import matplotlib.pyplot as plt
from sklearn.datasets import load_wine
from sklearn.preprocessing import MinMaxScaler

wine = load_wine()

x = range(0, wine.data.shape[0],1)  # same as x = range(0, len(y),1)

scaler = MinMaxScaler()
scaler.fit(wine.data)
wine_scaled = scaler.transform(wine.data)

def plot(init_feature, end_feature, mark):
    plt.figure(figsize=(30,5))
    for i in range(init_feature,end_feature,1):
        plt.plot(x, wine_scaled[:,i], mark, label = wine.feature_names[i])
    plt.rcParams.update({'font.size':18});
    plt.legend(prop={'size':22}); plt.grid(); 
    plt.show() 
    
# Plot all the features
plot(0,4,'-')
plot(4,8,'o')
plot(8,12,'^')
