"""
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.

Input Format

The first line contains the integer
.
The next lines contains the space separated elements of array

.

Output Format

Print the determinant of

.

Sample Input

2
1.1 1.1
1.1 1.1

Sample Output

0.0
"""


import numpy
n=int(input())
a=numpy.array([input().split() for _ in range(n)],float)
numpy.set_printoptions(legacy='1.13') #important
print(numpy.linalg.det(a))












