How do you solve Count Salary Categories on LeetCode?

Count Salary Categories (LeetCode #1907) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(1) space complexity. Key insight: Using conditional aggregation can simplify counting in SQL.

What is the brute force solution for Count Salary Categories?

The brute force approach for Count Salary Categories is Brute Force, with O(n) time complexity and O(1) space complexity. In this approach, we will iterate through the income values and check each one against the salary categories. This is straightforward but inefficient as it requires multiple passes through the data. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Count Salary Categories?

Count Salary Categories uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Count Salary Categories?

Always think about how to simplify your queries. Practice writing SQL queries to get comfortable with syntax. Understand the data types and structure of the tables you're working with.

What are common mistakes when solving Count Salary Categories?

Not using GROUP BY correctly in SQL. Overcomplicating the solution with multiple queries.

#1907

Count Salary Categories

Medium

DatabaseHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n)	O(n)
Space	O(1)	O(1)

💡

Intuition

Time O(n)Space O(1)

This approach uses a single SQL query with conditional aggregation to count the accounts in each category in one go. This is efficient and concise.

⚙️

Algorithm

4 steps

1Step 1: Use a single SELECT statement with conditional aggregation.
2Step 2: Use CASE statements to categorize incomes into Low, Average, and High.
3Step 3: Count the number of accounts for each category in the same query.
4Step 4: Return the result table with counts for each category.

solution.py10 lines

1# Full working Python code
2SELECT
3    CASE
4        WHEN income < 20000 THEN 'Low Salary'
5        WHEN income BETWEEN 20000 AND 50000 THEN 'Average Salary'
6        ELSE 'High Salary'
7    END AS category,
8    COUNT(*) AS accounts_count
9FROM Accounts
10GROUP BY category;

ℹ

Complexity note: The complexity remains O(n) since we are still processing each row once, but we are doing it in a more efficient way with a single query.

1Using conditional aggregation can simplify counting in SQL.
2Understanding how to categorize data efficiently is crucial.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.