Thursday, December 12, 2019

Beginning Numpy

This post I'm looking at continuing building my knowledge of Pandas through the training Pandas for Pentester from Pentester Academy.

Numpy allows us to use N Dimensional Arrays (NDArray)

Numpy can be very helpful when dealing with large datasets. It can be very efficient from a CPU utilisation perspective.


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#!/usr/bin/env python3

import numpy as np

def main():

    #print the installe version of numpy
    print('[*] The current version of numpy is {0}' .format(np.__version__))

    # Sample numpy array from a python list - one dimensional
    my_list = [10, 20, 30, 40, 50, -30, -20, 10]
    my_list = np.array(my_list)
    print('\n[*] numpy one dimensional array created from python list \n {}' .format(my_list))


    #numpy array - 2 dimensional
    my_np_array = np.array([[10,20], [30,40], [50,-30], [-20,10]])
    print('\n[*] Sample two dimensional numpy array \n{}'.format(my_np_array))

    print('\n[*] Each 2nd item in the array \n {}' .format(my_np_array[::2]))

    # To Access content in the array we can slice as follows

    print('\n[*] Printing the frist row in the array \n {}'.format(my_np_array[0]))

    print('\n[*] Printing the 2nd and 3rd row in the array \n {}'.format(my_np_array[1:3]))

    print('\n[*] Printing the 2nd colum only \n {}'.format(my_np_array[:,1]))

    print('\n[*] Printing the 2nd row and column \n {}'.format(my_np_array[1,1]))

    #Let's now multiply the value in the array by 2

    my_np_array_mul = my_np_array * 2
    print('\n[*] Array with values multiplied by 2 \n {}'.format(my_np_array_mul))

    #Add values to the np array by 10
    my_np_array_sum = my_np_array + 10

    print('\n[*] Array with values added by 10 \n {}'.format(my_np_array_sum))

    #Subtract values from np array by 15
    my_np_array_sub = my_np_array - 15

    print('\n[*] Array with values -10 \n {}'.format(my_np_array_sub))

    #Divide values from np array by 5
    my_np_array_div = my_np_array / 5
    print('\n[*] Array divide by 5 \n{}' .format(my_np_array_div))

    # Generate a 3 rows by 3 columns random array
    my_random_a = np.random.randint(1,300, (3,3))
    print('\n[*] A randomly generated 3 by 3 array \n{}'.format(my_random_a))
    my_random_b = np.random.randint(4,700,(3,3))
    print('\n[*] A randomly generated 3 by 3 array \n{}'.format(my_random_b))
    
# Let's now do one math operation against those two arrays
    print('\n[*] Sum of the values in the two random arrays \n {}'.format(my_random_a + my_random_b))

    # Let's now get the len and shape of the new combined array
    print('\n[*] The shape of the summed up array is \n {}' .format(np.shape(my_random_a + my_random_b)))
    print('\n[*] The dimension of the summed up array is \n {}' .format(np.ndim(my_random_a + my_random_b)))


    #Let's now create an array consisting of True and False
    my_true_false = np.array([True, False]*20)
    print('\n[*] Printing 20 True False \n {}'.format(my_true_false))

    # Creating a new 6 dimensional random array
    my_new_random_array = np.random.randint(1,1000, (6,6))
    print('\n[*] 6x6 array \n {}' .format(my_new_random_array))
    print('\n[*] values of X less tan 500 \n{}' .format(my_new_random_array < 500))
    print('\n[*] values of X less tan 500 \n{}' .format(my_new_random_array [my_new_random_array < 500]))


    #Create a range of numbers from 0 to 100
    create_range = np.arange(100)
    print('\n[*] Range of numbers 0 to 100 \n{}'.format(create_range))
    print('\n[*] The square of the numers in create_range are: \n {}'.format(np.square(create_range)))
    print('\n[*] The square root of the numers in create_range are: \n {}'.format(np.sqrt(create_range)))
    print('\n[*] The data type of the numers in create_range are: \n {}'.format(create_range.dtype))



'''
    #Replacing all values greater than  500 with -500 
    my_new_random_array_replaced = my_new_random_array[my_new_random_array > 500] = -500
    print('\n[*] Replacing all values greater than  500 with -500 \n{}' .format(my_new_random_array_replaced))

'''


if __name__ == '__main__':

    main()


Reference:
Pandas for Pentesters
https://docs.scipy.org/doc/numpy-1.13.0/reference/arrays.indexing.html
NumPy Reference


Posts in this series:
Beginning Numpy
Beginning Pandas
Pandas String Operations, etc.

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