  # scipy stats.gamma () | python

NumPy | Python Methods and Functions

scipy.stats.gamma () — it is a continuous gamma variable random variable that is defined by the 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: 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: gamma continuous random variable

Code # 1: Create gamma continuous random variable random variable

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

Output:

` RV : & lt; scipy.stats._distn_infrastructure.rv_frozen object at 0x0000018D57997F60 & gt; `

Code # 2: Generic Gamma Random Variables.

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

Output:

``` Random Variates: [0.01601209 0.05164555 1.22072489 0.53476245 0.11529018 0.16966403 0.59198231 0.71995529 0.86063603 3.81492177] Probability Distribution: [0.001976910916 0.34020629 0.38910556 0.42939763 0.46081639 0.48344302] < / pre>   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 ` ` = ` ` gamma.pdf (x, a, ` ` 1 ` `, ` ` 3 ` ` ) `  ` y2 ` ` = ` ` gamma.pdf ( x, a, ` ` 1 ` `, ` ` 4 ` `) `  ` plt.plot (x, y1, ` ` "*" ` `, x, y2, ` ` "r--" ` `) `           Output: 