scipy.stats.gengamma () — it is a generalized gamma continuous random variable that is defined by a standard format and some form parameters to complete its specification.

Parameters:
– & gt; q: lower and upper tail probability
– & gt; x: quantiles
– & gt; loc: [optional] location parameter. Default = 0
– & gt; scale: [optional] scale parameter. Default = 1
– & gt; size: [tuple of ints, optional] shape or random variates.
– & gt; a, b: shape parameters
– & gt; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: generalized gamma continuous random variable

Code # 1: Generating a generalized gamma -continuous random variable

from scipy.stats import gengamma    numargs = gengamma .numargs [a, b] = [ 0.7 ,] * numargs rv = gengamma (a, b)    print ( "RV:" , rv)

Output:

 RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A2A8BE0 & gt; 

Code # 2: Generalized Gamma Random Variables and Probability Distribution

import numpy as np quantile = np.arange ( 0.01 , 1 , 0.1 )   # Random Variants R = gengamma.rvs (a, b, scale = 2 , size = 10 ) print ( " Random Variates: " , R)   # PDF R = gengamma.pdf (a, b, quantile, loc = 0 , scale = 1 ) print ( "Probability Distribution:" , R)

Output:

 Random Variates: [1.28899567e-01 6.07031120 e-06 7.58807426e-01 1.02689244e + 00 2.75752340e-02 8.07943863e-03 4.69774065e-01 2.48110421e-01 4.64544740e-01 7.04892852e + 00] Probability Distribution: [0.004053 0.04502864 0.08671695 0.12897998 0.17168235 0.21469197 0.25788056 0.30112428 0.34430406 0.38730608] 

Code # 3: Graphic representation.

import numpy as np import matplotlib.pyplot as plt   distribution = np.linspace ( 0 , np.minimum (rv .dist.b, 3 )) print ( "Distribution:" , distribution)   plot = plt.plot (distribution, rv.pdf (distribution))

Output:

 Distribution: [0. 0.06122449 0.12244898 0.18367347 0.24489796 0.30612245 0.36734694 0.42857143 0.48979592 0.55102041 0.6122449 0.67346939 0.73469388 0.79591837 0.85714286 0.91836735 0.97959184 1.04081633 1.10204082 1.16326531 1.2244898 1.28571429 1.34693878 1.40816327 1.46938776 1.53061224 1.59183673 1.65306122 1.71428571 1.7755102 1.83673469 1.89795918 1.95918367 2.02040816 2.08163265 2.14285714 2.20408163 2.26530612 2.32653061 2.3877551 2.44897959 2.51020408 2.57142857 2.63265306 2.69387755 2.75510204 2.81632653 2.87755102 2.93877551 3. ] 

Code # 4: Various Positional Arguments

import matplotlib. pyplot as plt import numpy as np   x = np.linspace ( 0 , 5 , 100 )   # Various positional arguments y1 = gengamma.pdf (x, a, 1 , 3 ) y2 = gengamma.pdf ( x, a, 1 , 4 ) plt.plot (x, y1, " * " , x, y2, " r-- " )

Output: