What is the brute force solution for The Number of Employees Which Report to Each Employee?

The brute force approach for The Number of Employees Which Report to Each Employee is Brute Force, with O(n²) time complexity and O(1) space complexity. In this approach, we will iterate through each employee and count how many employees report to them by checking the entire list of employees. This is straightforward but inefficient for larger datasets. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve The Number of Employees Which Report to Each Employee?

The Number of Employees Which Report to Each Employee uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for The Number of Employees Which Report to Each Employee?

Always clarify the problem requirements and constraints before jumping into coding. Think about edge cases, such as employees with no reports or multiple levels of management. Explain your thought process clearly while coding; it shows your understanding and helps the interviewer follow along.

What are common mistakes when solving The Number of Employees Which Report to Each Employee?

Not checking for null values in reports_to, which can lead to errors. Failing to round the average age correctly.

#1731

The Number of Employees Which Report to Each Employee

Easy

DatabaseHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

In the optimal approach, we can use a single pass to create a mapping of managers to their reports. This reduces the number of iterations needed, making it much more efficient.

⚙️

Algorithm

4 steps

1Step 1: Create a mapping of each employee's reports using a HashMap.
2Step 2: For each employee, if they have reports, calculate the count and average age using the mapping.
3Step 3: Store the results in a list.
4Step 4: Sort the results by employee_id.

solution.py23 lines

1# Full working Python code
2import math
3from collections import defaultdict
4
5def employee_report(employees):
6    report_map = defaultdict(list)
7    for emp in employees:
8        if emp['reports_to'] is not None:
9            report_map[emp['reports_to']].append(emp)
10    result = []
11    for emp in employees:
12        if emp['employee_id'] in report_map:
13            reports = report_map[emp['employee_id']]
14            reports_count = len(reports)
15            total_age = sum(r['age'] for r in reports)
16            average_age = round(total_age / reports_count)
17            result.append({
18                'employee_id': emp['employee_id'],
19                'name': emp['name'],
20                'reports_count': reports_count,
21                'average_age': average_age
22            })
23    return sorted(result, key=lambda x: x['employee_id'])

ℹ

Complexity note: The time complexity is O(n) because we only iterate through the list of employees a couple of times. The space complexity is O(n) due to the additional storage for the report mapping.

1Understanding the structure of the data is crucial for efficient processing.
2Using a HashMap can significantly reduce the time complexity by allowing quick lookups.

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