<jupyter_start><jupyter_text>Method 1: Using inverse of matrix<jupyter_code>print(np.linalg.inv(z))
X = np.linalg.inv(z).dot(r)
print(X)<jupyter_output>[[-0.06282723  0.28795812 -0.19895288]
 [-0.0065445   0.0091623   0.02094241]
 [ 0.28795812 -0.90314136  0.57853403]]
[[10.  ]
 [ 0.25]
 [ 5.  ]]<jupyter_text>Method 2: Using solve() function<jupyter_code>y = np.linalg.solve(z,r)
print(y)<jupyter_output>[[10.  ]
 [ 0.25]
 [ 5.  ]]<jupyter_text>Concatenation<jupyter_code>a = np.array([[37, 20, 12]])
b = np.array([[15, 32, 4]])
c = np.array([[5,  40, 2]])

u = np.concatenate((a, b, c), axis=0)
print(u)<jupyter_output>[[37 20 12]
 [15 32  4]
 [ 5 40  2]]
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