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…)

my boh

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.

I like it. Learned from this kata the purpose of quotes.

creative!

this is much nicer

Two weeks on Codewars and I become smarter, hehe

Get CC :=)

da best

Yes!!!

At last I managed to make this one from first attempt. Hurrah! This projekt make me happy.

I like it. Made me think of new kata creating