This one was fairly straight forward. Right now it's about getting back into the flow. Start developing some tooling to make easier to perform repetitive actions.
My first pass thru this was messy as I used generators, but I cleaned it up by adding another for loop with a break. This improved runtime:
TIL: Change continue to break
TODO: difflib
In [9]: %timeit find_prototype_box_common_letters(boxes)
82.5 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [10]: %timeit find_prototype_box_single_pass(boxes)
19.9 ms ± 191 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)Got it done before starting work. As always, had to refer to the Regular Expression HOWTO and regexr to hammer something out.
TIL how to pull numbers with a quick regex (h/t thread on /r/adventofcode)
Since everything we are processing is a number and in the same order, we can use re.findall to pull all numbers and create a Claim object.
def load_input_improved(lines: List[str]) -> List[Claim]:
claims = [Claim(*map(int, re.findall(r"-?\d+", line))) for line in lines]
return claimsJust got a PS4 with Red Dead and FIFA so I started Day 4 just before Day 5 came out. This was a very messy solution. I'm sure I could've done it with a better data structure. I used a default dict and appended each sleep record to that.
TODO: Use a more optimized data structure. Instead of having a list for each sleep, have a combined list per guard.
Finally a problem where we have to be conscious not not using a naive solution! My solution to part 1 wasn't the most efficient, but it got the job done. It definitely did not work out for part 2. The input string I got required me to think of an edge case as the number was coming in way too high.
Achievement unlocked: did not import a single Python library. Straight up built-ins.
In [3]: %timeit remove_unit_reaction(line)
4.06 s ± 67.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)TODO: string.ascii_lowercase
I brute forced it by working through the problem exactly as described. I'm sure there is a smarter way of doing it, but it worked.
Got part 1 using a PriorityQueue. I may have gone a bit over the top for my part 2 solution as I tried to think of a generalized way of concurrently running workers in a task queue. I have a deliverable at work in the coming week which will require me to formalize how I split up and send jobs to Airflow. Thinking through this problem in a bit more depth will hopefully help me explain it in a clearer way.
TIL: PriorityQueue
I previousy used heapq and found that interface hard to use. Even wrote a class-based wrapper around it for sivtools. Of course, there is something in the standard library that does the same thing, but a lot better. Really like the max_size parameter.
Shoutout to Dan Bader for his Priority Queues in Python article for helping me along.
I tried a bunch of ways to do it, realized that recursion makes the most sense. Need to sit down and give this another shot.
TIL: Always forget how array slicing works and have to try it in the REPL. In Python it's closed on the left, open on the right.
I misread the problem so I spent a lot of time debugging my code that was correct. Once I took a peek at Reddit, I realized that last marble is worth 1618 points means 1618 total marbles, not that placing the marble scores 1618 points.
Part 1 ran without a hitch. Part 2 is taking a while to run, this is because I'm inserting / popping from the middle of a list which is an O(n) operation in Python. The strategy I should have employed is to use a deque and rotate so that we are always inserting / popping off from the ends [i.e. O(1)], which I did when I rewrote my code for part 2.