Stop wasting time with for loops!

One important note: Before fully committing to parallelization, ensure it’s beneficial for your task. Set the number of processes carefully, so it doesn’t overload the system, especially if you need resources for other tasks. Avoid parallelization if tasks depend on each other sequentially, as this will reduce efficiency. Additionally, parallelization can sometimes slow down your code. For instance, if the overhead from switching between processes (context switching) exceeds the time required to execute the code itself, the performance may actually degrade rather than improve.

please don't use parallelization if you don't know what you're doing. there's a lot more to consider than just speedup

Not ordered, and the function itself has random sleep. This example is dumb.
It's true that for loops are slow in Python, but you could've shown actual alternatives like numpy for large lists, but this is just ridiculous.

Great hack! very useful to optimize code 🔥
Keep it up and give us more Python hacks and tricks!