If the list is in random order, you can just take the first 50.

Otherwise, use

Copyimport random
random.sample(the_list, 50)

random.sample help text:

sample(self, population, k) method of random.Random instance
    Chooses k unique random elements from a population sequence.
    
    Returns a new list containing elements from the population while
    leaving the original population unchanged.  The resulting list is
    in selection order so that all sub-slices will also be valid random
    samples.  This allows raffle winners (the sample) to be partitioned
    into grand prize and second place winners (the subslices).
    
    Members of the population need not be hashable or unique.  If the
    population contains repeats, then each occurrence is a possible
    selection in the sample.
    
    To choose a sample in a range of integers, use xrange as an argument.
    This is especially fast and space efficient for sampling from a
    large population:   sample(xrange(10000000), 60)

Use random.choice():

import random

foo = ['a', 'b', 'c', 'd', 'e']
print(random.choice(foo))

For cryptographically secure random choices (e.g., for generating a passphrase from a wordlist), use secrets.choice():

import secrets

foo = ['battery', 'correct', 'horse', 'staple']
print(secrets.choice(foo))

secrets is new in Python 3.6. On older versions of Python you can use the random.SystemRandom class:

import random

secure_random = random.SystemRandom()
print(secure_random.choice(foo))

If your sequence is short enough that reading it into memory and randomly sorting it is acceptable, then a straightforward approach would be to just use random.shuffle:

import random
arr=[1,2,3,4]

# In-place shuffle
random.shuffle(arr)

# Take the first 2 elements of the now randomized array
print arr[0:2]
[1, 3]

Depending upon the type of your sequence, you may need to convert it to a list by calling list(your_sequence) on it, but this will work regardless of the types of the objects in your sequence.

Naturally, if you can't fit your sequence into memory or the memory or CPU requirements of this approach are too high for you, you will need to use a different solution.

As @ayhan confirmed, it can be done as such:

>>> data[np.random.choice(len(data), size=3, replace=False)]
array([[ 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 docs:

numpy.random.choice(a, size=None, replace=True, p=None)

Generates a random sample from a given 1-D array

The np.random.choice(data, size=3, replace=False) selects 3 elements from the list of indices of the data without replacement.

Then data[...] slices the index and retrieve the indices selected with np.random.choice.