Palantir FDE Intern OA (3 parts)

收到oa, 一个半小时大概做了一下，没什么特别难的可以注意下rounding。所有东西都整理好了贴出来了
新人求米新人求米

Part 1 — Initial

Problem

You're building a discount system for Trader Yojoe's, a growing grocery chain. Their business is thriving, but they're starting a new pilot program where shoppers can bring coupons and apply them to items at checkout. Their current point-of-sale system doesn't support discounts, so you need to build this functionality from scratch.

Given a list of items and a list of discount coupons, create an algorithm that correctly applies the discounts during purchase and calculates the final total.

Requirements

1. Each item has a name and price. The price is in integer cents.
2. Each discount coupon has an item name it applies to and a percent off.
3. Apply matching discounts to items and return a dictionary with three keys:

• subtotal: the sum of all original item prices
  
• total_discount: the sum of all discount amounts
  
• final_total: the subtotal minus the total discount
  

Assumptions

1. There will be at most one discount coupon per item name.
2. Discounts that don't match any item in the cart are ignored.

Notes

1. Round the final discount amount for each item to the nearest cent using round(). Do not round intermediate calculations.
2. Ensure all returned values are integers. Apply the Python

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r item name.
2. There will be at most one category discount available per category.
3. You may activate any combination of available discounts, subject to the point budget of 20.

Notes

1. This is an optimization problem — focus on getting a working solution first, then improve it.
2. The auto-grader will first validate your output format, then score based on total savings achieved.
3. You will have unlimited submissions for Part 3.
4. Feel free to copy/paste your code from Part 2 or start from scratch.

Example

1. items = [
2.     {"name": "Milk", "price": 500, "category": "Dairy"}
3. ]
5. discounts = [
6.     {"type": "item", "name": "Milk", "percent_off": 20}
7. ]

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optimize_discounts(items, discounts) returns:

1. {
2.     "selected_discounts": [
3.         {
4.             "type": "item",
5.             "name": "Milk",
6.             "percent_off": 20
7.         }
8.     ],
9.     "total_points_used": 2
10. }

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apply_discounts_and_calculate_total(items, selected_discounts) returns:

1. {
2.     "subtotal": 500,
3.     "total_discount": 100,
4.     "final_total": 400
5. }

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Starter Code

1. #!/bin/python3
3. [size=6][b]Sample data[/b][/size]
5. [size=6][b]items = [[/b][/size]
6. [size=6][b]{"name": "Organic Bananas", "price": 399, "category": "Produce"},[/b][/size]
7. [size=6][b]...[/b][/size]
8. [size=6][b]][/b][/size]
10. [size=6][b]discounts = [[/b][/size]
11. [size=6][b]{"type": "item", "name": "Organic Bananas", "percent_off": 15},[/b][/size]
12. [size=6][b]{"type": "category", "name": "Produce", "percent_off": 15},[/b][/size]
13. [size=6][b]...[/b][/size]
14. [size=6][b]][/b][/size]
17. from itertools import combinations
20. POINT_BUDGET = 20
21. ITEM_DISCOUNT_PRICE = 2
22. CATEGORY_DISCOUNT_PRICE = 5
25. def get_discount_cost(discount):
26.     if discount["type"] == "item":
27.         return ITEM_DISCOUNT_PRICE
29.     return CATEGORY_DISCOUNT_PRICE
32. def optimize_discounts(items, discounts):
33.     """
34.     Determine which discounts to activate to maximize savings.
35.     """

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