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Python – Planck Discrete Distribution in Statistics
• Last Updated : 10 Jan, 2020

scipy.stats.planck() is a Planck discrete random variable. It is inherited from the of generic methods as an instance of the rv_discrete class. It completes the methods with details specific for this particular distribution.

Parameters :

x : quantiles
loc : [optional]location parameter. Default = 0
scale : [optional]scale parameter. Default = 1
moments : [optional] composed of letters [‘mvsk’]; ‘m’ = mean, ‘v’ = variance, ‘s’ = Fisher’s skew and ‘k’ = Fisher’s kurtosis. (default = ‘mv’).

Results : Planck discrete random variable

Code #1 : Creating Planck discrete random variable

 # importing library    from scipy.stats import planck       numargs = planck .numargs  a, b = 0.2, 0.8 rv = planck (a, b)       print ("RV : \n", rv)

Output :

RV :
scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4D865848

Code #2 : Planck discrete variates and probability distribution

 import numpy as np  quantile = np.arange (0.01, 1, 0.1)     # Random Variates  R = planck .rvs(a, b, size = 10)  print ("Random Variates : \n", R)     # PDF  x = np.linspace(planck.ppf(0.01, a, b),                 planck.ppf(0.99, a, b), 10) R = planck.ppf(x, 1, 3) print ("\nProbability Distribution : \n", R)

Output :

Random Variates :
[ 3  0  0 15  0  1  4  2  0  6]

Probability Distribution :
[ 4. nan nan nan nan nan nan nan nan nan]

Code #3 : Graphical Representation.

 import numpy as np  import matplotlib.pyplot as plt        distribution = np.linspace(0, np.minimum(rv.dist.b, 2))  print("Distribution : \n", distribution)        plot = plt.plot(distribution, rv.ppf(distribution))

Output :

Distribution :
[0.         0.04081633 0.08163265 0.12244898 0.16326531 0.20408163
0.24489796 0.28571429 0.32653061 0.36734694 0.40816327 0.44897959
0.48979592 0.53061224 0.57142857 0.6122449  0.65306122 0.69387755
0.73469388 0.7755102  0.81632653 0.85714286 0.89795918 0.93877551
0.97959184 1.02040816 1.06122449 1.10204082 1.14285714 1.18367347
1.2244898  1.26530612 1.30612245 1.34693878 1.3877551  1.42857143
1.46938776 1.51020408 1.55102041 1.59183673 1.63265306 1.67346939
1.71428571 1.75510204 1.79591837 1.83673469 1.87755102 1.91836735
1.95918367 2.        ]

Code #4 : Varying Positional Arguments

 import matplotlib.pyplot as plt  import numpy as np     x = np.linspace(0, 5, 100)        # Varying positional arguments  y1 = planck.ppf(x, a, b)  y2 = planck.pmf(x, a, b)  plt.plot(x, y1, "*", x, y2, "r--")

Output :

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