"""
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 as np

np.set_printoptions(legacy="1.13")

N = int(input())
arr1 = np.array([list(map(float, input().split())) for _ in range(N)], float)
print(np.linalg.det(arr1))
