How to use the statistical distributions of scipy and matplot via array

Question

Hello, I need to plot graphs of some statistical distributions using python3, so I checked in the scipy documentation, from there, after making some changes to my needs, I got the following code to perform a normal distribution:

valor = [1,2,3,4,5,6,3]

from scipy.stats import norm
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)

# Calculate a few first moments:

mean, var, skew, kurt = norm.stats(moments='mvsk')
print(norm.stats(moments='mvsk'))

# Display the probability density function (``pdf``):

x = np.linspace(norm.ppf(0.01), norm.ppf(0.99), 100)
print(norm.ppf(0.01))
ax.plot(x, norm.pdf(x),'r--', lw=5, alpha=0.6, label='norm pdf')

rv = norm()
ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')

vals = norm.ppf([0.001, 0.5, 0.999])
np.allclose([0.001, 0.5, 0.999], norm.cdf(vals))
r = valor

# And compare the histogram:

ax.hist(r, bins=len(valor), normed=True ,alpha=0.6, color='g', edgecolor='black', linewidth=1.2)
ax.legend(loc='best', frameon=False)

plt.savefig("templates/docNORMAL.png")
#plt.show()
plt.gcf().clear()

However I could only use the example with the numbers, when using a list with 100 random values it presents errors:

When he should look like this:

I need to know how to make a list work with the documentation examples so I can implement the others.

Answer 1

There is a mix in your CDF code with PDF with your manual values and normal values. Below is an example showing the CDF and PDF of normal random values and manually input values.

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np


fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(16,4)

# Valores manuais
valores = np.array([1,2,3,4,5,6,3,8,8,8])
valores = np.sort(valores)               # coloca em ordem

cum = np.cumsum(valores)                 # CDF e PDF não funcionam para distribuições discretas
pmf = cum / np.amax(cum)                 # normaliza e cria o PMF

val, count = np.unique(valores, return_counts=True)  # Como PDF não vale contamos os valores
count = count / np.amax(count)                       # normaliza-se para mostrar em seguida


ax1.plot(val,count, 'r--',label='Frequencia')
ax1.plot(valores, pmf,'b--',label='PMF')
ax1.hist(valores, bins=len(valores), normed=True ,alpha=0.6, color='g', edgecolor='black', linewidth=1.2)


# Valores normais
valores = norm.rvs(size=100) # pegamos de norm 100 valores aleatórios normais
valores = np.sort(valores)     # coloca em ordem

ax2.plot(valores, norm.pdf(valores),'r--',label='PDF')
ax2.plot(valores, norm.cdf(valores),'b--',label='CDF')
ax2.hist(valores, bins=len(valores), normed=True,alpha=0.6, color='g', edgecolor='black', linewidth=1.2)



plt.show()