Algorithm

Problem Name: Python - Mean, Var, and Std

Problem Link: https://www.hackerrank.com/challenges/np-mean-var-and-std/problem?isFullScreen=true   

In this HackerRank Functions in PYTHON problem 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 = 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 * 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

 

 

 

Code Examples

#1 Code Example with Python Programming

Code - Python Programming


import numpy
space, _ = map(int, input().split(' '))
arr = []
for i in range(space):
    line = list(map(int, input().split(' ')))
    arr.append(line)

arr = numpy.array(arr)
print(numpy.mean(arr, axis=1))
print(numpy.var(arr, axis=0))
print(round(numpy.std(arr, axis=None),11))
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Demonstration

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