# Mean, Var, and Std in Python | HackerRank Solution

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

You are given a 2-D array of size N X M.

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.