# NumPy det()

The determinant of a matrix is a scalar value that provides information about the properties and behavior of the matrix.

### Example

The `numpy.linalg.det()` function is used to compute the determinant of a square matrix.

``````import numpy as np

# create a 2x2 matrix
matrix1 = np.array([[2, 4],
[1, 6]])

# compute the determinant
result = np.linalg.det(matrix1)

print(result)

# Output: 7.999999999999998``````

## det() Syntax

The syntax of `det()` is:

``numpy.linalg.det(matrix)``

## det() Arguments

The `det()` method takes the following arguments:

• `matrix` - the input matrix for which we want to compute the determinant

## det() Return Value

The `det()` method returns a floating-point number.

## Example 1: Determinant of a 3x3 Matrix

``````import numpy as np

# create a matrix
matrix1 = np.array([[1, 2, 3],
[4, 5, 1],
[2, 3, 4]])

# find determinant of matrix1
result = np.linalg.det(matrix1)

print(result)``````

Output

`-5.00`

Here, we have used the `np.linalg.det(matrix1)` function to find the determinant of the square matrix matrix1.

## Example 2: Determinant of a Random Matrix

``````import numpy as np

# create a random 2x2 matrix
matrix1 = np.random.randint(0, 10, (2, 2))

# find determinant of matrix1
result = np.linalg.det(matrix1)

print("Matrix:\n", matrix1)
print("Determinant: \n", result)``````

Output

```Matrix:
[[5 8]
[2 7]]
Determinant:
18.999999999999996```

Here, we have created a random 2x2 matrix using `np.random.randint()` and then used `np.linalg.det()` to find the determinant.

This code generates a different output each time we run it.