numpy Generating random data Generating random numbers drawn from specific distributions
Example
Draw samples from a normal (gaussian) distribution
# Generate 5 random numbers from a standard normal distribution
# (mean = 0, standard deviation = 1)
np.random.randn(5)
# Out: array([-0.84423086, 0.70564081, -0.39878617, -0.82719653, -0.4157447 ])
# This result can also be achieved with the more general np.random.normal
np.random.normal(0, 1, 5)
# Out: array([-0.84423086, 0.70564081, -0.39878617, -0.82719653, -0.4157447 ])
# Specify the distribution's parameters
# Generate 5 random numbers drawn from a normal distribution with mean=70, std=10
np.random.normal(70, 10, 5)
# Out: array([ 72.06498837, 65.43118674, 59.40024236, 76.14957316, 84.29660766])
There are several additional distributions available in numpy.random, for example poisson, binomial and logistic
np.random.poisson(2.5, 5) # 5 numbers, lambda=5
# Out: array([0, 2, 4, 3, 5])
np.random.binomial(4, 0.3, 5) # 5 numbers, n=4, p=0.3
# Out: array([1, 0, 2, 1, 0])
np.random.logistic(2.3, 1.2, 5) # 5 numbers, location=2.3, scale=1.2
# Out: array([ 1.23471936, 2.28598718, -0.81045893, 2.2474899 , 4.15836878])
