'''
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 A with dimensions NXN. Your task is to find the determinant. Note: Round the answer to 2 places after the decimal.

Input Format
The first line contains the integer N.
The next N lines contains the N space separated elements of array A.

Output Format
Print the determinant of A.

Sample Input
2
1.1 1.1
1.1 1.1

Sample Output
0.0

'''

import numpy

a = int(input())
b = []
for i in range(a):
    c = input()
    b.append(list(map(float, c.split())))
d = numpy.linalg.det(b)
print(round(d, 2))
