HackerRank Python Solution - Numpy Topic - Linear Algebra

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]]

Task:

You are given a square matrix A with dimensions N x N. 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

Solution:

import numpy

ip = [[1.1,1.1],[1.1,1.1]]
n = int(input())

arr = numpy.array([list(map(float,input().split())) for _ in range(n)])

print(round(numpy.linalg.det(arr),2))

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