The covariance matrix explores the linear relationship between variables. For more information, see this demo as well. Here we will try to display the covariance as a matrix of shades, setting the value for each shade to observe the relative differences.

```
import matplotlib.pyplot as plt
import numpy as np
A = np.array([
[3,25],
[4,20],
[5,15],
[6,10],
[7, 5]
])
B = np.array([
[10,2],
[12,5],
[15,8],
[19,11],
[24,14]
])
C = np.array([
[2.1, 5],
[2.2, 15],
[2.3, 25],
[2.2, 15],
[1.1, 5]
])
for matrix in [A, B, C]:
cov = np.cov(matrix)
xdim, ydim = cov.shape
plt.matshow(cov, cmap=plt.cm.gray)
for x in range(xdim):
for y in range(ydim):
plt.annotate(cov[x,y], xy=(x,y), xytext=(x-0.35, y+0.1), \
color='#13ffd3', fontsize=14, fontweight='bold')
plt.axis('off')
plt.show()
```

This leads to the following covariance matrices for A, B and C with lighter boxes indicating a stronger relationship.