生成一些gamma数据：

import scipy.stats as stats    
alpha = 5
loc = 100.5
beta = 22
data = stats.gamma.rvs(alpha, loc=loc, scale=beta, size=10000)    
print(data)
# [ 202.36035683  297.23906376  249.53831795 ...,  271.85204096  180.75026301
#   364.60240242]

在这里，我们将数据拟合到伽马分布：

fit_alpha, fit_loc, fit_beta=stats.gamma.fit(data)
print(fit_alpha, fit_loc, fit_beta)
# (5.0833692504230008, 100.08697963283467, 21.739518937816108)
print(alpha, loc, beta)
# (5, 100.5, 22)

我对ss.gamma.rvs-函数不满意，因为它可以生成负数，这是伽马分布不应该有的。因此，我通过期望值=均值(Data)和方差= var(data) (有关详细信息，请参阅维基百科)对样本进行拟合，并编写了一个函数，该函数可以在没有scipy的情况下生成伽马分布的随机样本(我发现很难在附注中正确安装它)：

import random
import numpy
data = [6176, 11046, 670, 6146, 7945, 6864, 767, 7623, 7212, 9040, 3213, 6302, 10044, 10195, 9386, 7230, 4602, 6282, 8619, 7903, 6318, 13294, 6990, 5515, 9157]
# Fit gamma distribution through mean and average
mean_of_distribution = numpy.mean(data)
variance_of_distribution = numpy.var(data)
def gamma_random_sample(mean, variance, size):
    """Yields a list of random numbers following a gamma distribution defined by mean and variance"""
    g_alpha = mean*mean/variance
    g_beta = mean/variance
    for i in range(size):
        yield random.gammavariate(g_alpha,1/g_beta)
# force integer values to get integer sample
grs = [int(i) for i in gamma_random_sample(mean_of_distribution,variance_of_distribution,len(data))]