Randomization

There are two relevant modules. random is for basic general purpose randomization; numpy.random is for more advanced randomization for scientific computing, statistics, machine learning, etc.

• Generate random integer:

    random.randint(a,b) # Returns a random integer in the range [a,b],
                        # including both end points.
  

• Basic random choice:

   random.choice(seq) # Choose a random element from a non-empty sequence.
  

• Basic sampling:

    random.sample(population, k) # return a new list containing k unique elements
                                 # chosen from the population sequence or set.
  

• Sampling with counts:

    sample(['red', 'blue'], counts=[4, 2], k=5) # equivalent to 
    sample(['red', 'red', 'red', 'red', 'blue', 'blue'], k=5)
  

• Mutating shuffle:

    random.shuffle(x) # Shuffle list x in place, and return None.
  

• Non-mutating shuffle:

    random.sample(x, len(x)) # Return a new shuffled list from the elements of x.
  

• Random float in \([0.0, 1.0)\):

    random.random() # Return the next random floating point number in the range [0.0, 1.0).