## Algorithm

Problem Name: Python - Linear Algebra

In this HackerRank Functions in PYTHON problem solution,

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

You are given a square matrix A with dimensions N * 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``

## Code Examples

### #1 Code Example with Python Programming

```Code - Python Programming```

``````
import numpy as np

n=int(input())

print(round(np.linalg.det([[float(j) for j in input().rstrip().split()] for i in range(n)]),2))
``````
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