Question

Assume that empName and empID are two parallel arrays of size numBmp that hold employee data. Write a pseudocode algorithm that sorts the emp ID array in ascending ID number order (using any sort you wish), such that the two arrays remain parallel. That is, after sorting, for all indexes in the arrays, empName [index] must still be the name of the employee whose ID is in empID [Index].

Short answer

Question: Given two parallel arrays empName and empID of size numEmp containing employee data, write a function that sorts the empID array in ascending order while keeping the two arrays parallel. Answer: To sort the empID array while keeping the empName array parallel, you can use the following function: ```python def sort_parallel_arrays(emp_name, emp_id, num_emp): for i in range(num_emp - 1): min_index = i for j in range(i + 1, num_emp): if emp_id[j] < emp_id[min_index]: min_index = j if min_index != i: emp_id[i], emp_id[min_index] = emp_id[min_index], emp_id[i] emp_name[i], emp_name[min_index] = emp_name[min_index], emp_name[i] ``` This function implements the selection sort algorithm to sort the empID array in ascending order while swapping the corresponding empName values to keep the two arrays parallel.

Step-by-step solution

Initialize the variables and the loop

Begin by setting up a for loop that goes from 0 to numEmp - 1. This loop will iterate through the entire empID array.

Find the minimum empID#index

In each iteration, find the index of the minimum empID value between the current iteration index and the rest of the empID array. We will need a nested for loop to find the minimum value. Keep track of the minimum value's index during this process.

Swap empID and empName values

If the index of the minimum value found in step 2 is not equal to the index of the current iteration in step 1, swap the values in both empID and empName arrays using a temporary variable.

Repeat for all indexes

Continue the process until the outer for loop reaches numEmp - 1. At this point, the empID array will be sorted in ascending order, and the empName array will remain parallel to the empID array. Here is the pseudocode algorithm: ``` function sortParallelArrays(empName, empID, numEmp) for i = 0 to numEmp-2 minIndex = i for j = i+1 to numEmp-1 if empID[j] < empID[minIndex] minIndex = j end if end for if minIndex != i swap empID[i] and empID[minIndex] swap empName[i] and empName[minIndex] end if end for end function ```

Key concepts

Pseudocode

Pseudocode is essentially a shorthand representation of a programming algorithm, a technique to formulate ideas clearly and concisely before committing to actual coding. It uses a mixture of natural language and conventional programming terms. This is greatly useful to convey the logic behind complex procedures without needing to be tied to a specific syntax or language constraints.
When creating pseudocode, it's important to follow a sequence that mirrors the logical steps your program will take. Use clear and logical instructions, looping structures, and conditional statements, such as loops and if-else conditions, to reflect the flow of a real code.
Pseudocode serves as a communication bridge between programmers who might use different languages, maintaining the fidelity of the core idea and algorithm logic. It’s a method to save time and reduce errors by creating a blueprint before the actual coding begins.

Sorting Algorithms

Sorting algorithms play a significant role in computer science and programming. They organize data, making it easier to search, manage, and utilize efficiently. There are several sorting algorithms available, each offering different advantages and trade-offs based on complexity and speed.
Common algorithms include:

• **Bubble Sort**: Simple but inefficient for large datasets.
• **Selection Sort**: Selects the smallest element repeatedly.
• **Quick Sort**: Efficient and widely used, using a divide-and-conquer approach.

In the given exercise, a Selection Sort is employed. It works by finding the minimum element in the unsorted portion and swapping it with the first unsorted element, moving through the whole array until all are sorted. This algorithm is easy to implement and understand, which makes it a good fit for sorting small datasets despite its inefficiency on large scales.

Employee Data

Managing and processing employee data programmatically often involves using arrays or similar data structures. In this context, two parallel arrays are used: `empName` for storing employee names, and `empID` for their IDs. This approach allows related data to be accessed in sync through shared indexes.
Using parallel arrays can simplify data management, especially when the data is accessible via consistent indexing methods. Operations on such arrays are straightforward because you only need to ensure that any change to the order or value in one array is mirrored in another. This ensures data integrity, maintaining logical relationships between different types of information.
For example, sorting based on one array, like `empID`, will require handling the second array, `empName`, alongside it. This coordinated handling maintains the connection between each employee's name and their corresponding ID.

Index Manipulation

Index manipulation refers to the ability to systematically change the position of elements within an array or between parallel arrays. It's a core part of sorting operations and other algorithms dealing with data reordering.
Considerations for index manipulation include:

• **Direct Indexing**: Directly modify or access elements via their index in the array.
• **Swapping**: Exchange elements between two indices to alter order, as seen in sorting algorithms.
• **Tracking**: Maintain the correctness of data relationships while reordering.

In our scenario, to sort arrays and keep them parallel, you must track indices carefully during swaps. It's crucial that changes made to indices in `empID` are also applied to `empName`, preserving the established order and pairing of data. Missteps in index manipulation could lead to "orphaned" data, where records don't align correctly across parallel arrays.