DoorDash Data Scientist Coding Questions

To improve delivery efficiency, DoorDash wants to analyze how many restaurants per square mile lead to faster delivery times. This problem can be approached using binary search over a range of possible densities.
Problem statement: Given an array of integers where each integer denotes the delivery time for different restaurant densities, and an integer target that represents the maximum acceptable delivery time, find the highest restaurant density that results in a delivery time less than or equal to the target.
Method Signature: def max_density(delivery_times: List[int], target: int) -> int: - Returns the highest feasible restaurant density.Example 1:
Input: [3, 1, 4, 2, 2, 5], target = 3
Output: 2
Explanation: A density of 2 corresponds to delivery times within the acceptable limit.Example 2:
Input: [5, 3, 6, 9, 2], target = 5
Output: 1
Explanation: The best density maintaining a delivery time under or equal to the target is 1.Constraints:
- 1 <= len(delivery_times) <= 100000
- 1 <= delivery_times[i] <= 10000
- 1 <= target <= 10000

In the fast-paced world of food delivery, DoorDash aims to minimize delivery times while optimizing routes based on real-time traffic data. This involves keeping track of delivery estimates dynamically based on recent data.
Problem statement: Given an array of integers representing the time taken for each segment of a route, return the minimum time required to complete the delivery after analyzing k continuous segments using a sliding window approach.
Method Signature: def min_delivery_time(route: List[int], k: int) -> int: - Returns the minimum time for k continuous segments.Example 1:
Input: [2, 3, 1, 5, 3, 7, 2], k = 3
Output: 6
Explanation: The segments [1, 5, 3] provide the minimum delivery time of 6.Example 2:
Input: [4, 2, 6, 3, 5], k = 2
Output: 5
Explanation: The segments [2, 3] provide the minimum delivery time of 5.Constraints:
- 1 <= len(route) <= 100000
- 1 <= route[i] <= 1000
- 1 <= k <= len(route)