# How to Fix: numpy.linalg.LinAlgError: Singular matrix

One error you may encounter in Python is:

```numpy.linalg.LinAlgError: Singular matrix
```

This error occurs when you attempt to invert a singular matrix, which by definition is a matrix that has a determinant of zero and cannot be inverted.

This tutorial shares how to resolve this error in practice.

### How to Reproduce the Error

Suppose we create the following matrix using NumPy:

```import numpy as np

#create 2x2 matrix
my_matrix = np.array([[1., 1.], [1., 1.]])

#display matrix
print(my_matrix)

[[1. 1.]
[1. 1.]]```

Now suppose we attempt to use the inv() function from NumPy to calculate the inverse of the matrix:

```from numpy import inv

#attempt to invert matrix
inv(my_matrix)

numpy.linalg.LinAlgError: Singular matrix```

We receive an error because the matrix that we created does not have an inverse matrix.

Note: Check out this page from Wolfram MathWorld that shows 10 different examples of matrices that have no inverse matrix.

By definition, a matrix is singular and cannot be inverted if it has a determinant of zero.

You can use the det() function from NumPy to calculate the determinant of a given matrix before you attempt to invert it:

```from numpy import det

#calculate determinant of matrix
det(my_matrix)

0.0
```

The determinant of our matrix is zero, which explains why we run into an error.

### How to Fix the Error

The only way to get around this error is to simply create a matrix that is not singular.

For example, suppose we use the inv() function to invert the following matrix:

```import numpy as np
from numpy.linalg import inv, det

#create 2x2 matrix that is not singular
my_matrix = np.array([[1., 7.], [4., 2.]])

#display matrix
print(my_matrix)

[[1. 7.]
[4. 2.]]

#calculate determinant of matrix
print(det(my_matrix))

-25.9999999993

#calculate inverse of matrix
print(inv(my_matrix))

[[-0.07692308  0.26923077]
[ 0.15384615 -0.03846154]]
```

We don’t receive any error when inverting the matrix because the matrix is not singular.