Write a Python program using Scikit-learn to split the iris dataset into 80% train data and 20% test data

# Import necessary modules 
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier 
from sklearn.model_selection import train_test_split  
iris = pd.read_csv("iris.csv")
#Drop id column
iris = iris.drop('Id',axis=1)
X = iris.iloc[:, :-1].values
y = iris.iloc[:, 4].values
#Split arrays or matrices into train and test subsets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20) 
knn = KNeighborsClassifier(n_neighbors=7)  
knn.fit(X_train, y_train)   
# Calculate the accuracy of the model for different values of k
for i in np.arange(1, 10):
    knn2 = KNeighborsClassifier(n_neighbors=i)
    knn2.fit(X_train, y_train)
    print("For k = %d accuracy is"%i,knn2.score(X_test,y_test))