=====Function Descriptions=====
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]]

=====Problem Statement=====
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 space separated elements of array A. 

=====Output Format=====
Print the determinant of A.

Difficulty: introductory

Starter code:

import numpy

Solutions:
import numpy
n = int(input())
ar = []
for i in range(n):
    tmp = list(map(float,input().split()))
    ar.append(tmp)
np_ar = numpy.array(ar,float)
print((numpy.linalg.det(np_ar)))


import numpy as np

n = int(input().strip())
array = np.array([[float(x) for x in input().strip().split()] for _ in range(n)], dtype = float)

print((np.linalg.det(array)))