Random.py

The world's easiest, most powerful random function.

🔷 What's all the hullabaloo?

This script helps Python developers code randomness more simply, readably, and securely.

🌀: Fast implementation

python main.py

Ⓜ️: Other examples of using the random function

 random.seed([X], version=2)                    # Initialization of the random number generator. If X is not specified, the system time is used.
 random.getstate()                              # The internal state of the generator.
 random.setstate(state)                         # Restores the internal state of the generator. The state parameter must be obtained by the getstate () function.
 random.getrandbits(N)                          # Returns N random bits.
 random.randrange(start, stop, step)            # Returns a randomly selected number from the sequence.
 random.randint(A, B)                           # Random integer N, A ≤ N ≤ B.
 random.choice(sequence)                        # A random element of a non-empty sequence.
 random.random()                                # A random number from 0 to 1.
 random.uniform(A, B)                           # A random floating-point number, A ≤ N ≤ B (or B ≤ N ≤ A).
 random.sample(population, k)                   # A list of length k from the population sequence.
 random.triangular(low, high, mode)             # A random floating-point number, low ≤ N ≤ high. Mode-distribution.
 random.shuffle(sequence, [rand])               # Shuffles the sequence (the sequence itself changes). Therefore, the function does not work for immutable objects.
 random.betavariate(alpha, beta)                # Beta distribution. alpha>0, beta>0. Returns from 0 to 1.
 random.expovariate(lambd)                      # Exponential distribution. lambd is equal to 1/the desired average. The Lambd must be non-zero.
 random.gammavariate(alpha, beta)               # Gamma distribution. Conditions for the parameters alpha>0 and beta>0.
 random.gauss(value, standard deviation)        # The Gaussian distribution.
 random.lognormvariate(mu, sigma)               # The logarithm of the normal distribution.
 random.normalvariate(mu, sigma)                # Normal distribution. mu is the average value, sigma is the standard deviation.
 random.vonmisesvariate(mu, kappa)              # mu is the average angle expressed in radians from 0 to 2π, and kappa is the concentration parameter, which must be greater than or equal to zero.
 random.paretovariate(alpha)                    # Pareto distribution.
 random.weibullvariate(alpha, beta)             # The Weibull distribution.

🧢: Supporters