Vectorization and looping in python

Vectorised and non vectorised code comparison

vectorization is required in python when we are dealing with matrics. With the evolution of deep learning it has gained more lime-light.

Here is the exection time comparison of vectorised and non vectorised  code.


# initialization of array 
import numpy as np
a=np.array([1,2,3,4])
print(a)

[1 2 3 4]

#initialize numpy array

import time
A = np.random.rand(1000000)
B = np.random.rand(1000000)

# calculating execution time using vectorization
tic = time.time()
C =np.dot(A, B)
toc =time.time()

print('total time taken in vectorised multiplication' + str(toc-tic) + 'mili-seconds')

total time taken in vectorised multiplication0.002000093460083008mili-seconds

# calculating execution time using non vectorization code
tic = time.time()
for i in range(1000000):
    C = C +A[i]*B[i]   
print(C)
toc= time.time()
print('total time taken in non-vectorised code'+ str(toc-tic) +' mili seconds')
749296.5132501889
total time taken in non-vectorised code0.6779999732971191 mili seconds