> As for that major point though, would you rather see a solution that does not scale to N results? Like, now it can give the top 3 paths but also the top N, whereas a faster solution that keeps a separate variable for the top entry cannot do that (or it needs to keep a list, but then there's more complexity and more O(n) operations). I'm not sure I agree that sorting is not a valid trade-off given the information at hand, that is, not having specified it needs to work realtime on a billion rows, for example. (Checking just now to quantify the time it takes: sorting is about 5% of the time on this 1M lines data sample.)

You need the list regardless, just do `max` instead of `sort` at the end, which is O(N) rather than O(N log N). Likewise, returning top 3 elements can still be done in O(N) without sorting (with heapq.nlargest or similar), although I agree that you probably shouldn't expect most interviewees to know about this.

As for the rest, as I've said, it depends on the candidate level. From a junior it's fine as-is, although I'd still want them to be able to fix at least some of those issues once I point them out. I'd expect a senior to be able to write a cleaner solution on their own, or at most with minimal prompting (eg "Can you optimize this?")

FYI, defaultdict and setdefault is not the same thing.

  d = defaultdict(list)
  d[key].append(value)

vs

  d = {}
  d.setdefault(key, []).append(value)

useful when you only want the "default" behavior in one piece of code but not others

  >   / -> / -> / with a count of 6120
  >   /robots.txt -> /robots.txt -> /robots.txt with a count of 4459

LOL