# .inv()

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Published Jun 5, 2024
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The `.inv()` function inverts a given matrix and returns the inverted matrix. If the inversion fails or the given matrix is not a square matrix, then it raises an `LinAlgError`. Some of its use cases in the field of statistical analysis include:

• Linear Regression
• Multivariate Analysis
• Bayesian Statistics

## Syntax

``````numpy.linalg.inv(a)
``````
• `a`: The matrix to be inverted.

## Example

The following example demonstrates the usage of the `.inv()` function:

```import numpy as np
array =  np.array([[2., 3.], [1., 4.]])array2 = np.array([[[2., 3.], [1., 4.]], [[6., 4.], [10., 10.]]])
# The determinant is bigger then zero since the above matrices are non-singular
print("One matrix: ")print(np.linalg.inv(array))
print("\nTwo matrices: ")print(np.linalg.inv(array2))
```

The output for the above code is as follows:

```One matrix:[[ 0.8 -0.6] [-0.2  0.4]]
Two matrices:[[[ 0.8 -0.6]  [-0.2  0.4]]
[[ 0.5 -0.2]  [-0.5  0.3]]]
```

## Codebyte Example

Run the following codebyte example of the `.inv()` function, to better understand its working:

`usVisit uscodeHide codeCodeOutputHide outputHide outputLoading...`

Note: The `.inv()` function raises an `LinAlgError` when a singular matrix is passed for inversion, as it can’t be inverted.