2.9. Code Speed

This section will show you some ways to speed up or track the performance of your Python code.

2.9.1. Concurrently Execute Tasks on Separate CPUs

If you want to concurrently execute tasks on separate CPUs to run faster, consider using joblib.Parallel. It allows you to easily execute several tasks at once, with each task using its own processor.

from joblib import Parallel, delayed
import multiprocessing

def add_three(num: int):
    return num + 3

num_cores = multiprocessing.cpu_count()
results = Parallel(n_jobs=num_cores)(delayed(add_three)(i) for i in range(10))
results 
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12]

2.9.2. Compare The Execution Time Between 2 Functions

If you want to compare the execution time between 2 functions, try timeit.timeit. You can also specify the number of times you want to rerun your function to get a better estimation of the time.

import time 
import timeit 

def func():
    """comprehension"""
    l = [i for i in range(10_000)]

def func2():
    """list range"""
    l = list(range(10_000))

expSize = 1000
time1 = timeit.timeit(func, number=expSize)
time2 = timeit.timeit(func2, number=expSize)

print(time1/time2)
2.6299518653018685

From the result, we can see that it is faster to use list range than to use list comprehension on average.