[](https://codecov.io/gh/aetperf/edsger)
[](https://edsger.readthedocs.io/en/latest/?badge=latest)
[](https://pypi.org/project/edsger/)
[](https://pepy.tech/project/edsger)
[](https://pypi.org/project/edsger/)
[](https://github.com/psf/black)
[](https://github.com/MarcoGorelli/cython-lint)
[](https://opensource.org/licenses/MIT)
# Edsger
*Graph algorithms in Cython*
Welcome to our Python library for graph algorithms. So far, the library only includes Dijkstra's algorithm but we should add a range of common path algorithms later. It is also open-source and easy to integrate with other Python libraries. To get started, simply install the library using pip, and import it into your Python project.
Documentation : [https://edsger.readthedocs.io/en/latest/](https://edsger.readthedocs.io/en/latest/)
## Small example : Dijkstra's Algorithm
To use Dijkstra's algorithm, you can import the `Dijkstra` class from the `path` module. The function takes a graph and a source node as input, and returns the shortest path from the source node to all other nodes in the graph.
```python
import pandas as pd
from edsger.path import Dijkstra
# Create a DataFrame with the edges of the graph
edges = pd.DataFrame({
'tail': [0, 0, 1, 2, 2, 3],
'head': [1, 2, 2, 3, 4, 4],
'weight': [1, 4, 2, 1.5, 3, 1]
})
edges
```
| | tail | head | weight |
|---:|-------:|-------:|---------:|
| 0 | 0 | 1 | 1.0 |
| 1 | 0 | 2 | 4.0 |
| 2 | 1 | 2 | 2.0 |
| 3 | 2 | 3 | 1.5 |
| 4 | 2 | 4 | 3.0 |
| 5 | 3 | 4 | 1.0 |
```python
# Initialize the Dijkstra object
dijkstra = Dijkstra(edges)
# Run the algorithm from a source vertex
shortest_paths = dijkstra.run(vertex_idx=0)
print("Shortest paths:", shortest_paths)
```
Shortest paths: [0. 1. 3. 4.5 5.5]
We get the shortest paths from the source node 0 to all other nodes in the graph. The output is an array with the shortest path length to each node. A path length is the sum of the weights of the edges in the path.
## Installation
### Standard Installation
```bash
pip install edsger
```
## Why Use Edsger?
Edsger is designed to be **dataframe-friendly**, providing seamless integration with pandas workflows for graph algorithms. Also it is rather efficient on Linux. Our benchmarks on the USA road network (23.9M vertices, 57.7M edges) demonstrate nice performance:
<img src="https://raw.githubusercontent.com/aetperf/edsger/release/docs/source/assets/dijkstra_benchmark_comparison.png" alt="Dijkstra Performance Comparison" width="700">
## Contributing
We welcome contributions to the Edsger library. If you have any suggestions, bug reports, or feature requests, please open an issue on our [GitHub repository](https://github.com/aetperf/Edsger).
## License
Edsger is licensed under the MIT License. See the LICENSE file for more details.
## Contact
For any questions or inquiries, please contact François Pacull at [francois.pacull@architecture-performance.fr](mailto:francois.pacull@architecture-performance.fr).
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