# scipy stats.f () | python

NumPy | Python Methods and Functions

scipy.stats.f () — it is a continuous random variable F that is defined with a standard format and some shape parameters to complete its specification.

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

Results: F continuous random variable

Code # 1: Create F continuous random variable values ​​

 ` from ` ` scipy.stats ` ` import ` ` f `   ` numargs ` ` = ` ` f.numargs ` ` [a, b] ` ` = ` ` [` ` 0.6 ` `,] ` ` * ` ` numargs ` ` rv ` ` = ` ` f (a, b) `   ` print ` ` (` ` "RV:" ` `, rv) `

Output:

` RV: & lt; scipy.stats. _distn_infrastructure.rv_frozen object at 0x0000018D566864A8 & gt; `

Code # 2: exponential F of random variables and probability distribution.

` `

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

` `

Output:

` Random Variates: [2.77609532e + 00 2.55454726e- 04 7.77303742e + 01 2.61642158e + 02 3.39772973e-01 8.63437666e + 02 3.24316832e + 02 5.88915362e + 06 1.27105242e + 03 7.30691909e-01] Probability Distribution: [0.00800042 0.06746857 0.10587056 0.13291306 0 .15295841 0.16837285 0.18056559 0.19043041 0.19856155 0.2053691] `

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 ` ` = ` ` f .pdf (x, ` ` 2 ` `, ` ` 6 ` `) ` ` y2 ` ` = ` ` f .pdf (x , ` ` 1 ` `, ` ` 4 ` `) ` ` plt.plot (x, y1, ` `" * "` , x, y2, ` "r--" ` `) `

Output: