How to Calculate the Determinant of a Matrix in Numpy

This tutorial will teach you how to calculate the determinant of a matrix using Python’s Numpy library.

Numpy determinant

The Determinant of a Matrix

The determinant of a matrix is a number that summarizes the properties of the matrix. It can be used to solve linear equations, find eigenvalues, and do other things.

Calculating the Determinant in Numpy

The Numpy library offers a dedicated det function that makes it very easy to calculate the determinant of a matrix.

import numpy as np

my_matrix = np.array([(10, 2),
                     (7, 8)])

determinant = np.linalg.det(my_matrix)

print(f"Determinant equals {round(determinant, 2)}")

This code creates a Numpy array called my_matrix and then calculates its determinant using the linalg.det function. The result is rounded to two decimal places and printed to the console.

See also  How to check if array is empty?


I created an example matrix and called it my_matrix. Then I used the np.linalg.det function, which takes my_matrix as an argument.

Numpy determinant

Finally, I printed out the result, which I rounded to the two decimal places. I recommend rounding results. Otherwise, the result will be more like 65.99999999999997. The reason is that Numpy calculates numerically and not analytically. That’s why the results, which are not rounded up look very strange and they are not very useful.

See also  How to add two arrays in Numpy?

Dealing with errors

Sometimes it may happen that you will get an error when trying to calculate the determinant of a matrix. Here are two common errors and how to deal with them:

LinAlgError: This error occurs when the matrix is not square.
TypeError: This error occurs when the data types of the matrix values are not the same.

    raise LinAlgError('%d-dimensional array given. Array must be '
numpy.linalg.LinAlgError: 1-dimensional array given. Array must be at least two-dimensional

That kind of error tells you that there is something wrong with your matrix. Please check if the tuples are equal. Maybe some of them contain obsolete values?

See also  Linear Regression with NumPy

The other type of error is like the below one:

    r = _umath_linalg.det(a, signature=signature)
TypeError: No loop matching the specified signature and casting was found for ufunc det

This one is not so obvious because it does not tell much. The reason for the error is that the data type of matrix values is not the same. One of them is probably a string. The data type of all values in the matrix must be the same.