Is Calculate Compressed Mean easy or hard?

Calculate Compressed Mean is classified as an Easy database problem. The main challenge is recognizing that the table represents compressed data and that the mean can be computed using a weighted average formula instead of expanding the dataset.

Calculate Compressed Mean Python/Java solution

Interview platforms typically expect a SQL query for this problem because it is tagged as a database question. In application code like Python or Java, the same logic applies: iterate through the pairs, accumulate value * frequency and total frequency, then divide to compute the weighted average.

How to solve Calculate Compressed Mean in O(n)?

Scan the compressed table once and treat each row as a weighted value. Multiply the number by its frequency to compute its total contribution, then divide the total weighted sum by the total frequency. In SQL this becomes SUM(num * frequency) / SUM(frequency).

What is the best approach for Calculate Compressed Mean?

The best approach is aggregated summation using SQL. Compute SUM(num * frequency) for the total value contribution and divide it by SUM(frequency) for the total count. This avoids expanding the dataset and runs in O(n) time with O(1) extra space.

Is Calculate Compressed Mean asked at Google/Amazon/Meta?

This type of problem appears frequently in SQL and data analytics interviews at large tech companies. Variations of weighted averages and compressed datasets are common in companies that evaluate data querying and aggregation skills.

What data structure is used in Calculate Compressed Mean?

The problem relies on a relational database table that stores compressed numeric data using value–frequency pairs. The solution uses SQL aggregation functions such as SUM along with arithmetic operations to compute a weighted mean.

What is the time complexity of Calculate Compressed Mean?

The optimal SQL solution runs in O(n) time where n is the number of rows in the compressed table. The query performs a single pass to calculate SUM(num * frequency) and SUM(frequency). Space complexity is O(1) because only aggregate counters are stored.

Description Solution Video 2:14

2985. Calculate Compressed Mean

Easy85.9% acceptance1 companyPremium elsewhere, free here

Table: Orders

+-------------------+------+
| Column Name       | Type |
+-------------------+------+
| order_id          | int  |
| item_count        | int  |
| order_occurrences | int  |
+-------------------+------+
order_id is column of unique values for this table.
This table contains order_id, item_count, and order_occurrences.

Write a solution to calculate the average number of items per order, rounded to 2 decimal places.

Return the result table in any order.

The result format is in the following example.

Example 1:

Input: 
Orders table:
+----------+------------+-------------------+
| order_id | item_count | order_occurrences | 
+----------+------------+-------------------+
| 10       | 1          | 500               | 
| 11       | 2          | 1000              |     
| 12       | 3          | 800               |  
| 13       | 4          | 1000              | 
+----------+------------+-------------------+
Output
+-------------------------+
| average_items_per_order | 
+-------------------------+
| 2.70                    |
+-------------------------+
Explanation
The calculation is as follows:
 - Total items: (1 * 500) + (2 * 1000) + (3 * 800) + (4 * 1000) = 8900 
 - Total orders: 500 + 1000 + 800 + 1000 = 3300 
 - Therefore, the average items per order is 8900 / 3300 = 2.70

Asked at 1 company

Google

Topics

Database

2:14

Still stuck?Read the written solution, or watch a walkthrough.

Open solution

LanguageLoading editor…

1class Solution:
  def solve(self):
      pass


Starter codePython 3Spaces: 4

Examples

Case 1

Input

{"headers":{"Orders":["order_id","item_count","order_occurrences"]},"rows":{"Orders":[[10,1,500],[11,2,1000],[12,3,800],[13,4,1000]]}}

Your code is saved on this device.Solution