HackerRank Python Solution - Numpy Topic - Sum and Prod

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. 

HackerRank Python Solution - Numpy Topic - Floor, Ceil and Rint

Floor:

The tool floor returns the floor of the input element-wise. The floor of x is the largest integer i where i<=x.

import numpy

my_array = numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9])
print numpy.floor(my_array)         #[ 1.  2.  3.  4.  5.  6.  7.  8.  9.]

Ceil:

The tool ceil returns the ceiling of the input element-wise. The ceiling of x is the smallest integer i where i >= x.

HackerRank Python Solution - Numpy Topic - Shape and Reshape

Shape:

The shape tool gives a tuple of array dimensions and can be used to change the dimensions of an array. 

(a). Using shape to get array dimensions

import numpy

my__1D_array = numpy.array([1, 2, 3, 4, 5])
print my_1D_array.shape     #(5,) -> 1 row and 5 columns

my__2D_array = numpy.array([[1, 2],[3, 4],[6,5]])
print my_2D_array.shape     #(3, 2) -> 3 rows and 2 columns 

HackerRank Python Solution - Numpy Topic - Array Mathematics

Basic mathematical functions operate elementwise on arrays. They are available both as operator overloads and as functions in the NumPy module.

import numpy

a = numpy.array([1,2,3,4], float)
b = numpy.array([5,6,7,8], float)

print a + b                     #[  6.   8.  10.  12.]
print numpy.add(a, b)           #[  6.   8.  10.  12.]

print a - b                     #[-4. -4. -4. -4.]
print numpy.subtract(a, b)      #[-4. -4. -4. -4.]

print a * b                     #[  5.  12.  21.  32.]
print numpy.multiply(a, b)      #[  5.  12.  21.  32.]

print a / b                     #[ 0.2         0.33333333  0.42857143  0.5       ]
print numpy.divide(a, b)        #[ 0.2         0.33333333  0.42857143  0.5       ]

print a % b                     #[ 1.  2.  3.  4.]
print numpy.mod(a, b)           #[ 1.  2.  3.  4.]

print a**b                      #[  1.00000000e+00   6.40000000e+01   2.18700000e+03   6.55360000e+04]
print numpy.power(a, b)         #[  1.00000000e+00   6.40000000e+01   2.18700000e+03   6.55360000e+04]

HackerRank Python Solution - Numpy Topic - Eye and Identity

Identity:

The identity tool returns an identity array. An identity array is a square matrix with all the main diagonal elements as 1 and the rest as 0. The default type of element is float.

import numpy
print numpy.identity(3) #3 is for  dimension 3 X 3

#Output
[[ 1.  0.  0.]
 [ 0.  1.  0.]
 [ 0.  0.  1.]]

HackerRank Python Solution - Numpy Topic - Zeros and Ones

Zeros:

The zeros tool returns a new array with a given shape and type filled with 0's.

import numpy

print numpy.zeros((1,2))                    #Default type is float
#Output : [[ 0.  0.]] 

print numpy.zeros((1,2), dtype = numpy.int) #Type changes to int
#Output : [[0 0]]

HackerRank Python Solution - Numpy Topic - Concatenate

Concatenate:

Two or more arrays can be concatenated together using the concatenate function with a tuple of the arrays to be joined:

import numpy

array_1 = numpy.array([1,2,3])
array_2 = numpy.array([4,5,6])
array_3 = numpy.array([7,8,9])

print numpy.concatenate((array_1, array_2, array_3))    

#Output
[1 2 3 4 5 6 7 8 9]