#Python vectorize for loop code

This is the number one topic of developers and data scientists being separated from each other. Tic= time.time() #counting initial time #replacing for-loop with np.Developers tend to handle problems with conditional statements and loops. Np.dot(): returns product value of two arraysįull-Code : import time import numpy as np Time.time() : returns the time as a floating-point number expressed in seconds since the epoch, in UTC Np.random.rand(): creates an array of specified shape and fills it with random values. As a result, the loop is slower in Python. So every time iterating, the interpreter has to figure out the datatype. In Python, there is no way to predict the datatype beforehand in a for loop as stored data can be heterogeneous if it's a list. On the other hand, for-loop can't work parallelly instead it iterates every element one by one. In addition, vectorized operations in NumPy are optimized with C. Also, NumPy arrays are homogeneous and only contain objects of one type.

So it can run operations on them at once. NumPy uses the parallelism technique to work faster with arrays. Why NumPy(vectorization process) is faster than loop ? Here, from these solutions, we can see that, for-loop takes (650.61/4.67)=139.32 times more to execute than vectorizations. #vectorized execution time=final time -initial time Tic= time.time() #counting initial time #replacing for-loop with np.dot Let's try the same example from before by replacing the loop and checking the execution time. #for_loop execution time=final time -initial time Tic= time.time() #counting initial time for i in range( 1000000): The third array will store the product of array1 and array2. Now with array1 and array2's values, we will create a third array calls arra圓 which will be a 2-dimensional array. #randomly generating 1000000 digits and storing them in one dimensional array array2 #randomly generating 1000000 digits and storing them in one dimensional array array1 We will create two variables with each having a one-dimensional array. We perform operations with these functions to reduce the execution and runtime. And to achieve that goal, we use functions defined by various modules.

#Python vectorize for loop code

In deep learning, code gets much faster if we vectorized our code. Vectorization is a method to make code efficient without for-loops.

details of functions/mudules used in the code.

Why NumPy(vectorization process) is faster than loop ?.

Now, the question arises what could be the easier replacement then? The answer is vectorization and we should check it by comparing them with real examples. Thus, for-loop can be a huge bottleneck sometimes and can result in running a code for ages. In deep learning, if you are thinking about iterating through a huge dataset with for loop, that could cost a huge amount of time. A bunch of times when we need to use nested loops, our code could run slower. When it comes to execute code repeatedly, we tend to use for-loop.