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NumPy

A collection of 9 issues

Introduction to NumPy for numerical computing

Introduction to NumPy for numerical computing NumPy is a powerful and versatile library for Python developers, providing a range of features for data manipulation and analysis. In this article, we’ll explore the fundamentals of NumPy, including what it is, its history, key features, and benefits. We’ll also take

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

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

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.

Hackerrank Eye and Identity Solution

Solution in python3 Approach 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)

Hackerrank Zeros and Ones Solution

Solution in python3 Approach 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(

Hackerrank Floor, Ceil and Rint Solution

Solution in python3 Approach 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

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,

Hackerrank Shape and Reshape Solution

Solution in python3 Approach 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(