Generating correlation matrix with Matplotlib and Seaborn

I have some data in Excel that I wanted to generate a graphical correlation matrix in Python. I looked in the Seaborn documentation and found a an example code on the documentation page, but my data that I want to compute the correlation I put each one of them in a list, in case the lists A, B, C, D and E of my code.

But my code is not running, I do not know if it would be the case to calculate the direct correlation in the Excel table without having to put each column in a list?

Also in the documentation code I did not understand the first line from string import ascii_letters . When I get the code straight from the documentation page it runs but it does not generate any graphics.

In the end, my code looks like this.

# Importando Bibliotecas

from string import ascii_letters
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

#Criação das Listas nas variaveis a,b,c,d,e a partir do Arquivo do Excel

a = []
b = []
c = []
d = []
e = []

dataset = open ("Teste_Plotagem.csv","r")

for line in dataset:
    line = line.strip()
    A,B,C,D,E = line.split(";")
    a.append(A)
    b.append(B)
    c.append(C)
    d.append(D)
    e.append(E)

dataset.close()

# Tentando Fazer a correlação entre as listas

sns.set(style="white")

corr = cor(a,b,c,d,e)

# Gerando a mascara da diagonal superior da matriz de correlação
# Daqui para baixo eu peguei do código da documentação

mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

# Definindo a plotagem no matplotlib
f, ax = plt.subplots(figsize=(11, 9))

# Gerando o mapa de cores a partir do seaborn
cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Desenhando o heatmap com a mascara
sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,
            square=True, linewidths=.5, cbar_kws={"shrink": .5})