network-graph-viewer 0.2.0

Interactive network graphs in a Jupyter notebook

network-graph-viewer

Interactive network graphs in a Jupyter notebook, from the Network Graph Viewer.

The whole app runs in the cell output: the sidebar, the filters, the metrics, the data table. Nothing is sent anywhere. The graph goes out to the browser as the app's own workspace format, and the selection and any edits come back.

Install

pip install network-graph-viewer

or:

uv add network-graph-viewer

Nothing else is needed: the widget ships its own JavaScript, and pandas and networkx are only used if you hand it one of theirs.

Use

import network_graph_viewer as ngv
import pandas as pd

edges = pd.DataFrame(
    [
        {"from": "ana", "to": "ben", "weight": 3, "team": "design"},
        {"from": "ben", "to": "cleo", "weight": 1, "team": "design"},
    ]
)

w = ngv.show(edges, source="from", target="to", color="team")
w

edges can also be a list of dicts, a list of (source, target) pairs, or a networkx graph, in which case the node attributes come with it:

import networkx as nx

ngv.show(nx.karate_club_graph(), color="club")

Node attributes that live in their own table go in as nodes, and any node an edge names but the table does not gets a row anyway:

ngv.show(edges, source="from", target="to", nodes=people, node_id="Id")

What the cell shows

The cell shows the graph and nothing else. Every panel has a labelled tab on the edge of the stage that opens it, and panels= opens any of them to start with:

ngv.show(edges, source="from", target="to", panels=["sidebar", "table"])

The widget follows the notebook's own light or dark theme and keeps following it, so switching the JupyterLab theme switches the graph too. Pin it with theme="light" or theme="dark", or change it live in the View menu on the graph.

Reading the graph back

The widget is live. Click a node, edit a cell, run a metric, and the kernel sees it:

w.selected_node  # 'ana', or None
w.edges  # the edge table as a DataFrame, edits included
w.nodes  # likewise, with any computed columns
w.save("graph.ngv.json")

w.edges and w.nodes are None until the browser has reported something, which it does shortly after the widget first draws.

How it works

The graph is handed to the browser as the app's own .ngv.json workspace, so a notebook goes in through the same door as a dropped file or a shared link, and meets the same reader on the other side. What comes back is the selection and the edited tables. Nothing is uploaded: the kernel and the browser are talking over the notebook's own connection.

build_workspace is that translation on its own, if you want the dictionary without a widget:

workspace = ngv.build_workspace(edges, source="from", target="to")

Every cell has to survive the trip as JSON, so NumPy scalars, timestamps and decimals are each converted to the nearest thing a cell can hold, and an integer too large for a JSON number keeps its digits as text rather than rounding into a collision.

Development

The widget loads one JavaScript file, src/network_graph_viewer/static/widget.js. It is built from the TypeScript app at the repository root and is not in the repository: a fresh clone has to build it before the package will run, and so does anything that rebuilds the app.

pnpm install            # once, at the repository root
pnpm build:widget       # writes static/widget.js

Then, in this directory:

uv sync
uv run pytest
uv run pytest --nbmake examples/demo.ipynb
uv run ruff check && uv run ruff format

The release workflow builds the bundle from the source at the tag, so what gets published is never taken on trust from anybody's working tree.