This was basically my first solution before I learned about %B lol

how?

Why not inline the import as well? :D

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

When running the psql postgres CLI, you do use a semicolon to let the CLI know to start processing the statement.

the bash shell interprets {1...20} to be a continous sequence and fills in the numbers between

Well, you reached 2 kyu, hopes that I can reach it too.

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.

Check mine.