2.8 The `random` module

The random module contains function for generating random samples from probability distributions:

random.binomial( $$$$ p,n $)$
The random.binomial( $$$$ p,n $) function returns a random sample from a binomial distribution with$ n $independent trials and a success probability$ p $.$ n $must be a real positive dimensionless integer.$ p $must be a dimensionless number in the range$ 0≤p≤1 $.$

random.chisq( $$$$ ν $)$
The random.chisq( $$$$ ν $) function returns a random sample from a$ χ $-squared distribution with$ ν $degrees of freedom, where$ ν $must be a real positive dimensionless integer.$

random.gaussian( $$$$ σ $)$
The random.gaussian( $$$$ σ $) function returns a random sample from a Gaussian (normal) distribution of standard deviation$ σ $and centred upon zero.$ σ $must be real, but may have any physical units. The returned random sample shares the physical units of$ σ $.$

random.lognormal( $$$$ ζ,σ $)$
The random.lognormal( $$$$ ζ,σ $) function returns a random sample from the log normal distribution centred on$ ζ $, and of width$ σ $.$ σ $must be a real positive dimensionless number.$ ζ $must be real, but may have any physical units. The returned random sample shares the physical units of$ ζ $.$

random.poisson( $$$$ n $)$
The random.poisson( $$$$ n $) function returns a random integer from a Poisson distribution with mean$ n $, where$ n $must be a real positive dimensionless number.$

random.random()
The random.random() function returns a random real number between 0 and 1.

random.tdist( $$$$ ν $)$
The random.tdist( $$$$ ν $) function returns a random sample from a$ t $-distribution with$ ν $degrees of freedom, where$ ν $must be a real positive dimensionless integer.$

2.8 The random module

2.8 The `random` module