Mean, Var, and Std HackerRank Solution In Python

Problem

mean

The mean tool computes the arithmetic mean along the specified axis.

import numpymy_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 : 2.5print 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 numpymy_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 : 1.25print 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 numpymy_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 : 1.11803398875print numpy.std(my_array)                   #Output : 1.11803398875

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

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Task

You are given a 2-D array of size NXM.
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 21 23 4

Sample Output

[ 1.5  3.5][ 1.  1.]1.11803398875

Solution – Mean, Var, and Std In Python | HackerRank

import numpy as npn, m = list(map(int, input().split()))a = np.array([list(map(int, input().split())) for _ in range(n)])print(np.mean(a, axis=1))print(np.var(a, axis=0))print(round(np.std(a, axis=None), 11))