Mean, Var, and Std-HackerRank Solution

Mean, Var, and Std-HackerRank 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.

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Task

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

1. The mean along axis 
2. The var along axis 
3. The std along axis 

Input Format

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

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Min and Max-HackerRank Solution

 Min and Max-HackerRank Solution

min

The tool min returns the minimum value along a given axis.

import numpy

my_array = numpy.array([[2, 5], 
                        [3, 7],
                        [1, 3],
                        [4, 0]])

print numpy.min(my_array, axis = 0)         #Output : [1 0]
print numpy.min(my_array, axis = 1)         #Output : [2 3 1 0]
print numpy.min(my_array, axis = None)      #Output : 0
print numpy.min(my_array)                   #Output : 0

By default, the axis value is None. Therefore, it finds the minimum over all the dimensions of the input array.

max

The tool max returns the maximum value along a given axis.

import numpy

my_array = numpy.array([[2, 5], 
                        [3, 7],
                        [1, 3],
                        [4, 0]])

print numpy.max(my_array, axis = 0)         #Output : [4 7]
print numpy.max(my_array, axis = 1)         #Output : [5 7 3 4]
print numpy.max(my_array, axis = None)      #Output : 7
print numpy.max(my_array)                   #Output : 7

By default, the axis value is None. Therefore, it finds the maximum over all the dimensions of the input array.

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Task

You are given a 2-D array with dimensions X.
Your task is to perform the min function over axis  and then find the max of that.

Input Format

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

Output Format

Compute the min along axis  and then print the max of that result.

Sample Input

4 2
2 5
3 7
1 3
4 0

Sample Output

3

Explanation

The min along axis  = 
The max of  = 3

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Sum and Prod-HackerRank Solution

 Sum and Prod-HackerRank Solution

sum

The sum tool returns the sum of array elements over a given axis.

import numpy

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

print numpy.sum(my_array, axis = 0)         #Output : [4 6]
print numpy.sum(my_array, axis = 1)         #Output : [3 7]
print numpy.sum(my_array, axis = None)      #Output : 10
print numpy.sum(my_array)                   #Output : 10

By default, the axis value is None. Therefore, it performs a sum over all the dimensions of the input array.

prod

The prod tool returns the product of array elements over a given axis.

import numpy

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

print numpy.prod(my_array, axis = 0)            #Output : [3 8]
print numpy.prod(my_array, axis = 1)            #Output : [ 2 12]
print numpy.prod(my_array, axis = None)         #Output : 24
print numpy.prod(my_array)                      #Output : 24

By default, the axis value is None. Therefore, it performs the product over all the dimensions of the input array.

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Task

You are given a 2-D array with dimensions X.
Your task is to perform the  tool over axis  and then find the  of that result.

Input Format

The first line of input contains space separated values of  and .
The next  lines contains  space separated integers.

Output Format

Compute the sum along axis . Then, print the product of that sum.

Sample Input

2 2
1 2
3 4

Sample Output

24

Explanation

The sum along axis  = [ ]
The product of this sum = 24

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