"""
Problem Statement

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.

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

n = int(raw_input())
a = numpy.array([map(float, raw_input().split()) for i in range(n)],
                dtype=numpy.float)
print numpy.linalg.det(a)

