Random numbers are used in Monte Carlo methods, stochastic calculus, and more. Real random numbers are hard to generate, so in practice we use pseudo random numbers. Pseudo random numbers are random enough for most intents and purposes, except for some very special cases. The functions related to random numbers can be found in the NumPy random module. The core random number generator is based on the Mersenne Twister algorithm. Random numbers can be generated from discrete or continuous distributions. The distribution functions have an optional size parameter, which tells NumPy how many numbers to generate. You can specify either an integer or a tuple as size. This will result in an array filled with random numbers of appropriate shape. Discrete distributions include the geometric, hypergeometric, and binomial distributions.