Advent of Code - Wrap Up

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I know, I know… it’s 25 days of Christmas not 20. But the holidays got busy and I tried to catch up in the new year, but eventually I wanted to move on to a new project, so I finally called it after day 20. Maybe I’ll poke through the last 5 as the year goes on… maybe.

Overall, this was a really fun first just-for-me code adventure! The problems are interesting and challenging, the story line keeps it more engaging than generic LeetCode problems, and I enjoyed the Reddit community of people solving the same problems at the same time. So with that, here is a quick summary of some of the interesting stuff I learned along the way!

Some useful things I learned

Collections

• The Python Collections module has several useful items such as counter and defaultdict.
• The counter object can tally the number of occurences of items in a list. It returns a dictionary with the items as keys and the values as the number of times the item occured in the list.
• You can also use counter to generate a list with a specified number of each element.
• defaultdict is a dictionary object but lets you define a default value for items that are not yet defined. This makes it easier to avoid a KeyError when accessing items in the dictionary, because if a key does not exist, it is created with the default value.

Efficiency

• String concatenations in Python are expensive. Instead, you can create a list of the strings you want to concatenate, and then use join to combine them into one string.
• Using break statements in code can improve performance if you are using loops to search for a solution to something. Despite my long held belief that breaks should be avoided, in these situations it can save time so that you don’t do unnecessary iterations of the loop.

Convenient Python Tricks

• You can use integer math to avoid having to cast the result of math to integers. For example a // b is the integer result of a divided by b.
• List.index() can be used to get the index of the first instance of an element in a list.
• itertools.product() returns the cartesian product of the input, which is the set of all pairs of items in the input.
• Use enumerate to avoid having to manually track an index when looping through a list.
• Regex groups (not just Python). When you include parentheses in the Regex, only the items in the parentheses will be returned, but the whole regex will still be matched.
• functools.cache is a built-in function cache that when used, creates a dictionary for the function arguments and stores the function output as their value.
• zip() takes any number of iterators as an argument, and returns the pairs (or groups) of each element at each position in the iterators.
• heapq is a built-in priority queue in Python, implemented as a minimum heap.

Tricky Python Tricks

• Tuple comparisons are done elementwise, so comparing one tuple to another (in terms of >, <, ==), the first element of each tuple is compared, then the second, and so on.
• Copy behavior. A shallow copy of a list duplicates the outermost level of the list, but the items within the list reference the same object in memory. This means if you shallow copy a list of lists (an array), the rows will be duplicated, but the values in each row will still be a pointer to the original row. To avoid this issue, you have to use deepcopy instead.
• Overloading print. In Python, we overload __str__ or __repr__ and then when the object is passed to print(), whatever is returned by these is what is printed. print() will look for __str__ first, and then use __repr__ if it does not find it.

Callista Krebs, 2024