scipy.stats.randint() is a uniform 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 : uniform discrete random variable
Code #1 : Creating uniform discrete random variable
# importing library from scipy.stats import randint numargs = randint .numargs a, b = 0.2, 0.8rv = randint (a, b) print ("RV : \n", rv) |
Output :
RV : scipy.stats._distn_infrastructure.rv_frozen object at 0x0000016A4D865848
Code #2 : uniform discrete variates and probability distribution
import numpy as np quantile = np.arange (0.01, 1, 0.1) # Random Variates R = randint .rvs(a, b, size = 10) print ("Random Variates : \n", R) # PDF x = np.linspace(randint.ppf(0.01, a, b), randint.ppf(0.99, a, b), 10) R = randint.ppf(x, 1, 3) print ("\nProbability Distribution : \n", R) |
Output :
Random Variates : [ 3 0 0 15 0 1 4 2 0 6] Probability Distribution : [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
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 = randint.ppf(x, a, b) y2 = randint.pmf(x, a, b) plt.plot(x, y1, "*", x, y2, "r--") |
Output :
