this is like bug = debugged lol

So you're saying python isn't for people who want to solve algorithms in O(n) time where possible?

Wow...how far I have to go still.

Just make a research with the names of the methods you don't know and you should find all the informations about it.

Someone can explain me this please? ^^

It's a solution of a programmer knowing his language.

12 Best practices and 18 Clever votes
don't you think this is a typewriter operator, not a programmer, solution?

rly? well, maybe it's clever but not sophisticated.

Oh boy. I really need to take a closer look to the built-in methods.
This is so obvious and I wrote like a mile of code. - Thank you for this solution.

Thanks. For now information is enough.

What complexity is about: how does the time processing grow against data growth.

Think about how many times you have to parse data. If you parse it once, that's O(n). Twice should be O(2n) then, but we consider it as O(n) cause time processing still grows proportionally with data size. That's what we want to know. If you parse the whole data for each item, the processing time grows quadratically with data, that's O(n²). You can see n as number of items.

So from here, you can deduce we also can have O(1): you don't parse data; O(n³): you parse whole data for each item for each item; O(n!): you parse whole remaining data for each item, O(log(n)): you parse half remaining data on each iteration; etc.

You'll easily find documentations, graphs, tutorials, and so on (even hundreds pages book). Just look for it :)

(If needed, a real pro will complete, precise, or correct…)

I want to know more about code complexity. How do you know if it O(n) or higher? I did this solution as described here, but I did it cause codewars makes me think in that way, you know.

just like all people, worked for and 30min to solve normally, but very much thanks to your solution. it helps me and so many people. Thank you ;-)

wow

The duplicate kata has been retired.