Multiple random.sample in numpy

I have a list of vectors:

>>> import numpy as np
>>> num_dim, num_data = 10, 5
>>> data = np.random.rand(num_data, num_dim)
>>> data
array([[ 0.0498063 ,  0.18659463,  0.30563225,  0.99681495,  0.35692358,
         0.47759707,  0.85755606,  0.39373145,  0.54677259,  0.5168117 ],
       [ 0.18034536,  0.25935541,  0.79718771,  0.28604057,  0.17165293,
         0.90277904,  0.94016733,  0.15689765,  0.79758063,  0.41250143],
       [ 0.80716045,  0.84998745,  0.17893211,  0.36206016,  0.69604008,
         0.27249491,  0.92570247,  0.446499  ,  0.34424945,  0.08576628],
       [ 0.35311449,  0.67901964,  0.71023927,  0.03120829,  0.72864953,
         0.60717032,  0.8020118 ,  0.36047207,  0.46362718,  0.12441942],
       [ 0.1955419 ,  0.02702753,  0.76828842,  0.5438226 ,  0.69407709,
         0.20865243,  0.12783666,  0.81486189,  0.95583274,  0.30157658]])

From the data, I need to randomly pick 3 vectors, I could do it with:

>>> import random
>>> random.sample(data, 3)
[array([ 0.80716045,  0.84998745,  0.17893211,  0.36206016,  0.69604008,
        0.27249491,  0.92570247,  0.446499  ,  0.34424945,  0.08576628]), array([ 0.18034536,  0.25935541,  0.79718771,  0.28604057,  0.17165293,
        0.90277904,  0.94016733,  0.15689765,  0.79758063,  0.41250143]), array([ 0.35311449,  0.67901964,  0.71023927,  0.03120829,  0.72864953,
        0.60717032,  0.8020118 ,  0.36047207,  0.46362718,  0.12441942])]

I've checked the docs at http://ift.tt/1knVTH4 and I couldn't figure out whether there is such a functionality in numpy as random.sample().

Is it right that the numpy.random.sample() isn't the same as random.sample()?

Is there an equivalence of random.sample() in numpy?