[](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. The library includes both Dijkstra's and Bellman-Ford's algorithms, with plans to add more 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.
## Bellman-Ford Algorithm: Handling Negative Weights
The Bellman-Ford algorithm can handle graphs with negative edge weights and detect negative cycles, making it suitable for more complex scenarios than Dijkstra's algorithm.
```python
from edsger.path import BellmanFord
# Create a graph with negative weights
edges_negative = pd.DataFrame({
'tail': [0, 0, 1, 1, 2, 3],
'head': [1, 2, 2, 3, 3, 4],
'weight': [1, 4, -2, 5, 1, 3] # Note the negative weight
})
edges_negative
```
| | tail | head | weight |
|---:|-------:|-------:|---------:|
| 0 | 0 | 1 | 1.0 |
| 1 | 0 | 2 | 4.0 |
| 2 | 1 | 2 | -2.0 |
| 3 | 1 | 3 | 5.0 |
| 4 | 2 | 3 | 1.0 |
| 5 | 3 | 4 | 3.0 |
```python
# Initialize and run Bellman-Ford
bf = BellmanFord(edges_negative)
shortest_paths = bf.run(vertex_idx=0)
print("Shortest paths:", shortest_paths)
```
Shortest paths: [ 0. 1. -1. 0. 3.]
The Bellman-Ford algorithm finds the optimal path even with negative weights. In this example, the shortest path from node 0 to node 2 has length -1 (going 0→1→2 with weights 1 + (-2) = -1), which is shorter than the direct path 0→2 with weight 4.
### Negative Cycle Detection
Bellman-Ford can also detect negative cycles, which indicate that no shortest path exists:
```python
# Create a graph with a negative cycle
edges_cycle = pd.DataFrame({
'tail': [0, 1, 2],
'head': [1, 2, 0],
'weight': [1, -2, -1] # Cycle 0→1→2→0 has total weight -2
})
bf_cycle = BellmanFord(edges_cycle)
try:
bf_cycle.run(vertex_idx=0)
except ValueError as e:
print("Error:", e)
```
Error: Negative cycle detected in the graph
## 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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"description": "\n\n[](https://codecov.io/gh/aetperf/edsger)\n[](https://edsger.readthedocs.io/en/latest/?badge=latest)\n[](https://pypi.org/project/edsger/)\n[](https://pepy.tech/project/edsger)\n[](https://pypi.org/project/edsger/)\n[](https://github.com/psf/black)\n[](https://github.com/MarcoGorelli/cython-lint)\n[](https://opensource.org/licenses/MIT)\n\n# Edsger\n\n*Graph algorithms in Cython*\n\nWelcome to our Python library for graph algorithms. The library includes both Dijkstra's and Bellman-Ford's algorithms, with plans to add more 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.\n\nDocumentation : [https://edsger.readthedocs.io/en/latest/](https://edsger.readthedocs.io/en/latest/)\n\n## Small example : Dijkstra's Algorithm\n\nTo 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.\n\n```python\nimport pandas as pd\n\nfrom edsger.path import Dijkstra\n\n# Create a DataFrame with the edges of the graph\nedges = pd.DataFrame({\n 'tail': [0, 0, 1, 2, 2, 3],\n 'head': [1, 2, 2, 3, 4, 4],\n 'weight': [1, 4, 2, 1.5, 3, 1]\n})\nedges\n```\n\n| | tail | head | weight |\n|---:|-------:|-------:|---------:|\n| 0 | 0 | 1 | 1.0 |\n| 1 | 0 | 2 | 4.0 |\n| 2 | 1 | 2 | 2.0 |\n| 3 | 2 | 3 | 1.5 |\n| 4 | 2 | 4 | 3.0 |\n| 5 | 3 | 4 | 1.0 |\n\n```python\n# Initialize the Dijkstra object\ndijkstra = Dijkstra(edges)\n\n# Run the algorithm from a source vertex\nshortest_paths = dijkstra.run(vertex_idx=0)\nprint(\"Shortest paths:\", shortest_paths)\n```\n\n Shortest paths: [0. 1. 3. 4.5 5.5]\n\nWe 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.\n\n## Bellman-Ford Algorithm: Handling Negative Weights\n\nThe Bellman-Ford algorithm can handle graphs with negative edge weights and detect negative cycles, making it suitable for more complex scenarios than Dijkstra's algorithm.\n\n```python\nfrom edsger.path import BellmanFord\n\n# Create a graph with negative weights\nedges_negative = pd.DataFrame({\n 'tail': [0, 0, 1, 1, 2, 3],\n 'head': [1, 2, 2, 3, 3, 4],\n 'weight': [1, 4, -2, 5, 1, 3] # Note the negative weight\n})\nedges_negative\n```\n\n| | tail | head | weight |\n|---:|-------:|-------:|---------:|\n| 0 | 0 | 1 | 1.0 |\n| 1 | 0 | 2 | 4.0 |\n| 2 | 1 | 2 | -2.0 |\n| 3 | 1 | 3 | 5.0 |\n| 4 | 2 | 3 | 1.0 |\n| 5 | 3 | 4 | 3.0 |\n\n```python\n# Initialize and run Bellman-Ford\nbf = BellmanFord(edges_negative)\nshortest_paths = bf.run(vertex_idx=0)\nprint(\"Shortest paths:\", shortest_paths)\n```\n\n Shortest paths: [ 0. 1. -1. 0. 3.]\n\nThe Bellman-Ford algorithm finds the optimal path even with negative weights. In this example, the shortest path from node 0 to node 2 has length -1 (going 0\u21921\u21922 with weights 1 + (-2) = -1), which is shorter than the direct path 0\u21922 with weight 4.\n\n### Negative Cycle Detection\n\nBellman-Ford can also detect negative cycles, which indicate that no shortest path exists:\n\n```python\n# Create a graph with a negative cycle\nedges_cycle = pd.DataFrame({\n 'tail': [0, 1, 2],\n 'head': [1, 2, 0],\n 'weight': [1, -2, -1] # Cycle 0\u21921\u21922\u21920 has total weight -2\n})\n\nbf_cycle = BellmanFord(edges_cycle)\ntry:\n bf_cycle.run(vertex_idx=0)\nexcept ValueError as e:\n print(\"Error:\", e)\n```\n\n Error: Negative cycle detected in the graph\n\n## Installation\n\n### Standard Installation\n\n```bash\npip install edsger\n```\n\n## Why Use Edsger?\n\nEdsger 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:\n\n<img src=\"https://raw.githubusercontent.com/aetperf/edsger/release/docs/source/assets/dijkstra_benchmark_comparison.png\" alt=\"Dijkstra Performance Comparison\" width=\"700\">\n\n## Contributing\n\nWe 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).\n\n## License\n\nEdsger is licensed under the MIT License. See the LICENSE file for more details.\n\n## Contact\n\nFor any questions or inquiries, please contact Fran\u00e7ois Pacull at [francois.pacull@architecture-performance.fr](mailto:francois.pacull@architecture-performance.fr).\n",
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