Btukfyl

 import numpy as np

 import matplotlib.pyplot as plt

 from matplotlib import style

 import pandas as pd 

 import time



 start_time = time.time()



 style.use('ggplot')



 class K_Means:

      def __init__(self, k =3, tolerance = 0.0001, max_iterations = 500):

      self.k = k

    self.tolerance = tolerance

    self.max_iterations = max_iterations



    def fit(self, data):



    self.centroids = {}



    #initialize the centroids, the first 'k' elements in the dataset will be our initial centroids

    for i in range(self.k):

        self.centroids[i] = data[i]



    #begin iterations

    for i in range(self.max_iterations):

        self.classes = {}

        for i in range(self.k):

            self.classes[i] = 



        #find the distance between the point and cluster; choose the nearest centroid

        for features in data:

            distances = [np.linalg.norm(features - self.centroids[centroid]) for centroid in self.centroids]

            classification = distances.index(min(distances))

            self.classes[classification].append(features)



        previous = dict(self.centroids)



        #average the cluster datapoints to re-calculate the centroids

        for classification in self.classes:

            self.centroids[classification] = np.average(self.classes[classification], axis = 0)



        isOptimal = True



        for centroid in self.centroids:



            original_centroid = previous[centroid]

            curr = self.centroids[centroid]



            if np.sum((curr - original_centroid)/original_centroid * 100.0) > self.tolerance:

                isOptimal = False



        #break out of the main loop if the results are optimal, ie. the centroids don't change their positions much(more than our tolerance)

        if isOptimal:

            break



def pred(self, data):

    distances = [np.linalg.norm(data - self.centroids[centroid]) for centroid in self.centroids]

    classification = distances.index(min(distances))

    return classification



def main():





#df = pd.read_csv(r"ipl.csv")

df = pd.read_csv(r"CustomerData4.csv",nrows=200)

#df = df[['one', 'two']]

df=df[['MRank','FRank','RRank']]

dataset = df.astype(float).values.tolist()

X = df.values 



#df 

dataset = df.astype(float).values.tolist()

X = df.values #returns a numpy array



km = K_Means(5)



km.fit(X)

#y_kmeansP=km.fit(X)



# Plotting starts here

colors = 10*["r", "g", "c", "b", "k"]

#prediction = pd.DataFrame(km.fit(X), columns=['predictions']).to_csv('prediction.csv')



for centroid in km.centroids:

    plt.scatter(km.centroids[centroid][0], km.centroids[centroid][1], s = 130, marker = "x")









for classification in km.classes:

    color = colors[classification]

    for features in km.classes[classification]:

        print(classification)

        df['Cluster'] = classification

        plt.scatter(features[0], features[1], color = color,s = 30)





df.to_csv("clusteringfromscrtach.csv")

#plt.show()

print("--- %s seconds ---" % (time.time() - start_time))



  if __name__ == "__main__":

     main()

This is the code for K Mean Clustering from scratch
I want to export my data frame with one column added which is cluster I used that line tdf['Cluster'] = classification to add new column named Cluster to my dataframe but it only added one cluster '4'
Other clusters are 0 1 2 3
Any solution to this problem

df['Cluster'] = classification

Obviously you are overwriting this column k times.

Instead, combine the results into one column.

Benchmark your code on larger data, too...