One4.py

# importing one hot encoder 
from sklearn from sklearn.preprocessing import OneHotEncoder
# creating one hot encoder object 
onehotencoder = OneHotEncoder()
#reshape the 1-D country array to 2-D as fit_transform expects 2-D and finally fit the object 
X = onehotencoder.fit_transform(data.Country.values.reshape(-1,1)).toarray()
#To add this back into the original dataframe 
dfOneHot = pd.DataFrame(X, columns = ["Country_"+str(int(i)) for i in range(data.shape[1])]) 
df = pd.concat([data, dfOneHot], axis=1)
#droping the country column 
df= df.drop(['Country'], axis=1) 
#printing to verify 
print(df.head())