There's An OPTIMIZED Way To Do THIS In Python (ft. enumerate)

Here's a quick optimization that you can perform in Python with enumerate().
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00:00 Intro
00:10 enumerate()
01:38 The tests
03:53 A good optimisation?
04:14 That’s it

[@cerealport2726]

For the occasions where "start" with enumerate has been really useful for me, I define the start as a variable because it depends on some other conditions. Then I refer to that variable via "start=". I think having the start value somewhere other than with "start=" is less readable, but each to their own, I guess.

[@Indently]

Out of curiosity, do you mind sharing an example of where you would do that? Just curious!

[@xelxen]

Loved the video! Which IDE is that tho?

[@HardLessonsOfLife]

Thanks for the information

[@HerculeanYT]

Correct me if I am incorrect, could you not have just done the following for the enumerate function?:
temp_list = list(enumerate(values, start=1))

[@Indently]

That's the correct code golf yes. But then you also have to do it for the normal_func.

[@kite759]

look at this code below
for key, group by groupby(enumerate(numbers,start=1), lambda x: x[1] - x[0])
i can't set i + 1) use start instead, it more readable

[@kamurashev]

Is it the same if you first time normal and then the “start=1” solution?
It’s not very accurate to benchmark like this.
First timed would be in worst condition, starting with the system that is not yet heated up.
Ideally this should be 2 separate programs and you should start it separate with a propped benchmarking tool that performs several heat up runs and then several measurement runs and calculate the average.

[@Indently]

Yeah I tried swapping them and the result remained the same. But I'll look into using more accurate benchmark tools, thanks for the recommendation!

[@kamurashev]

@@Indently np! I just like similar simple benchmarks myself. I enjoy doing different kinds of comparisons and I lately use benchmarking tools, don’t want to recommend anything just in order to not advertise 🙂. But it’s much more accurate from my experience.

[@Indently]

You're more than welcome to recommend any! Some people (including myself) love reading experiences with certain tools from others.

[@kamurashev]

@@Indently I personally like hyperfine, yes it’s not python native its a standalone os tool, but it’s open source and it worked for me perfectly as I often compare different languages or different versions of languages.

[@Indently]

It looks super cool to me, I might try it in my next video! I'll play around with it first. Thanks for sharing!

[@HoSza1]

Writing start= is especially better if you use the value more than once.

[@anth5189]

How is it confusing to read?

[@the_huge_knight]

Why I need to use that "enumerate" function instead of "for _ in sth" with definition of list_index = 0 and in the cycle I can just increment that index. Indexes often are not required in iterations.

[@MrMaxtng]

Because Python is not C. For loops in Python are very slow compared to C. Enumerate is faster.

[@AWriterWandering]

You could do that, but generally your python code will be faster if you use the built-in functions when available. The reason being that these functions are frequently implemented in C, and therefore can do the job faster than pure Python.

[@InterpretingYou]

Python 11 shaving off time, by making both implementations slower 🙃 @ 3:41

[@callbettersaul]

What are you talking about? 3.10 ran both of them slower than 3.11.

[@MuitaMerdaAoVivo]

the first run was on python 11, the slower run was in python 10.