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https://github.com/Zsailer/nx_altair
Draw interactive NetworkX graphs with Altair
https://github.com/Zsailer/nx_altair
altair networkx pandas python
Last synced: about 2 months ago
JSON representation
Draw interactive NetworkX graphs with Altair
- Host: GitHub
- URL: https://github.com/Zsailer/nx_altair
- Owner: Zsailer
- License: mit
- Created: 2018-05-13T00:10:12.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-09-27T23:13:07.000Z (9 months ago)
- Last Synced: 2024-04-29T22:43:26.438Z (about 2 months ago)
- Topics: altair, networkx, pandas, python
- Language: Python
- Homepage:
- Size: 2.66 MB
- Stars: 222
- Watchers: 10
- Forks: 27
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-vega - nx_altair - [NetworkX](https://networkx.org/) graphs with Altair. (Altair / Packages)
- awesome-python-machine-learning-resources - GitHub - 60% open · ⏱️ 02.06.2020): (数据可视化)
README
# nx_altair
*Draw NetworkX graphs with Altair*
[](https://gitter.im/nx_altair/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link)
**nx_altair** offers a similar **draw** API to NetworkX but returns Altair Charts instead.
If you'd like to contribute, join the [Gitter chatroom](https://gitter.im/nx_altair/Lobby?utm_source=share-link&utm_medium=link&utm_campaign=share-link) and share your ideas! Also, checkout the [to-do list](#todo-list) below.
## Examples
If you'd like to start playing with nx_altair, download [this notebook](examples/nx_altair-tutorial.ipynb)!
### Simple graph
```python
import networkx as nx
import nx_altair as nxa
# Generate a random graph
G = nx.fast_gnp_random_graph(n=20, p=0.25)
# Compute positions for viz.
pos = nx.spring_layout(G)
# Draw the graph using Altair
viz = nxa.draw_networkx(G, pos=pos)
# Show it as an interactive plot!
viz.interactive()
```
### Leverage Altair
### Customize the visualization
**nx_altair** also supports many of the same arguments from NetworkX for styling your network--with an Altair twist! Map visualization attributes in a declarative manner.
```python
import numpy as np
# Add weights to nodes and edges
for n in G.nodes():
G.nodes[n]['weight'] = np.random.randn()
for e in G.edges():
G.edges[e]['weight'] = np.random.uniform(1, 10)
# Draw the graph using Altair
viz = nxa.draw_networkx(
G, pos=pos,
node_color='weight',
cmap='viridis',
width='weight',
edge_color='black',
)
# Show it as an interactive plot!
viz.interactive()
```
## Install
To install from PyPI:
```
pip install nx_altair
```
To install for development, clone this repos and install using pip
```
pip install -e .
```
## Contributing
We welcome pull requests! If you find a bug, we'd love for you to submit a PR. If you're not sure how to do that, check out this [simple guide](https://github.com/Zsailer/guide-to-working-as-team-on-github).
If you have a feature request, please open an issue or submit a PR!
## Todo list
A list of things to-do (good ideas for PRs).
- [ ] Add `draw` method that mirrors networkx (easy)
- [ ] Node color scale vmix, vmax (easy)
- [ ] Edge color scale vmix, vmax (easy)
- [ ] Node labels (medium)
- [ ] Edge labels (medium)
- [ ] Mouse-hover interactivity (medium)
- [ ] Arrows for edges (hard)
- [ ] Support for large (>1000 edge) networks (hard)
- [ ] Force-directed interactivity (very hard---beyond altair's current support).