Python: Real-World Data Science

NumPy random numbers

An important part of any simulation is the ability to generate random numbers. For this purpose, NumPy provides various routines in the submodule random. It uses a particular algorithm, called the Mersenne Twister, to generate pseudorandom numbers.

First, we need to define a seed that makes the random numbers predictable. When the value is reset, the same numbers will appear every time. If we do not assign the seed, NumPy automatically selects a random seed value based on the system's random number generator device or on the clock:

>>> np.random.seed(20)

An array of random numbers in the [0.0, 1.0] interval can be generated as follows:

>>> np.random.rand(5)
array([0.5881308, 0.89771373, 0.89153073, 0.81583748, 
 0.03588959]) ...