Erreur de code python

Bonsoir;
je veux calculer le nombre de cluster sur une matrice de similarité,
j'ai utilisé ce code, mais j'ai une erreur d'execution à la ligne 74,
erreur:
TypeError: '(slice(None, None, None), 0)' is an invalid key

# -*- coding: utf-8 -*-
"""
===================================
Demo of DBSCAN clustering algorithm
===================================

Finds core samples of high density and expands clusters from them.

"""
print(__doc__)

import numpy as np
import pandas as pd 
from sklearn.cluster import DBSCAN
from sklearn import metrics
#from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

X=pd.read_csv("C:/Users/click/Desktop/doctorat/rfiddd/fichier trouvé resultats/2020-07-22/lev_173.csv", sep=";")
# #############################################################################
# Generate sample data
#centers = [[1, 1], [-1, -1], [1, -1]]
#X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,
                           # random_state=0)



#X = StandardScaler().fit_transform(X)

# #############################################################################
# Compute DBSCAN
db = DBSCAN(eps=15, min_samples=2).fit(X)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print(labels)
n_noise_ = list(labels).count(-1)

print('Estimated number of clusters: %d' % n_clusters_)
print('Estimated number of noise points: %d' % n_noise_)
#print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
#print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
#print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
#print("Adjusted Rand Index: %0.3f"
#      % metrics.adjusted_rand_score(labels_true, labels))
#print("Adjusted Mutual Information: %0.3f"
#      % metrics.adjusted_mutual_info_score(labels_true, labels))
#print("Silhouette Coefficient: %0.3f"
#      % metrics.silhouette_score(X, labels))

# #############################################################################
# Plot result
import matplotlib.pyplot as plt

# Black removed and is used for noise instead.
unique_labels = set(labels)
#colors = [plt.cm.Spectral(each)
#          for each in np.linspace(0, 1, len(unique_labels))]

colors=['y','b','g','r']
print (colors)
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
#        col = [0, 0, 0, 1]
         col='k'

    class_member_mask = (labels == k)

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=6)

    xy = X[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
             markeredgecolor='k', markersize=6)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

merci d'avance pour toute aide.