Priority Warehouse Stock Maximization

Hard

An operations team manages a 500,000-square-foot warehouse and uses a strict two-phase stocking policy. Table: Inventory item_id: unique identifier for an item template item_type: either "prime_eligible" or "not_prime" item_category: descriptive category label square_footage: area consumed by one unit of that item template Stocking policy: First, maximize how many complete prime_eligible bundles can be stored. Then, use only the leftover space to store complete not_prime bundles. Bundle definition: A complete bundle for a type contains every row of that type exactly once. If one bundle for a type uses area A, the number of bundles is floor(available_space / A). Total items contributed by that type = bundles * number_of_rows_of_that_type. Output requirements: Return exactly two rows with columns in this order: item_type, item_count Always include both item types, even if item_count is 0 Sort by item_count in descending order If counts are tied, place prime_eligible before not_prime Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

Inventory:
| item_id | item_type      | item_category | square_footage |
|---------|----------------|---------------|----------------|
| 1374    | prime_eligible | Watches       | 68.00          |
| 4245    | not_prime      | Art           | 26.40          |
| 5743    | prime_eligible | Software      | 325.00         |
| 8543    | not_prime      | Clothing      | 64.50          |
| 2556    | not_prime      | Shoes         | 15.00          |
| 2452    | prime_eligible | Scientific    | 85.00          |
| 3255    | not_prime      | Furniture     | 22.60          |
| 1672    | prime_eligible | Beauty        | 8.50           |
| 4256    | prime_eligible | Furniture     | 55.50          |
| 6325    | prime_eligible | Food          | 13.20          |

Output

[
  {"item_type":"prime_eligible","item_count":5400},
  {"item_type":"not_prime","item_count":8}
]

Explanation

A prime bundle uses 555.20 square feet and contains 6 items, so 900 bundles fit first (5400 prime items). The remaining 320 square feet fits 2 non-prime bundles (4 items per bundle), giving 8 not_prime items.

Example 2

Input

Inventory:
| item_id | item_type | item_category | square_footage |
|---------|-----------|---------------|----------------|
| 10      | not_prime | Bulk_A        | 40.00          |
| 11      | not_prime | Bulk_B        | 75.00          |
| 12      | not_prime | Bulk_C        | 100.00         |

Output

[
  {"item_type":"not_prime","item_count":6975},
  {"item_type":"prime_eligible","item_count":0}
]

Explanation

There are no prime_eligible rows, so prime contributes 0 items and all capacity goes to not_prime bundles.

Example 3

Input

Inventory:
| item_id | item_type      | item_category | square_footage |
|---------|----------------|---------------|----------------|
| 1       | prime_eligible | Core_A        | 12500.00       |
| 2       | prime_eligible | Core_B        | 7500.00        |
| 3       | prime_eligible | Core_C        | 5000.00        |
| 4       | not_prime      | Flex_A        | 900.00         |
| 5       | not_prime      | Flex_B        | 1100.00        |
| 6       | not_prime      | Flex_C        | 1400.00        |

Output

[
  {"item_type":"prime_eligible","item_count":60},
  {"item_type":"not_prime","item_count":0}
]

Explanation

A prime bundle uses 25,000 square feet and contains 3 items, so exactly 20 prime bundles fill the warehouse (60 items), leaving no area for not_prime.

Constraints

1 <= number of rows in Inventory <= 100,000
1 <= item_id <= 1,000,000,000
item_id is unique within each test case
item_type is either 'prime_eligible' or 'not_prime'
1 <= length(item_category) <= 100
0.01 <= square_footage <= 500000.00
square_footage has at most 2 decimal places
Use complete-bundle counting with floor division semantics
Return exactly two rows with columns [item_type, item_count]
Sort by item_count DESC, then prime_eligible before not_prime
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with columns [item_type, item_count]

Inventory: | item_id | item_type | item_category | square_footage | |---------|----------------|---------------|----------------| | 1374 | prime_eligible | Watches | 68.00 | | 4245 | not_prime | Art | 26.40 | | 5743 | prime_eligible | Software | 325.00 | | 8543 | not_prime | Clothing | 64.50 | | 2556 | not_prime | Shoes | 15.00 | | 2452 | prime_eligible | Scientific | 85.00 | | 3255 | not_prime | Furniture | 22.60 | | 1672 | prime_eligible | Beauty | 8.50 | | 4256 | prime_eligible | Furniture | 55.50 | | 6325 | prime_eligible | Food | 13.20 |

Inventory: | item_id | item_type | item_category | square_footage | |---------|-----------|---------------|----------------| | 10 | not_prime | Bulk_A | 40.00 | | 11 | not_prime | Bulk_B | 75.00 | | 12 | not_prime | Bulk_C | 100.00 |

Inventory: | item_id | item_type | item_category | square_footage | |---------|----------------|---------------|----------------| | 1 | prime_eligible | Core_A | 12500.00 | | 2 | prime_eligible | Core_B | 7500.00 | | 3 | prime_eligible | Core_C | 5000.00 | | 4 | not_prime | Flex_A | 900.00 | | 5 | not_prime | Flex_B | 1100.00 | | 6 | not_prime | Flex_C | 1400.00 |