x = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
    2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4,
    4, 4, 4, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 8, 8, 9, 9, 9,
    10, 10, 11, 12, 13, 14, 14, 15, 19, 19, 21, 23, 25, 27, 28, 30, 31, 36,
    41, 45, 48, 52, 55, 60, 68, 75, 86, 118, 159, 207, 354])
mu, sigma = stats.norm.fit(np.log(x))
mu, sigma
shape, loc, scale = stats.lognorm.fit(x, floc=0)
np.log(scale), shape
генерировать случайные логнормальные распределения, используя форму наблюдаемых данных