  # scipy stats.gengamma () | python

NumPy | Python Methods and Functions

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: