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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Analisis de Redes
Step1:  Ejercicio 2
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Python Code:
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stas
%matplotlib inline
x = np.arange(0.01, 1, 0.01)
values = [(0.5, 0.5),(5, 1),(1, 3),(2, 2),(2, 5)]
for i, j in values:
    y = stas.beta.pdf(x,i,j)
    plt.plot(x,y)
plt.show()
Explanation: Analisis de Redes: Repaso Estadistico
Ejercicio 1: Hacer este gŕafico en Python.
End of explanation
md = []
mn = []
mo = []
kur = []
ske = []
for i, j in values:
    r = stas.beta.rvs(i, j, size=1000000)
    md.append(np.median(r))
    mn.append(np.mean(r))
    mo.append(stas.mode(r)[0][0])
    kur.append(stas.kurtosis(r))
    ske.append(stas.skew(r))
fig = plt.figure()
ax1 = fig.add_subplot(151)
ax1.set_title('Median')
ax1.plot(md)
ax2 = fig.add_subplot(152)
ax2.set_title('Mean')
ax2.plot(mn)
ax3 = fig.add_subplot(153)
ax3.set_title('Mode')
ax3.plot(mo)
ax4 = fig.add_subplot(154)
ax4.set_title('Kurtosis')
ax4.plot(kur)
ax5 = fig.add_subplot(155)
ax5.set_title('Skewness')
ax5.plot(ske)
axes = [ax1, ax2, ax3, ax4, ax5]
for i in axes:
    plt.setp(i.get_xticklabels(), visible=False)
    plt.setp(i.get_yticklabels(), visible=False)
Explanation: Ejercicio 2: Con datos aleatorios de distribuciones beta, obtener y graficar sus propiedades descriptivas.
End of explanation
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