import matplotlib.pyplot as plt

from sklearn.decomposition import PCA

from sklearn.datasets import load_iris

data = load_iris()

y = data.target

X = data.data

pca = PCA(n_components=2)

reduced_X = pca.fit_transform(X)

red_x, red_y = [], []

blue_x, blue_y = [], []

green_x, green_y = [], []

for i in range(len(reduced_X)):

    if y[i] == 0:

        red_x.append(reduced_X[i][0])

        red_y.append(reduced_X[i][1])

    elif y[i] == 1:

        blue_x.append(reduced_X[i][0])

        blue_y.append(reduced_X[i][1])

    else:

        green_x.append(reduced_X[i][0])

        green_y.append(reduced_X[i][1])

plt.scatter(red_x, red_y, c='r', marker='x')

plt.scatter(blue_x, blue_y, c='b', marker='D')

plt.scatter(green_x, green_y, c='g', marker='.')

plt.show()