Compressed Order Mean Estimator

Easy

Your analytics warehouse stores order volume in compressed form. Table: orders order_id: unique bucket identifier item_count: number of items represented by the bucket order_occurrences: how many real orders share that item_count Instead of storing every order row, one row summarizes many identical orders. Task: Compute the average number of items per order across the full dataset. Metric definition: weighted_items = SUM(item_count * order_occurrences) total_orders = SUM(order_occurrences) average_items_per_order = weighted_items / total_orders Output rules: Return exactly one row with one column: average_items_per_order Round to 2 decimal places If total_orders is 0, return 0.00 Supported submission environments: SQL (SQLite 3.27.2) Pandas (Python)

Example 1

Input

orders:
| order_id | item_count | order_occurrences |
|----------|------------|-------------------|
| 10       | 1          | 500               |
| 11       | 2          | 1000              |
| 12       | 3          | 800               |
| 13       | 4          | 1000              |

Output

[
  {"average_items_per_order":2.7}
]

Explanation

Weighted items = 1*500 + 2*1000 + 3*800 + 4*1000 = 8900. Total orders = 3300. Mean = 8900/3300 = 2.696..., rounded to 2.70.

Example 2

Input

orders:
| order_id | item_count | order_occurrences |
|----------|------------|-------------------|
| 20       | 2          | 250               |
| 21       | 1          | 750               |

Output

[
  {"average_items_per_order":1.25}
]

Explanation

Weighted items = 2*250 + 1*750 = 1250, total orders = 1000, so the compressed mean is 1.25.

Example 3

Input

orders:
| order_id | item_count | order_occurrences |
|----------|------------|-------------------|
| 30       | 5          | 0                 |
| 31       | 2          | 0                 |
| 32       | 9          | 0                 |

Output

[
  {"average_items_per_order":0.0}
]

Explanation

No effective orders exist because all occurrences are zero, so the denominator is 0 and the result must be 0.00.

Constraints

1 <= number of rows in orders <= 100000
order_id is unique
0 <= item_count <= 2000000
0 <= order_occurrences <= 2000000
At least one valid row is always present in the table
If SUM(order_occurrences) = 0, return 0.00
Round final metric to 2 decimal places
Use SQLite 3.27.2 semantics for SQL submissions
For Pandas submissions, return a DataFrame with one column: average_items_per_order

orders: | order_id | item_count | order_occurrences | |----------|------------|-------------------| | 10 | 1 | 500 | | 11 | 2 | 1000 | | 12 | 3 | 800 | | 13 | 4 | 1000 |

orders: | order_id | item_count | order_occurrences | |----------|------------|-------------------| | 30 | 5 | 0 | | 31 | 2 | 0 | | 32 | 9 | 0 |