How to use NumPy for Linear Algebra

import numpy as np
from numpy import linalg
 
A = np.array([[1, 2, 1],
              [4, 9, 5],
              [4, 8, 11]])
 
print("Rank of matrix A:", linalg.matrix_rank(A))
 
print("Determinant of matrix A:", linalg.det(A))
 
print("Inverse of A:", linalg.inv(A))

import numpy as np
from numpy import linalg
 
arr = np.array([[3, -4j], [5j, 6]])
 
print("Given Array:",arr)
 
e1, e2 = linalg.eigh(arr)
 
print("Eigen value is :", e1)
print("Eigen value is :", e2)

import numpy as np
# x + y = 6 
# −3x + y = 2 
arr1 = np.array([[1, 1], [-3, 1]])
arr2 = np.array([6, 2])
 
arr =  np.linalg.solve(arr1, arr2)
print ('x =', arr[0])
print ('y =', arr[1])

import numpy as np
# dot product of two vectors
a = np.array([1+2j,3+4j])
b = np.array([5+6j,7+8j])
 
product = np.vdot(a, b)
print("Dot Product  : ", product)
# inner product of arrays
a = np.array([1,2,3])
b = np.array([0,1,0])
product = np.inner(a, b)
print("Inner Product  : ", product)
# matrix multiplication
a = np.array([[1, 0],
              [0, 1]])
b = np.array([[4, 1],
              [2, 2]])
product = np.matmul(a, b)
print("Product of Matrices : ", product)