graph = {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}


def bfs_shortest_path(graph, start, goal):
    # keep track of explored nodes
    explored = []
    # keep track of all the paths to be checked
    queue = [[start]]

    # return path if start is goal
    if start == goal:
        return "That was easy! Start = goal"

    # keeps looping until all possible paths have been checked
    while queue:
        # pop the first path from the queue
        path = queue.pop(0)
        # get the last node from the path
        node = path[-1]
        if node not in explored:
            neighbours = graph[node]
            # go through all neighbour nodes, construct a new path and
            # push it into the queue
            for neighbour in neighbours:
                new_path = list(path)
                new_path.append(neighbour)
                queue.append(new_path)
                # return path if neighbour is goal
                if neighbour == goal:
                    return new_path

            # mark node as explored
            explored.append(node)

    # in case there's no path between the 2 nodes
    return "So sorry, but a connecting path doesn't exist :("


bfs_shortest_path(graph, "G", "D")  # returns ['G', 'C', 'A', 'B', 'D']