**Eigenvalues** are scalar solutions to the equation `Ax = ax`

, where
`A`

is a two-dimensional matrix and `x`

is a one-dimensional vector. **Eigenvectors** are vectors corresponding to eigenvalues.

Eigenvalues and eigenvectors are fundamental in mathematics and are used in many important algorithms, such as **Principal Component Analysis** (**PCA**). PCA can be used to simplify the analysis of large datasets.

The `eigvals()`

subroutine in the `numpy.linalg`

package computes eigenvalues. The `eig()`

function gives back a tuple holding eigenvalues and eigenvectors.

We will obtain the eigenvalues and eigenvectors of a matrix with the `eigvals()`

and `eig()`

functions of the `numpy.linalg`

subpackage. We will check the outcome by ...

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