The NumPy module also comes with a number of built-in routines for linear algebra calculations. These can be found in the sub-module linalg.

linalg.det

The linalg.det tool computes the determinant of an array.

print numpy.linalg.det([[1 , 2], [2, 1]]) #Output : -3.0 

linalg.eig

The linalg.eig computes the eigenvalues and right eigenvectors of a square array.

vals, vecs = numpy.linalg.eig([[1 , 2], [2, 1]]) print vals #Output : [ 3. -1.] print vecs #Output : [[ 0.70710678 -0.70710678] # [ 0.70710678 0.70710678]] 

linalg.inv

The linalg.inv tool computes the (multiplicative) inverse of a matrix.

print numpy.linalg.inv([[1 , 2], [2, 1]]) #Output : [[-0.33333333 0.66666667] # [ 0.66666667 -0.33333333]] 

Other routines can be found here

Task

You are given a square matrix with dimensions X. Your task is to find the determinant. Note: Round the answer to 2 places after the decimal.