pca_python

pca = PCA(n_components=2)

X = StandardScaler().fit_transform(df_clustering_ready)
principal_components = pca.fit_transform(X)
df_pca = pd.DataFrame(data = principal_components,columns=['component1', 'component2'])

clust_opt = OPTICS(min_samples=2, xi=.05, min_cluster_size=.05, n_jobs=-1)
clust_opt.fit(X)
reachability = clust_opt.reachability_[clust_opt.ordering_]
labels_op = clust_opt.labels_[clust_opt.ordering_]
unique_labels_op = set(labels_op)
n_clusters_op = len(set(labels_op)) - (1 if -1 in labels_op else 0)
colors = [plt.cm.Spectral(each)
          for each in np.linspace(0, 1, len(labels_op))]
for k, col in zip(labels_op, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 1]

    class_member_mask = (labels_op == k)
    Rk = reachability[labels_op == k]
    #xy = X[class_member_mask & core_samples_mask]
    xy = df_pca[class_member_mask & core_samples_mask]
    plt.plot(xy['component1'], xy['component2'], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)

    #xy = X[class_member_mask & ~core_samples_mask]
    #xy = df_pca[class_member_mask & ~core_samples_mask]
    plt.plot(xy['component1'], xy['component2'], 'o', markerfacecolor=tuple(col),markeredgecolor='k', markersize=6)

plt.title('Estimated number of clusters: {} \nAutomatic Clustering OPTICS'.format(n_clusters_op))
plt.grid()
plt.legend()
plt.show()