Python 3 Script Kruskal’s Minimum Spanning Tree Algorithm Full Example Project For Beginners

Python 3 Script Kruskal’s Minimum Spanning Tree Algorithm Full Example Project For Beginners


Welcome folks today in this blog post we will be implementing kruskal's minium spanning tree algorithm in python. All the full source code of the application is shown below.





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In order to get started you need to make an file and copy paste the following code



# Python program for Kruskal's algorithm to find
# Minimum Spanning Tree of a given connected,
# undirected and weighted graph

from collections import defaultdict

# Class to represent a graph

class Graph:

    def __init__(self, vertices):
        self.V = vertices # No. of vertices
        self.graph = [] # default dictionary
        # to store graph

    # function to add an edge to graph
    def addEdge(self, u, v, w):
        self.graph.append([u, v, w])

    # A utility function to find set of an element i
    # (uses path compression technique)
    def find(self, parent, i):
        if parent[i] == i:
            return i
        return self.find(parent, parent[i])

    # A function that does union of two sets of x and y
    # (uses union by rank)
    def union(self, parent, rank, x, y):
        xroot = self.find(parent, x)
        yroot = self.find(parent, y)

        # Attach smaller rank tree under root of
        # high rank tree (Union by Rank)
        if rank[xroot] < rank[yroot]:
            parent[xroot] = yroot
        elif rank[xroot] > rank[yroot]:
            parent[yroot] = xroot

        # If ranks are same, then make one as root
        # and increment its rank by one
            parent[yroot] = xroot
            rank[xroot] += 1

    # The main function to construct MST using Kruskal's
        # algorithm
    def KruskalMST(self):

        result = [] # This will store the resultant MST
        # An index variable, used for sorted edges
        i = 0
        # An index variable, used for result[]
        e = 0

        # Step 1: Sort all the edges in
        # non-decreasing order of their
        # weight. If we are not allowed to change the
        # given graph, we can create a copy of graph
        self.graph = sorted(self.graph,
                            key=lambda item: item[2])

        parent = []
        rank = []

        # Create V subsets with single elements
        for node in range(self.V):

        # Number of edges to be taken is equal to V-1
        while e < self.V - 1:

            # Step 2: Pick the smallest edge and increment
            # the index for next iteration
            u, v, w = self.graph[i]
            i = i + 1
            x = self.find(parent, u)
            y = self.find(parent, v)

            # If including this edge does't
            # cause cycle, include it in result
            # and increment the indexof result
            # for next edge
            if x != y:
                e = e + 1
                result.append([u, v, w])
                self.union(parent, rank, x, y)
            # Else discard the edge

        minimumCost = 0
        print ("Edges in the constructed MST")
        for u, v, weight in result:
            minimumCost += weight
            print("%d -- %d == %d" % (u, v, weight))
        print("Minimum Spanning Tree" , minimumCost)

# Driver code
g = Graph(4)
g.addEdge(0, 1, 10)
g.addEdge(0, 2, 6)
g.addEdge(0, 3, 5)
g.addEdge(1, 3, 15)
g.addEdge(2, 3, 4)

# Function call

# This code is contributed by Neelam Yadav


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Now if you execute the python script by typing the below command as shown below






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