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Beeze Aal

Hackerrank Mean, Var, and Std Solution

mean The mean tool computes the arithmetic mean along the specified axis. import numpy my_array = numpy.array([ [1, 2], [3, 4] ]) print numpy.mean(my_array, axis = 0) #Output : [ 2. 3.] print numpy.mean(my_array, axis = 1) #Output : [ 1.5 3.5] print numpy.mean(my_array, axis
Beeze Aal

Hackerrank Polynomials Solution

poly The poly tool returns the coefficients of a polynomial with the given sequence of roots. print numpy.poly([-1, 1, 1, 10]) #Output : [ 1 -11 9 11 -10] roots The roots tool returns the roots of a polynomial with the given coefficients. print numpy.roots([1, 0, -1]) #Output
Beeze Aal

Hackerrank Linear Algebra 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.
Beeze Aal

Hackerrank Eye and Identity Solution

Solution in python3Approach 1. import numpy print(numpy.eye(*map(int,input().split())))Approach 2. import numpy N, M = map(int, input().split()) print(numpy.eye(N, M))Solution in python import numpy N,M = map(int,raw_input().split()) print numpy.eye(N,M)
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Hackerrank Zeros and Ones Solution

Solution in python3Approach 1. import numpy N = list(map(int, input().split())) print(numpy.zeros(N, int)) print(numpy.ones(N, int))Approach 2. import numpy size = tuple(map(int,input().strip().split())) print( numpy.zeros(size, int) ) print( numpy.ones(size, int) )Approach 3. import numpy N = [int(x)
Beeze Aal

Hackerrank Floor, Ceil and Rint Solution

Solution in python3Approach 1. import numpy a = numpy.array(input().split(),float) print(numpy.floor(a),numpy.ceil(a),numpy.rint(a),sep="\n")Approach 2. from numpy import * A = array([float(x) for x in input().split()],float) print(floor(A),ceil(A),rint(A),sep = '\n')Approach
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Hackerrank Array Mathematics Solution

Basic mathematical functions operate element-wise on arrays. They are available both as operator overloads and as functions in the NumPy module. import numpy a = numpy.array([1,2,3,4], float) b = numpy.array([5,6,7,8], float) print a + b #[ 6. 8. 10. 12.] print numpy.add(a,
Beeze Aal

Hackerrank Shape and Reshape Solution

Solution in python3Approach 1. import numpy print( numpy.array(input().split(" "),int).reshape(3,3) )Approach 2. import numpy l=input().split() a=numpy.array(l,int) a.shape=(3,3) print(a)Approach 3. import numpy A = numpy.array(input().strip().split(),int) print(numpy.reshape(A,(3,3)