import numpy as np
from math import sqrt
import warnings
import pandas as pd
import random
from collections import Counter

def k_nearest_neighbors(data,predict,k=3):
    if(len(data)>=k):
        warnings.warn('K is set to a value less than total voting groups')
    distances=[]
    for group in data:
        for features in data[group]:
            euclidean_distance=np.linalg.norm(np.array(features)-np.array(predict))
            distances.append([euclidean_distance,group])

    votes=[i[1] for i in sorted(distances)[:k]]


    vote_result=Counter(votes).most_common(1)[0][0] #now two zeroes meaning is clear
    return vote_result


df=pd.read_csv("breast-cancer-wisconsin.data.txt")
df.replace('?',-99999,inplace=True)
df.drop(['id'],1,inplace=True)

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

random.shuffle(full_data)


test_size=0.2
train_set={2:[],4:[]}  #dictionary of classes
test_set={2:[],4:[]}
train_data=full_data[:-int(test_size*len(full_data))]
test_data=full_data[-int(test_size*len(full_data)):]

#populating our dictionaries
for i in train_data:
    train_set[i[-1]].append(i[:-1])

for i in test_data:
    train_set[i[-1]].append(i[:-1])


correct=0
total=0

print('$')

for group in test_set:
    for datax in test_set[group]:
        print('$')   #this is not printing,loop not running
        vote=k_nearest_neighbors(train_set,datx,k=5)
        if group==vote:
            correct+=1
        total+=1

print('accuracy:',correct/total)

Traceback (most recent call last):
      File "my_k_near_neigh.py", line 67, in <module>
        print(c/t)
    ZeroDivisionError: division by zero