Dictionary best data structure for train routes?

Question

So I\'ve been tasked with essentially reading in a file (notepad file) that has a bunch of train stops and the time it takes to get from one stop to another. For example it

Answer 1

I will go against the grain -- and say that a straight flat dict is not the best for this.

Let's say you have 100 stops and multiple routes that are non-alphabetical and non-numeric. Think the Paris subway:

Now try and use a straight Python dict to calculate the time between FDR and La Fourche? That involves two or more different routes and multiple options.

A tree or some form of graph is a better structure. A dict is fabulous for a 1 to 1 mapping; tree are better for a rich description of nodes that relate to each other. You would then use something like Dijkstra's Algorithm to navigate it.

Since a nested dict of dicts or dict of lists IS a graph, it is easy to come up with a recursive example:

def find_all_paths(graph, start, end, path=[]):
        path = path + [start]
        if start == end:
            return [path]
        if start not in graph:
            return []
        paths = []
        for node in graph[start]:
            if node not in path:
                newpaths = find_all_paths(graph, node, end, path)
                for newpath in newpaths:
                    paths.append(newpath)
        return paths       

def min_path(graph, start, end):
    paths=find_all_paths(graph,start,end)
    mt=10**99
    mpath=[]
    print '\tAll paths:',paths
    for path in paths:
        t=sum(graph[i][j] for i,j in zip(path,path[1::]))
        print '\t\tevaluating:',path, t
        if t{}:{}'.format(i,j,graph[i][j]) for i,j in zip(mpath,mpath[1::]))
    e2=str(sum(graph[i][j] for i,j in zip(mpath,mpath[1::])))
    print 'Best path: '+e1+'   Total: '+e2+'\n'  

if __name__ == "__main__":
    graph = {'A': {'B':5, 'C':4},
             'B': {'C':3, 'D':10},
             'C': {'D':12},
             'D': {'C':5, 'E':9},
             'E': {'F':8},
             'F': {'C':7}}
    min_path(graph,'A','E')
    min_path(graph,'A','D')
    min_path(graph,'A','F')

Prints:

    All paths: [['A', 'C', 'D', 'E'], ['A', 'B', 'C', 'D', 'E'], ['A', 'B', 'D', 'E']]
        evaluating: ['A', 'C', 'D', 'E'] 25
        evaluating: ['A', 'B', 'C', 'D', 'E'] 29
        evaluating: ['A', 'B', 'D', 'E'] 24
Best path: A->B:5 B->D:10 D->E:9   Total: 24

    All paths: [['A', 'C', 'D'], ['A', 'B', 'C', 'D'], ['A', 'B', 'D']]
        evaluating: ['A', 'C', 'D'] 16
        evaluating: ['A', 'B', 'C', 'D'] 20
        evaluating: ['A', 'B', 'D'] 15
Best path: A->B:5 B->D:10   Total: 15

    All paths: [['A', 'C', 'D', 'E', 'F'], ['A', 'B', 'C', 'D', 'E', 'F'], ['A', 'B', 'D', 'E', 'F']]
        evaluating: ['A', 'C', 'D', 'E', 'F'] 33
        evaluating: ['A', 'B', 'C', 'D', 'E', 'F'] 37
        evaluating: ['A', 'B', 'D', 'E', 'F'] 32
Best path: A->B:5 B->D:10 D->E:9 E->F:8   Total: 32