numpy.linalg.eig() Method in Python

In NumPy we can compute the eigenvalues and right eigenvectors of a given square array with the help of numpy.linalg.eig(). It will take a square array as a parameter and it will return two values first one is eigenvalues of the array and second is the right eigenvectors of a given square array.

Syntax: numpy.linalg.eig()

Parameter: An square array.

Return: It will return two values first is eigenvalues and second is eigenvectors.

Example 1:



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import numpy as np
  
  
mat = np.mat("1 -2;1 3")
  
# Original matrix
print(mat)
print("")
evalue, evect = np.linalg.eig(mat)
  
# Eigenvalues of the said matrix"
print(evalue)
print("")
  
# Eigenvectors of the said matrix
print(evect)

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Output:

[[ 1 -2]
 [ 1  3]]

[2.+1.j 2.-1.j]

[[ 0.81649658+0.j          0.81649658-0.j        ]
 [-0.40824829-0.40824829j -0.40824829+0.40824829j]]

Example 2:

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import numpy as np
  
  
mat = np.mat("1 2 3;1 3 4;3 2 1")
  
# Original matrix
print(mat)
print("")
evalue, evect = np.linalg.eig(mat)
  
# Eigenvalues of the said matrix"
print(evalue)
print("")
  
# Eigenvectors of the said matrix
print(evect)

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Output:

[[1 2 3]
 [1 3 4]
 [3 2 1]]

[ 6.70156212  0.29843788 -2.        ]

[[-0.5113361  -0.42932334 -0.40482045]
 [-0.69070311  0.7945835  -0.52048344]
 [-0.5113361  -0.42932334  0.75180941]]
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