HackerRank Python Solution - Numpy Topic - Mean, Var, and Std

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: 

• The mean along axis 1
• The var along axis 0
• The std along axis None

Input Format:

The first line contains the space-separated values of N and M. The next N lines contain 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:

import numpy
array = []
n, m = map(int, input().split())

for _ in range(n): 
    array.append(list(map(int, input().split())))

array = numpy.array(array)


print(numpy.mean(array, axis=1))
print(numpy.var(array, axis=0))
print(round(numpy.std(array), 11))

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