2798. Number of Employees Who Met the Target

EasyArrayEnumeration

Problem Description

In this problem, there is a company with n employees, each of whom is assigned a unique number from 0 to n - 1. We are provided with an array called hours, which is indexed from 0. The value at each index i in this array represents the number of hours employee i has worked. The company has also set a minimum required number of hours that each employee must meet or exceed, which is specified by a target variable.

The objective is to determine how many employees have worked for at least target hours. This is an example of a counting problem where we need to count the number of elements in an array that meet a particular condition.

Intuition

The intuition behind the solution is very straightforward. Since we need to count the number of employees who have met or exceeded the target hours, we can iterate through the hours array and compare each employee's hours with the target. Every time we find an employee whose hours are greater than or equal to target, we increment our count.

We arrive at the solution approach by recognizing that it's a direct application of the concept of iteration and comparison. Each employee's hours worked are independently compared to target, and if they satisfy the condition x >= target, they are included in our count.

In Python, this can be neatly expressed in a single line using a generator expression inside the sum() function, which iterates through each element x in hours and evaluates the condition x >= target. The sum() function effectively counts the number of True evaluations, which corresponds to the number of employees who have met the target hours.

Solution Approach

The implementation of the solution employs a simple approach that does not necessarily rely on complex algorithms, data structures, or patterns. It uses Python's built-in features to perform the operation efficiently and with clarity.

The solution is to use a generator expression inside the sum() function. A generator expression is a concise way to create a generator on the fly without the overhead of loops or the need to define a generator function. In this case, the expression (x >= target for x in hours) generates a sequence of boolean values (True or False). Each boolean value corresponds to whether an individual employee's hours worked is greater than or equal to the target.

The sum() function then takes this sequence of booleans and counts the number of True values. In Python, True is equivalent to 1 and False is equivalent to 0 when performing arithmetic operations, so summing a sequence of booleans effectively counts the number of True values.

To step through the code:

hours is a list of integers, where each integer represents the hours worked by an employee.
target is an integer representing the target number of hours that employees should meet or exceed.
The generator expression (x >= target for x in hours) iterates over each element x in hours.
For each x, it checks whether x >= target. This returns True if the condition is met, and False otherwise.
The sum() function adds up all the True values, effectively counting them.
The final returned value is the total count of employees who have worked at least target hours.

No additional data structures are needed for this approach, and its time complexity is O(n), where n is the number of employees, since it involves a single pass through the hours list.

Ready to land your dream job?

Unlock your dream job with a 2-minute evaluator for a personalized learning plan!

Start Evaluator

Example Walkthrough

Let's use a small example to depict how the solution approach is applied to determine the number of employees who have met or exceeded the target number of hours.

Suppose we have a company with 5 employees and the following hours they have worked: hours = [6, 2, 9, 4, 7]. The company has set a minimum required number of hours (target) to be 5.

Now, let's walk through the solution step by step:

We have our hours list: [6, 2, 9, 4, 7].
Our target is 5.
We construct a generator expression: (x >= target for x in hours) which we will use to iterate over the hours.
As we iterate through the hours, we compare each value (each employee's hours) with the target:
- 6 >= 5 --> True
- 2 >= 5 --> False
- 9 >= 5 --> True
- 4 >= 5 --> False
- 7 >= 5 --> True
We then pass the generator expression to the sum() function, which will process the boolean values:
- sum([True, False, True, False, True]) is the same as sum([1, 0, 1, 0, 1])
The sum() function counts the number of 1s (or True values), which is 3 in this case.

The final output of this operation is 3, which means 3 employees have worked at least the target number of hours 5. The steps we followed are not only intuitive but also concise and efficient, resulting in an elegant solution to our counting problem.

Solution Implementation

1class Solution:
2    def number_of_employees_who_met_target(self, hours: List[int], target: int) -> int:
3        # Initialize the count of employees who meet or exceed the target hours
4        count_met_target = 0
5      
6        # Iterate through the list of employee hours
7        for employee_hours in hours:
8            # If an employee meets or exceeds the target, increment the counter
9            if employee_hours >= target:
10                count_met_target += 1
11      
12        # Return the total count of employees who met or exceeded the target hours
13        return count_met_target
14

1class Solution {
2    // Method to count the number of employees who have met or exceeded the target number of hours
3    public int numberOfEmployeesWhoMetTarget(int[] hoursWorked, int targetHours) {
4        // Initialize a counter to keep track of the employees meeting the target
5        int numberOfEmployeesMeetingTarget = 0;
6
7        // Iterate through the hours worked by each employee
8        for (int hours : hoursWorked) {
9            // If the employee has worked hours greater than or equal to the target, increment the counter
10            if (hours >= targetHours) {
11                numberOfEmployeesMeetingTarget++;
12            }
13        }
14
15        // Return the total count of employees who have met or exceeded the target hours
16        return numberOfEmployeesMeetingTarget;
17    }
18}
19

1class Solution {
2public:
3    // This function counts and returns the number of employees who have worked at least 'target' number of hours.
4    int numberOfEmployeesWhoMetTarget(vector<int>& hours, int target) {
5        int count = 0; // Initialize the count of employees meeting the target
6      
7        // Iterate over the vector containing hours worked by each employee
8        for (int workedHours : hours) {
9            // If the current employee's hours are greater than or equal to the target, increment the count
10            if (workedHours >= target) {
11                count++;
12            }
13        }
14      
15        // Return the final count of employees who met or exceeded the target hours
16        return count;
17    }
18};
19

1// Calculates the number of employees who have met or exceeded a target number of work hours.
2// @param {number[]} workHours - An array representing the number of hours worked by each employee.
3// @param {number} targetHours - The target number of hours that employees should meet or exceed.
4// @returns {number} The count of employees who have met or exceeded the target hours.
5function numberOfEmployeesWhoMetTarget(workHours: number[], targetHours: number): number {
6    let count = 0; // Initialize a counter for the employees meeting the target.
7  
8    // Iterate over the array of work hours.
9    for (const hoursWorked of workHours) {
10        // If this employee's hours are greater than or equal to the target, increment the counter.
11        if (hoursWorked >= targetHours) {
12            count++;
13        }
14    }
15  
16    return count; // Return the total count of employees meeting the target.
17}
18

Time and Space Complexity

Time Complexity:

The given code has a single list comprehension that iterates over the list of hours once and checks if each element is greater than or equal to the given target. This operation is O(n) where n is the number of elements in the hours list. Hence, the time complexity of the code is O(n).

Space Complexity:

This code does not use any additional space that scales with the size of the input except for a few variables to keep track of the sum. As a result, the space complexity is O(1) using a constant amount of extra space.

Learn more about how to find time and space complexity quickly using problem constraints.

Discover Your Strengths and Weaknesses: Take Our 2-Minute Quiz to Tailor Your Study Plan:

Question 1 out of 10

Which algorithm is best for finding the shortest distance between two points in an unweighted graph?

Depth-First Search

Breadth-First Search

Binary Search

Priority Queue

I don’t know