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.