SpeedUp Python List and Dictionary

- 4 mins

Today, we will be discussing the optimization technique in Python. In this article, you will get to know to speed up your code by avoiding the re-evaluation inside a list and dictionary.

Here I have written the decorator function to calculate the execution time of a function.

import functools
import time

def timeit(func):
    @functools.wraps(func)
    def newfunc(*args, **kwargs):
        startTime = time.time()
        func(*args, **kwargs)
        elapsedTime = time.time() - startTime
        print('function - {}, took {} ms to complete'.format(func.__name__, int(elapsedTime * 1000)))
    return newfunc

let’s move to the actual function

Avoid Re-evaluation in Lists

Evaluating nums.append inside the loop

@timeit
def append_inside_loop(limit):
    nums = []
    for num in limit:
        nums.append(num)

append_inside_loop(list(range(1, 9999999)))

In the above function nums.append function references that are re-evaluated each time through the loop. After execution, The total time taken by the above function

o/p - function - append_inside_loop, took 529 ms to complete

Evaluating nums.append outside the loop

@timeit
def append_outside_loop(limit):
    nums = []
    append = nums.append
    for num in limit:
        append(num)

append_outside_loop(list(range(1, 9999999)))

In the above function, I evaluate nums.append outside the loop and used append inside the loop as a variable. Total time is taken by the above function

o/p - function - append_outside_loop, took 328 ms to complete

As you can see when I have evaluated the append = nums.append outside the for loop as a local variable, it took less time and speed-up the code by 201 ms.

The same technique we can apply to the dictionary case also, look at the below example

Avoid Re-evaluation in Dictionary

Evaluating data.get each time inside the loop

@timeit
def inside_evaluation(limit):
    data = {}
    for num in limit:
        data[num] = data.get(num, 0) + 1

inside_evaluation(list(range(1, 9999999)))

Total Time taken by the above function -

o/p - function - inside_evaluation, took 1400 ms to complete

Evaluating data.get outside the loop

@timeit
def outside_evaluation(limit):
    data = {}
    get = data.get
    for num in limit:
        data[num] = get(num, 0) + 1


outside_evaluation(list(range(1, 9999999)))

Total time taken by the above function -

o/p - function - outside_evaluation, took 1189 ms to complete

As you can see we have speed-up the code here by 211 ms.

I hope you like the explanation of the optimization technique in Python for the list and dictionary. Still, if any doubt or improvement regarding it, ask in the comment section. Also, don’t forget to share your optimization technique.

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