yep, but when you create your own solution, with own seing, thats cool! like you creating your own library

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

Well since we are talking about words/sentences, they can be only so long, so I don't see why performance would play a huge part in this code. At least for the test cases both solutions are almost equal (sorted even being a bit faster).

I had this originally but it fails if the two words are the same as it returns True. It seems that the problem does not count a word as an anagram of iteself, so it would be asking for a False return if the two words were "Shops" and "sHops".

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.

Geat Job bro!

Same

#11b35/3qb:0z:vV

it is the tradeoff for using a scrited language. it is almost impossible to do
anything without headers in c.

I had the same idea but yours is cleaner. Well done.