<jupyter_start><jupyter_text>Distribuciones continuas más utilizadas<jupyter_code>import numpy as np
import pandas as pd

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline<jupyter_output><empty_output><jupyter_text>Distribución Exponencial<jupyter_code>from scipy.stats import expon
data = expon.rvs(size = 1000000)
len(data)
mean, var, skew, kurt = expon.stats(moments = 'mvsk')
(mean, var, skew, kurt)
axis = sns.distplot(data, bins = 50, kde = False, color = 'blue')
axis.set(xlabel = 'Distribución exponencial', ylabel = 'Frecuencia')
axis.axvline(x = mean, linestyle = ('--'), label = 'Media')
axis.legend()<jupyter_output><empty_output><jupyter_text>Distribución Normal<jupyter_code>from scipy.stats import norm
data = norm.rvs(size = 1000000)
len(data)
mean, var, skew, kurt = norm.stats(moments = 'mvsk')
(mean, var, skew, kurt)
axis = sns.distplot(data, bins = 50, kde = False, color = 'blue')
axis.set(xlabel = 'Distribución Normal Estándar', ylabel = 'Frecuencia')
axis.axvline(x = mean, linestyle = ('--'), label = 'Media')
axis.legend()<jupyter_output><empty_output><jupyter_text>Distribución Uniforme<jupyter_code>from scipy.stats import uniform
data = uniform.rvs(size = 1000000)
len(data)
mean, var, skew, kurt = uniform.stats(moments = 'mvsk')
(mean, var, skew, kurt)
axis = sns.distplot(data, bins = 50, kde = False, color = 'blue')
axis.set(xlabel = 'Distribución Uniforme', ylabel = 'Frecuencia')
axis.axvline(x = mean, linestyle = ('--'), label = 'Media')
axis.legend()<jupyter_output><empty_output>
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