from sklearn.datasets.samples_generator import make_blobs
import numpy as np
import matplotlib.pyplot as plt

#product 20 samples and divide them in 4 different types
X, label_true = make_blobs(n_samples=20,centers=4)
print("Data:{:}".format(X))
print("label_true:{:}".format(label_true))

#eliminate the repeated elements
labels=np.unique(label_true)
print("labels:{:}".format(labels))

#plot
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
colors = 'rgbycm'
for index,elem in enumerate(labels):
 position=label_true==elem
 print("position{:}:{:}".format(index,position))
 plt.scatter(X[position,0],X[position,1],label="cluster %d"%elem,color=colors[index%len(colors)])
plt.show()

Data:[[ 6.28987299 1.19041843]
 [ 2.12673463 -1.90647309]
 [-8.56276424 1.8136798 ]
 [ 2.42611937 -3.81970786]
 [ 1.83488662 -3.10733306]
 [ 6.28320138 -0.24840258]
 [-6.74802304 1.13642657]
 [ 2.21681643 6.28894411]
 [-7.16100601 0.04482262]
 [ 1.66858847 3.42225284]
 [ 3.19972789 4.58804196]
 [-7.37006942 0.57068008]
 [ 0.52465584 -2.68794047]
 [ 2.71075921 3.57281778]
 [ 5.99343237 0.0120798 ]
 [ 4.28307033 4.28727222]
 [ 0.73714246 -2.38643522]
 [ 5.58384782 -0.62066592]
 [-8.44295576 -0.05933983]
 [ 5.33991984 1.24833992]]
label_true:[0 2 1 2 2 0 1 3 1 3 3 1 2 3 0 3 2 0 1 0]
labels:[0 1 2 3]
position0:[ True False False False False True False False False False False False
 False False True False False True False True]
position1:[False False True False False False True False True False False True
 False False False False False False True False]
position2:[False True False True True False False False False False False False
 True False False False True False False False]
position3:[False False False False False False False True False True True False
 False True False True False False False False]

import numpy as np
a=np.empty(shape=[0,4], dtype=int)
a=np.append(a,[[1,2,3,4],[2,3,4,5],[7,8,9,10]],axis=0)
position=[True,False,True]
print(a)
print(a[position,3])

[[ 1 2 3 4]
 [ 2 3 4 5]
 [ 7 8 9 10]]
[ 4 10]