Mean, Var, and Std in Python | HackerRank Solution

Hello coders, today we are going to solve Mean, Var, and Std HackerRank Solution in Python.

Mean, Var, and Std in Python

Contents

Objective

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 = None)     #Output : 2.5
print numpy.mean(my_array)                  #Output : 2.5

By default, the axis is None. Therefore, it computes the mean of the flattened array.

var

The var tool computes the arithmetic variance along the specified axis.

import numpy

my_array = numpy.array([ [1, 2], [3, 4] ])

print numpy.var(my_array, axis = 0)         #Output : [ 1.  1.]
print numpy.var(my_array, axis = 1)         #Output : [ 0.25  0.25]
print numpy.var(my_array, axis = None)      #Output : 1.25
print numpy.var(my_array)                   #Output : 1.25

By default, the axis is None. Therefore, it computes the variance of the flattened array.

std

The std tool computes the arithmetic standard deviation along the specified axis.

import numpy

my_array = numpy.array([ [1, 2], [3, 4] ])

print numpy.std(my_array, axis = 0)         #Output : [ 1.  1.]
print numpy.std(my_array, axis = 1)         #Output : [ 0.5  0.5]
print numpy.std(my_array, axis = None)      #Output : 1.11803398875
print numpy.std(my_array)                   #Output : 1.11803398875

By default, the axis is None. Therefore, it computes the standard deviation of the flattened array.

Task

You are given a 2-D array of size N X M.
Your task is to find:

  1. The mean along axis 1
  2. The var along axis 0
  3. The std along axis None

Input Format

The first line contains the space separated values of N and M.
The next N lines contains M space separated integers.

Output Format

First, print the mean.
Second, print the var.
Third, print the std.

Sample Input

2 2
1 2
3 4

Sample Output

[ 1.5  3.5]
[ 1.  1.]
1.11803398875

Solution – Mean, Var, and Std in Python

import numpy as np
n, m = map(int, input().split())
k = np.array([input().split() for _ in range(n)],dtype = int)
np.set_printoptions(legacy='1.13')
print(np.mean(k,axis=1), np.var(k,axis=0), np.std(k), sep='\n')

Disclaimer: The above Problem (Mean, Var, and Std) is generated by Hacker Rank but the Solution is Provided by CodingBroz. This tutorial is only for Educational and Learning Purpose.

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