Finding the shortest path in a weighted DAG with Dijkstra in Python and heapq

shortestPath.py

import collections
import heapq

def shortestPath(edges, source, sink):
    # create a weighted DAG - {node:[(cost,neighbour), ...]}
    graph = collections.defaultdict(list)
    for l, r, c in edges:
        graph[l].append((c,r))
    # create a priority queue and hash set to store visited nodes
    queue, visited = [(0, source, [])], set()
    heapq.heapify(queue)
    # traverse graph with BFS
    while queue:
        (cost, node, path) = heapq.heappop(queue)
        # visit the node if it was not visited before
        if node not in visited:
            visited.add(node)
            path = path + [node]
            # hit the sink
            if node == sink:
                return (cost, path)
            # visit neighbours
            for c, neighbour in graph[node]:
                if neighbour not in visited:
                    heapq.heappush(queue, (cost+c, neighbour, path))
    return float("inf")

if __name__ == "__main__":
    edges = [
        ("A", "B", 7),
        ("A", "D", 5),
        ("B", "C", 8),
        ("B", "D", 9),
        ("B", "E", 7),
        ("C", "E", 5),
        ("D", "E", 15),
        ("D", "F", 6),
        ("E", "F", 8),
        ("E", "G", 9),
        ("F", "G", 11)
    ]

    print ("Find the shortest path with Dijkstra")
    print (edges)
    print ("A -> E:")
    print (shortestPath(edges, "A", "E"))

This is great,,, Thanks man