# 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:

## Python

 `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) `

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"``) ` ` `  `# 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) `

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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