Hahahhahahahahahahhahahahahahahhahhahaa

ez

Really good use of a dictionary

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cool

This helped a lot. Thanks. Haha

Great solution.

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

TypeError: 'int' object is not iterable ???

I thought of this when I looked at kyu level again after having a timeout error. No way you need to use algos for this level.

timeout error + 8 kyu = there's a simple brutforce

nice

Yet, this mutation is quite harmless for it adds an additional element and it doesn't actually modify any existing data. In that specific case, I would assume that adding data is actualy the requirement rather than not.

I brute forced it

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…)