1.
Introduction
2.
What is a stable sorting algorithm?
3.
Do we care for simple arrays like the array of integers?
3.1.
Python
4.
Where stable sorting algorithms are useful?
4.1.
Python
5.
Which sorting algorithms are stable?
6.
Bubble Sort
6.1.
Python
7.
Insertion Sort
7.1.
Python
8.
Merge Sort
8.1.
Python
9.
Tim Sort
9.1.
Python
10.
Can we make any sorting algorithm stable?
10.1.
Making Quick Sort Stable
10.2.
Python
11.
11.1.
Why is stability important in sorting algorithms?
11.2.
Can stability affect the performance of a sorting algorithm?
11.3.
How can I determine if a sorting algorithm is stable?
12.
Conclusion
Last Updated: Mar 27, 2024
Easy

# Stable Sorting Algorithm

Rinki Deka
0 upvote

## Introduction

When it comes to sorting data, the method we choose can significantly impact the efficiency and outcome of our tasks. Sorting algorithms are fundamental tools in computer science, used to organize data in a specific order. Among these, stable sorting algorithms hold a special place for their ability to maintain the relative order of records with equal keys.

In this article, we'll explore what stable sorting algorithms are, explore their importance in various scenarios, and highlight some common stable sorting techniques. By the end, you'll have a comprehensive understanding of stable sorting algorithms and where they shine the most.

## What is a stable sorting algorithm?

A stable sorting algorithm is one where two objects with equal keys appear in the same order in the sorted output as they appear in the input array to be sorted. To put it simply, if two elements are equal in the context of the sorting order (like two students having the same score), a stable sort will ensure that their original relative order is preserved in the sorted list.

For instance, consider an array of student records where each record contains the student's name and score. If we sort this array by score using a stable sorting algorithm, students with the same score will remain in the same order as they were in the original array. This characteristic is crucial in scenarios where the order of equivalent elements carries important information that we want to retain even after sorting.

Stable sorting algorithms are particularly useful when we perform multiple rounds of sorting. For example, if we first sort the student records by name and then by score, using a stable sort for the second round ensures that students with the same score are still sorted by their names in the final list.

## Do we care for simple arrays like the array of integers?

At first glance, it might seem that stability in sorting algorithms is not necessary when dealing with simple data structures like an array of integers, where each element is distinct and does not carry additional information. However, even in these cases, the concept of stability can be beneficial.

Imagine we have a list of tasks represented by integers, where each integer corresponds to a unique task ID. These tasks are grouped by priority levels, but within each priority level, the tasks are ordered by their creation time (earlier tasks first). Our goal is to sort these tasks by priority level while preserving the creation order within each priority group.

Here's a Python code snippet to illustrate this scenario:

• Python

### Python

``# Task IDs with their priority levels (Task ID, Priority Level)tasks = [    (101, 3),  # Task 101 with priority 3    (102, 2),  # Task 102 with priority 2    (103, 3),  # Task 103 with priority 3    (104, 1),  # Task 104 with priority 1    (105, 2)   # Task 105 with priority 2]# Custom sort function that uses priority as the key# This sort needs to be stable to maintain the creation order within each priority leveldef stable_sort_by_priority(tasks):    # Sorting by priority level, but preserving the original order within each priority    tasks_sorted = sorted(tasks, key=lambda x: x[1])    return tasks_sorted# Perform the stable sortsorted_tasks = stable_sort_by_priority(tasks)# Display the sorted tasksprint("Tasks sorted by priority while preserving creation order within each priority level:")for task in sorted_tasks:    print(f"Task ID: {task[0]}, Priority Level: {task[1]}")``

Output

In this example, the sorted function in Python is inherently stable, which means that tasks with the same priority level will remain in the same order as they were in the original list, effectively preserving the creation order within each priority group. This is crucial because even though we're dealing with a simple array of integer pairs, the stability of the sorting algorithm ensures that our secondary sorting criterion (the implicit creation order) is respected.

This demonstrates that even in scenarios involving simple data structures like arrays of integers, the stability of the sorting algorithm can play a significant role, particularly when the order of elements carries implicit information or significance beyond their face value.

## Where stable sorting algorithms are useful?

Stable sorting algorithms shine in contexts where the order of equivalent elements is significant, especially when sorting complex data structures. A classic example is sorting a list of records based on multiple fields.

Imagine we have a list of employee records, where each record contains an employee's department, name, and years of service. We might want to sort these records in multiple steps: first by department, then by name, and finally by years of service. In such multi-level sorting, stable sorting algorithms ensure that the order determined by previous sorting criteria is preserved when subsequent sorts are applied.To illustrate this with code, let's consider a simple Python example using the sorted function, which is stable. We'll sort a list of tuples representing employees, where each tuple contains (department, name, years of service):

• Python

### Python

``# List of employee records: (Department, Name, Years of Service)employees = [    ('HR', 'Alice', 5),    ('IT', 'Bob', 3),    ('HR', 'Charlie', 2),    ('IT', 'Dana', 5),    ('HR', 'Eve', 7)]# First, sort by departmentsorted_by_dept = sorted(employees, key=lambda x: x[0])# Then, within each department, sort by years of servicesorted_by_dept_and_years = sorted(sorted_by_dept, key=lambda x: x[2])print("Sorted by Department and then Years of Service:")for record in sorted_by_dept_and_years:    print(record)``

Output

In this code, sorted_by_dept first organizes the records by department. Because sorted is stable, when we sort sorted_by_dept by years of service, records within the same department remain in the order they were sorted previously (by name in this case). This ensures that within each department, employees are listed in ascending order of their years of service, but if they have the same years of service, they are listed in alphabetical order.

This example demonstrates the utility of stable sorting in preserving the order of elements when sorting by multiple criteria. The ability to maintain this order is crucial in many real-world applications, such as database queries, report generation, and data analysis, where the meaning and relationships within the data must be preserved through various sorting operations.

## Which sorting algorithms are stable?

In sorting algorithms, stability is a property that distinguishes various approaches based on how they treat equal elements. Stable sorting algorithms maintain the relative order of records with equal keys, which can be crucial for certain applications. Let's explore some of the common stable sorting algorithms:

## Bubble Sort

Bubble Sort is a straightforward sorting algorithm that repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order. The process is repeated until the list is sorted. Bubble Sort is stable because it only swaps elements if they are in the wrong order, ensuring that equal elements retain their original sequence.

Code Example:

• Python

### Python

``def bubble_sort(arr):    n = len(arr)    for i in range(n-1):        for j in range(0, n-i-1):            if arr[j] > arr[j+1]:                arr[j], arr[j+1] = arr[j+1], arr[j]# Sample arrayarr = [64, 34, 25, 12, 22, 11, 90]bubble_sort(arr)print("Sorted array:", arr)``

Output

This code snippet demonstrates Bubble Sort in action. The bubble_sort function iteratively compares and swaps adjacent elements if they are in the wrong order, ensuring the stability of the sort.

## Insertion Sort

Insertion Sort builds the final sorted array (or list) one item at a time. It works by picking each element in the array and placing it in its correct position within the already sorted part of the array. This ensures that equal elements are never leapfrogged over each other, maintaining their relative order and thus the stability of the algorithm.

Code Example:

• Python

### Python

``def insertion_sort(arr):    for i in range(1, len(arr)):        key = arr[i]        j = i-1        while j >=0 and key < arr[j]:            arr[j+1] = arr[j]            j -= 1        arr[j+1] = key# Sample arrayarr = [12, 11, 13, 5, 6]insertion_sort(arr)print("Sorted array:", arr)``

Output

In this example, the insertion_sort function iteratively places each element in its correct position within the sorted portion of the array, ensuring stability by not moving elements past any equal elements.

## Merge Sort

Merge Sort is a divide-and-conquer algorithm that divides the unsorted list into n sublists, each containing one element (a list of one element is considered sorted), and then repeatedly merges these sublists to produce new sorted sublists until there is only one sublist remaining. Merge Sort is stable as it preserves the original order of equal elements during the merging process.

Code Example:

• Python

### Python

``def merge_sort(arr):    if len(arr) > 1:        mid = len(arr) // 2        L = arr[:mid]        R = arr[mid:]        merge_sort(L)        merge_sort(R)        i = j = k = 0        while i < len(L) and j < len(R):            if L[i] <= R[j]:                arr[k] = L[i]                i += 1            else:                arr[k] = R[j]                j += 1            k += 1        while i < len(L):            arr[k] = L[i]            i += 1            k += 1        while j < len(R):            arr[k] = R[j]            j += 1            k += 1# Sample arrayarr = [38, 27, 43, 3, 9, 82, 10]merge_sort(arr)print("Sorted array is:", arr)``

Output

This code demonstrates Merge Sort, where the merge_sort function recursively divides and merges the array, ensuring stability by preserving the order of equal elements during the merge.

## Tim Sort

Tim Sort is a sophisticated hybrid stable sorting algorithm derived from Merge Sort and Insertion Sort. It is designed to perform optimally on many kinds of real-world data. Tim Sort takes advantage of existing order in data to minimize work, making it efficient and stable, as it uses Merge Sort to combine elements, thereby preserving their original order.

Code Example:

Python's built-in sort() and sorted() functions use Tim Sort under the hood. Here's an example using sorted() to demonstrate Tim Sort's stability:

• Python

### Python

``# List of tuples (Name, Age)people = [('Alice', 30), ('Bob', 25), ('Charlie', 25), ('Dana', 20)]# Sorting by age using Python's built-in sorted() function, which employs Tim Sortsorted_people = sorted(people, key=lambda x: x[1])print("People sorted by age:")for person in sorted_people:    print(person)``

Output

In this example, sorted_people retains the original order of 'Bob' and 'Charlie' since they have the same age, demonstrating the stability of Tim Sort.

## Can we make any sorting algorithm stable?

Not all sorting algorithms are stable by design. However, with certain modifications or additional steps, it's possible to enhance an inherently unstable sorting algorithm to make it stable. This usually involves augmenting the data being sorted with additional information that allows the original order of equal elements to be preserved throughout the sorting process.

One common approach is to add a secondary key to the data that acts as a tie-breaker for equal elements. This secondary key is typically the original position of each element in the dataset. By sorting with respect to this augmented data, we can ensure that when the primary keys are equal, the original order is maintained by the secondary key, thus achieving stability.

Let's demonstrate this concept with a code example using Quick Sort, which is not stable by default. We'll augment each element with its original index, sort by the primary key, and use the original index as a tie-breaker to maintain stability.

• Python

### Python

``def stable_quick_sort(arr, start, end, original_indices):    if start < end:        pIndex = partition(arr, start, end, original_indices)        stable_quick_sort(arr, start, pIndex-1, original_indices)        stable_quick_sort(arr, pIndex+1, end, original_indices)def partition(arr, start, end, original_indices):    pivot = arr[end]    pIndex = start    for i in range(start, end):        if arr[i] < pivot or (arr[i] == pivot and original_indices[i] < original_indices[end]):            arr[i], arr[pIndex] = arr[pIndex], arr[i]            original_indices[i], original_indices[pIndex] = original_indices[pIndex], original_indices[i]            pIndex += 1    arr[pIndex], arr[end] = arr[end], arr[pIndex]    original_indices[pIndex], original_indices[end] = original_indices[end], original_indices[pIndex]    return pIndex# Sample arrayarr = [4, 2, 5, 3, 5, 3, 2]original_indices = list(range(len(arr)))stable_quick_sort(arr, 0, len(arr) - 1, original_indices)print("Stably sorted array:", arr)``

Output

In this example, arr is the array to be sorted, and original_indices is an array of the same length as arr, storing each element's original index. The stable_quick_sort function sorts arr using Quick Sort, but the partitioning step considers both the element value and its original index. This ensures that when two elements are equal, their original order (as determined by original_indices) is used to break the tie, thereby maintaining stability.

This technique can be applied to other inherently unstable sorting algorithms, such as Heap Sort or Quick Sort, to enhance them for scenarios where stability is desired.

### Why is stability important in sorting algorithms?

Stability in sorting algorithms ensures that two equal elements maintain their original order relative to each other after sorting. This is crucial in scenarios where the order of elements carries additional meaning, such as when sorting records by multiple fields. Stability ensures that the secondary order is preserved when primary keys are equal.

### Can stability affect the performance of a sorting algorithm?

Adding stability to an inherently unstable sorting algorithm can slightly impact its performance due to the additional complexity of maintaining the original order of equal elements. However, in many real-world applications, the benefits of preserving this order outweigh the minor performance costs.

### How can I determine if a sorting algorithm is stable?

To test stability, sort a sequence of items that have equal sort keys but different secondary attributes. After sorting, if the relative order of items with equal keys is preserved from the original sequence, the algorithm is stable.

## Conclusion

Stable sorting algorithms play a vital role in the world of computing, especially in applications where the relative order of records with equal keys must be maintained. Understanding these algorithms, their importance, and how to implement or modify them for stability is crucial for developers, particularly those dealing with complex data sorting requirements. Whether you're sorting simple integer arrays or complex objects based on multiple criteria, stability can be a key factor in ensuring your data remains ordered in a meaningful way. By grasively understanding and applying stable sorting algorithms, coding students and professionals alike can tackle a wide range of sorting challenges with confidence.

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