Plotting Correlation Matrix in Three Ways Using Pandas, Matplotlib and Seaborn

Suppose we have a correlation matrix for comparison of results from various outlier detection algorithms as follows:
This correlation matrix was generated in this post.


               ae     a_knn     cblof        fb      hbos        if       knn  \
ae       1.000000 -0.002106  0.292843  0.005122  0.494888  0.466024  0.216526   
a_knn   -0.002106  1.000000  0.206014 -0.002029  0.067267  0.206014  0.230566   
cblof    0.292843  0.206014  1.000000 -0.009849  0.134093  0.437160  0.490545   
fb       0.005122 -0.002029 -0.009849  1.000000 -0.009849 -0.009849 -0.008801   
hbos     0.494888  0.067267  0.134093 -0.009849  1.000000  0.350570  0.184289   
if       0.466024  0.206014  0.437160 -0.009849  0.350570  1.000000  0.409951   
knn      0.216526  0.230566  0.490545 -0.008801  0.184289  0.409951  1.000000   
lof     -0.009460 -0.001949  0.006117 -0.009112 -0.009460 -0.009460  0.008945   
loda     0.081817  0.062475  0.125143 -0.032479  0.072189  0.139584  0.158056   
m_knn    0.218000  0.269756  0.387356 -0.007522  0.142730  0.312087  0.749635   
mcd      0.278411 -0.002106  0.004207 -0.009849  0.321706  0.047502  0.006983   
mo_gaal -0.010225 -0.002106 -0.010225 -0.009849 -0.010225 -0.010225 -0.009136   
ocsvm    0.191820  0.206014  0.466024 -0.009849  0.177388  0.509319  0.667851   
pca      0.985568 -0.002106  0.307274  0.005122  0.494888  0.466024  0.216526   
so_gaal  0.004207 -0.002106 -0.010225 -0.009849  0.018639 -0.010225 -0.009136   
vae      1.000000 -0.002106  0.292843  0.005122  0.494888  0.466024  0.216526   

              lof      loda     m_knn       mcd   mo_gaal     ocsvm       pca  \
ae      -0.009460  0.081817  0.218000  0.278411 -0.010225  0.191820  0.985568   
a_knn   -0.001949  0.062475  0.269756 -0.002106 -0.002106  0.206014 -0.002106   
cblof    0.006117  0.125143  0.387356  0.004207 -0.010225  0.466024  0.307274   
fb      -0.009112 -0.032479 -0.007522 -0.009849 -0.009849 -0.009849  0.005122   
hbos    -0.009460  0.072189  0.142730  0.321706 -0.010225  0.177388  0.494888   
if      -0.009460  0.139584  0.312087  0.047502 -0.010225  0.509319  0.466024   
knn      0.008945  0.158056  0.749635  0.006983 -0.009136  0.667851  0.216526   
lof      1.000000  0.031157  0.013086 -0.009460 -0.009460  0.006117 -0.009460   
loda     0.031157  1.000000  0.118617  0.048120 -0.028903  0.235863  0.081817   
m_knn    0.013086  0.118617  1.000000  0.011009 -0.007809  0.537895  0.218000   
mcd     -0.009460  0.048120  0.011009  1.000000 -0.010225 -0.010225  0.278411   
mo_gaal -0.009460 -0.028903 -0.007809 -0.010225  1.000000 -0.010225 -0.010225   
ocsvm    0.006117  0.235863  0.537895 -0.010225 -0.010225  1.000000  0.206252   
pca     -0.009460  0.081817  0.218000  0.278411 -0.010225  0.206252  1.000000   
so_gaal -0.009460  0.028864 -0.007809  0.004207 -0.010225 -0.010225  0.004207   
vae     -0.009460  0.081817  0.218000  0.278411 -0.010225  0.191820  0.985568   

          so_gaal       vae  
ae       0.004207  1.000000  
a_knn   -0.002106 -0.002106  
cblof   -0.010225  0.292843  
fb      -0.009849  0.005122  
hbos     0.018639  0.494888  
if      -0.010225  0.466024  
knn     -0.009136  0.216526  
lof     -0.009460 -0.009460  
loda     0.028864  0.081817  
m_knn   -0.007809  0.218000  
mcd      0.004207  0.278411  
mo_gaal -0.010225 -0.010225  
ocsvm   -0.010225  0.191820  
pca      0.004207  0.985568  
so_gaal  1.000000  0.004207  
vae      0.004207  1.000000  

We will plot this correlation matrix in three ways using Pandas, Matplotlib and Seaborn.

# Imports
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

1: Pandas

cmap=sns.diverging_palette(0, 255, sep=1, n=256, as_cmap=True)
corr.style.background_gradient(cmap, axis=1).set_precision(2)

    
    
2: Matplotlib

plt.figure(figsize = (7,7))
plt.title("Correlation between results from various outlier detection algos", y=1.05)
plt.xticks(range(len(corr.columns)), corr.columns, rotation=70)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.imshow(corr, interpolation='nearest', cmap=sns.diverging_palette(0, 255, sep=1, n=256, as_cmap=True), vmin=-1, vmax=1) 
# cmap could also be set to: 'hot' or 'jet' or "YlGnBu" 
plt.colorbar()
plt.show()

    

# Ref: 1 and 2 

3: Seaborn

sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, cmap=sns.diverging_palette(0, 255, sep=1, n=256), vmin=-1, vmax=1)

    

References:
Ref 1: https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.imshow.html
Ref 2: https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.xticks