numpyrandomnormal

numpy.random.normal is a function in the NumPy library that generates random numbers from a normal distribution. The function is typically invoked as random.normal, with the distribution parameters being specified as arguments.

The function generates random samples from a normal (Gaussian) distribution, which is a type of probability distribution that is symmetric about the mean. The mean of the distribution is specified by the loc argument, which is the central value of the distribution. The scale parameter, specified by the sigma argument, is the standard deviation of the distribution. Both loc and sigma are required arguments.

The array-like produced by random.normal contains samples from the normal distribution, with the shape given by the output array. By default, the output array is of shape (n, d), where n is the number of samples to draw and d is the number of elements per sample.

Seed intelligently is controlled by the seed argument, with default values deriving from the current global generator state. The random generator state can be accessed through the RandomState instance that is returned by the function. The noising can be performed using random.normal by using the size parameter to set the output array dimensions.

The normal distribution is typically characterized by its mean and standard deviation. The sigma argument in the random.normal function can be used to scale the distribution by its standard deviation. For example, if a distribution has a sigma of 1, any value outside of plus or minus in one standard deviation from the mean is considered an outlier.

The numpy.random.normal function is used in a variety of statistical and computational applications, including data analysis, machine learning, and simulations.