How to implement Bellman Ford algorithm using Python to find optimal data packet routing path?

Question

The input data file is coordinates(nodes). The distances between them are calculated and matched against a table to find transmission rates. The goal is to find optimal routing path to either of 2 stations. That is,a path with maximum transmission rate while minimizing total number of links. What will be the code to implement Bellman Ford algorithm for this ? start nodes/vertices, links/edges, and transmission rates are stored as excel file.

    df = pd.read_csv ('Trans_rates_complete.csv')

class Graph:
    def __init__(self, vertices):
        self.vertices = vertices
        self.edges = []

    def add_edge(self, u, v, weight):
        self.edges.append((u, v, weight))


class BellmanFord:
    def __init__(self, graph, source):
        self.graph = graph
        self.source = source
        self.distances = {vertex: float('inf') for vertex in graph.vertices}
        self.distances[source] = 0

    def run(self):
        for i in range(len(self.graph.vertices) - 1):
            for u, v, weight in self.graph.edges:
                if self.distances[u] + weight < self.distances[v]:
                    self.distances[v] = self.distances[u] + weight

        for u, v, weight in self.graph.edges:
            if self.distances[u] + weight < self.distances[v]:
                print("Negative cycle detected")
                return

        print("Shortest distances:", self.distances)

    def get_shortest_path(self, destination):
        path = []
        while destination != self.source:
            path.append(destination) 
            destination = self.predecessors[destination]
        path.append(self.source)
        return path[::-1]



i=0
vertices=[]
graph=[]
while i<len(df):
    vertices.append(df.start_car)
    u=df.start_car[i]
    v=df.end_car[i]
    weight=df.trans_rate[i]
    graph.append([u,v,weight])
    i=i+1
vertices.append('BS1')
vertices.append('BS2')
graph=Graph(vertices)
bf = BellmanFord(graph, 0)
bf.run()
print("Shortest path:", bf.get_shortest_path('BS1'))