The weibullvariate function in Python’s random module returns a random floating-point number based on the Weibull distribution. This function is useful for generating random numbers that follow a Weibull distribution, which is often used in reliability engineering and failure analysis.
The paretovariate function in Python’s random module returns a random floating-point number based on a Pareto distribution. This function is useful for generating random numbers that follow a Pareto distribution, which is commonly used in economics, finance, and various natural phenomena.
The vonmisesvariate function in Python’s random module returns a random floating-point number based on the von Mises distribution. This function is useful for generating random angles, which are often used in directional statistics and circular data analysis.
The normalvariate function in Python’s random module returns a random floating-point number based on a normal (Gaussian) distribution. This function is useful for generating random numbers that follow a normal distribution, which is common in statistics and various scientific fields.
The lognormvariate function in Python’s random module returns a random floating-point number based on a log-normal distribution. This function is useful for generating random numbers that follow a log-normal distribution, which is common in financial modeling and various natural phenomena.
The gammavariate function in Python’s random module returns a random floating-point number based on the Gamma distribution. This function is useful for generating random numbers that follow a Gamma distribution, which is often used in statistics, finance, and various simulations.
The betavariate function in Python’s random module returns a random floating-point number following a Beta distribution. This function is useful for generating random numbers that follow a Beta distribution, which is commonly used in Bayesian statistics and various simulations.
The triangular function in Python’s random module returns a random floating-point number within a specified range, with a triangular distribution. This function is useful for simulations and modeling scenarios where values are more likely to be near a central value.
The uniform function in Python’s random module returns a random floating-point number between two given numbers. This function is useful when you need to generate a random decimal number within a specific range.
The shuffle function in Python’s random module shuffles the elements of a list in place, meaning the original list is modified. This function is useful for randomizing the order of elements in a list.
The choices function in Python’s random module returns a list of elements chosen randomly from a given sequence. Unlike choice, which selects a single element, choices can return multiple elements and allows for weighted random selection.
The choice function in Python’s random module returns a randomly selected element from a non-empty sequence, such as a list, tuple, or string. This function is useful when you need to select a random item from a collection.
The randrange function in Python’s random module returns a randomly selected element from the specified range. This function is versatile and allows for generating random numbers with various step values.
The randint function in Python’s random module returns a random integer within a specified range. This function is useful when you need to generate a random whole number between two given values, inclusive.
The setstate function in Python’s random module sets the internal state of the random number generator. This method is useful when you want to restore the state of the generator to reproduce a sequence of random numbers.
The getstate function in Python’s random module returns the internal state of the random number generator. This function is useful when you need to save the state of the generator to reproduce the sequence of random numbers later.
The seed function in Python’s random module sets the starting point for generating random numbers. This is helpful when you want to produce the same sequence of random numbers each time you run your code.