In [1]:

    

%matplotlib inline

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


    

In [17]:

    

centers = [[1,1,1],[3,3,3],[3.5,2,6],[5,5,5],[6,6,6]]
X_, _ = make_blobs(n_samples=400, n_features=2, centers=centers, cluster_std=0.6)

X = []
for i in range(len(X_)):
    if min(X_[i]) >= 0:
        X.append(X_[i])
X = np.array(X)

plt.scatter(X[:,0], X[:,1], s = 10)
plt.show()
# X.shape


    

In [25]:

    

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.scatter(X[:,0], X[:,1], X[:,2])


    

    
Out[25]:





<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x10feed358>






    

In [31]:

    

X[0][2]


    

    
Out[31]:





4.5728588948170703

In [30]:

    

X[:,2].dtype


    

    
Out[30]:





dtype('float64')

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