()
db.execute("INSERT (:Person {name: 'Alice'})-[:KNOWS]->(:Person {name: 'Bob'})")

result = db.execute("MATCH (a)-[r]->(b) RETURN a, r, b")
graph = Graph.from_grafeo(result)

Neo4j

from neo4j import GraphDatabase
from anywidget_graph import Graph

driver = GraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

with driver.session() as session:
    result = session.run("MATCH (a)-[r]->(b) RETURN a, r, b LIMIT 100")
    graph = Graph.from_neo4j(result)

NetworkX

import networkx as nx
from anywidget_graph import Graph

G = nx.karate_club_graph()
graph = Graph.from_networkx(G)

pandas

import pandas as pd
from anywidget_graph import Graph

edges = pd.DataFrame({
    "source": ["alice", "alice", "bob"],
    "target": ["bob", "carol", "carol"],
    "weight": [1.0, 0.5, 0.8]
})

graph = Graph.from_pandas(edges)

Interactivity

Events

graph = Graph.from_dict(data)

@graph.on_node_click
def handle_node(node_id, node_data):
    print(f"Clicked: {node_id}")

@graph.on_edge_click  
def handle_edge(edge_id, edge_data):
    print(f"Edge: {edge_data['label']}")

Selection

graph.selected_nodes         # Get current selection
graph.select(["alice"])      # Select nodes
graph.clear_selection()      # Clear

Expansion

graph.expand("alice")        # Show neighbors
graph.collapse("alice")      # Hide neighbors

Styling

By Group

graph = Graph.from_dict(
    data,
    node_styles={
        "person": {"color": "#4CAF50", "size": 30},
        "document": {"color": "#2196F3", "shape": "square"},
    }
)

By Property

graph = Graph.from_dict(
    data,
    node_color="group",                    # Color by field
    node_size=lambda n: n["score"] * 10,   # Size by function
    edge_width="weight",                   # Width by field
)

Layouts

Graph.from_dict(data, layout="force")        # Default
Graph.from_dict(data, layout="hierarchical")
Graph.from_dict(data, layout="circular")
Graph.from_dict(data, layout="grid")

Options

graph = Graph.from_dict(
    data,
    width=800,
    height=600,
    directed=True,
    labels=True,
    edge_labels=False,
    physics=True,
    zoom=(0.1, 4),
)

Large Graphs

For 1000+ nodes:

graph = Graph.from_dict(
    data,
    virtualize=True,
    cluster=True,
)

Export

graph.to_png("graph.png")
graph.to_svg("graph.svg")
graph.to_json("graph.json")

Environment Support

Environment	Supported
Marimo	✅
JupyterLab	✅
Jupyter Notebook	✅
VS Code	✅
Google Colab	✅
Databricks	✅

Related

• anywidget — Custom Jupyter widgets made easy
• Grafeo — Embeddable graph database
• grafeo-wasm — Grafeo in the browser

License

Apache-2.0