Media y desviacion estandar con python

\text{mean} = \mu = \frac{1}{n}\sum_{i = 1}^n x_i

\text{standard deviation} = \sigma = \sqrt{\frac{1}{n}\sum_{i = 1}^n (x_i - \mu)^2}

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#media de una lista
x = [3, 5, 7, 9, 11]
np.mean(x)

Out[ ]:

7.0

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#desviación estándar de una lista 
np.std(x)

Out[ ]:

2.8284271247461903

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#distribución Binominal con n = 20, p = 0.5  
b = stats.binom(20, 0.5)

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#media
b.mean()

Out[ ]:

10.0

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#gráfica
plt.bar(range(21), b.pmf(range(21)))

Out[ ]:

<BarContainer object of 21 artists>

In [ ]:

#otro ejemplo
#binomial n = 50, p = 0.3
ex2 = stats.binom(50, 0.3)
ex3 = stats.binom(20, 0.3)

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#media
ex2.mean()

Out[ ]:

15.0

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ex3.mean()

Out[ ]:

6.0

In [ ]:

ex3.std()

Out[ ]:

2.0493901531919194

In [ ]:

#desviación 
ex2.std()

Out[ ]:

3.24037034920393

In [ ]:

#gráfica
plt.bar(range(51), ex2.pmf(range(51)))
plt.bar(range(21), ex3.pmf(range(21)))

Out[ ]:

<BarContainer object of 21 artists>