Geek4Tutorial.com

HackerRank Python Solution - Numpy Topic - Min and Max

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.

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]

HackerRank Python Solution - Numpy Topic - Transpose and Flatten

Question 2 - Transpose and Flatten

Task

You are given an NxM integer array matrix with space-separated elements (N= rows and M= columns).
The question is to print the transpose and flatten the results.

Input Format

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

HackerRank Python Solution - Numpy Topic - Arrays

Question1 - Arrays:

Task

You are given a space-separated list of numbers.
Your task is to print a reversed NumPy array with the element type float.

Input Format

A single line of input containing space-separated numbers.

Library List in AS400

Library List:

A library list is a list of libraries maintained for each user session with the libraries arranged in decreasing order of priority.

Whenever an object is referenced in command without a library, then the system starts checking for the object in all the libraries in the library list.

If the object is found in the first library, then the system picks that object from that first library.

If the object is not found in any library in the library list then the system will throw an error.

Navbar menu