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# Inverse of a Matrix using Numpy The inverse of a matrix is just a reciprocal of the matrix as we do in normal arithmetic for a single number which is used to solve the equations to find the value of unknown variables.

The inverse of a matrix is that matrix which when multiplied with the original matrix will give an identity matrix. The inverse of a matrix exists only if the matrix is non-singular i.e., determinant should not be 0. Using determinant and adjoint, we can easily find the inverse of a square matrix using below formula,

```if det(A) != 0
else
"Inverse doesn't exist" ```

Inverse of a Matrix using NumPy

Python provides a very easy method to calculate the inverse of a matrix. The function numpy.linalg.inv() which is available in the python NumPy module is used to compute the inverse of a matrix.

First we Import numpy package

`import numpy as np`

Taking a 3 * 3 matrix as

`A = np.array([[4, 3, 6],[2, 5, 9],[8, 6, 3]]) `

Now, calculating the inverse of the matrix as

`print(np.linalg.inv(A))`
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