I process the strings column by column from left to right. I keep track of which adjacent string pairs are already confirmed to be in correct lexicographic order.
For each column:
(strs[i][c] > strs[i+1][c]), I must delete this column.I iterate column by column. For each column, I compare characters from top to bottom. If I find any row where the character is smaller than the one above it, the column is not lexicographically sorted, so I count it as a deletion and move to the next column.
class Solution:
def minDeletionSize(self, strs: List[str]) -> int:I first compute the original profit as a baseline. Since I’m allowed at most one modification, I reframe the problem as: what is the maximum additional gain I can get by replacing any length-k subarray with a fixed pattern — first k/2 days contribute 0, last k/2 days contribute +prices[i]. For every possible window of length k, I compute the delta profit: (new contribution of the window) − (original contribution of the window). To do this efficiently, I use prefix sums:
I scan the array once while tracking the length of the current smooth descent streak. If prices[i] == prices[i-1] - 1, the streak continues and I increment its length; otherwise, I reset the streak length to 1. At each index, I add the current streak length to the answer because every prefix of the current streak forms a valid smooth descent period ending at that day. This way, all single-day and multi-day valid periods are counted in one pass.
class Solution:
def getDescentPeriods(self, prices: List[int]) -> int:I iterate through all coupons and keep only those that satisfy all validity rules: the coupon must be active, its business line must be one of the allowed categories, and its code must be non-empty and contain only alphanumeric characters or underscores. While filtering, I group valid coupon codes by their business line. Finally, I output the result by traversing the business lines in the required fixed order and appending the codes in lexicographical order within each category.
class Solution:
def validateCoupons(self, code: List[str], businessLine: List[str], isActive: List[bool]) -> List[str]:I simulate events in increasing timestamp order and, for events sharing a timestamp, I process all OFFLINE events before MESSAGE events (because status changes are applied before messages at the same time). I keep an offline_until time for each user (the time they become online again); a user is online at time T iff T >= offline_until[user]. On an OFFLINE event at time t for user id I set offline_until[id] = t + 60. On a MESSAGE event at time t I parse the mentions string token-by-token: