numpy.linalg.eig() Method in Python
Last Updated :
10 Aug, 2020
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:
Python
import numpy as np
mat = np.mat( "1 -2;1 3" )
print (mat)
print ("")
evalue, evect = np.linalg.eig(mat)
print (evalue)
print ("")
print (evect)
|
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:
Python
import numpy as np
mat = np.mat( "1 2 3;1 3 4;3 2 1" )
print (mat)
print ("")
evalue, evect = np.linalg.eig(mat)
print (evalue)
print ("")
print (evect)
|
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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