  # scipy stats.gompertz () | python

NumPy | Python Methods and Functions

> 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; moments: [optional] composed of letters [`mvsk`]; `m` = mean, `v` = variance, `s` = Fisher`s skew and `k` = Fisher`s kurtosis. (default = `mv`).

Results: Gompertz (or truncated Gumbel) continuous random variable

Code # 1 : Generating a continuous Gompertz (or truncated Gumbel) random variable

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

Output:

` RV: & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x000001E39A3E2470 & gt; `

Code # 2: Gompertz (or Truncated Gumbel) Random Variations and Probability Distribution

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

Output:

` Random Variates: [1.29938059 1.47547887 1.33324567 1.79424061 0.45304378 1.46222247 1.29260365 0.59989705 3.58467676 1.81226267] Probability Distribution: [0.01993441 0.19813875 0.34179784 0.45591617 0.54485437 0.61240 685 0.66187043 0.69610503 0.71758726 0.72845776] `

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 (distr ibution, 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 = gompertz.pdf (x, 1 , 3 ) y2 = gompertz.pdf (x , 1 , 4 ) plt.plot (x, y1, "*" , x, y2, "r--" ) `

Output: