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What is Sorting in Data Structure?

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In this blog, we will go through the comprehensive journey of sorting algorithms, diving into their inner workings, applications, and importance within the field of data structures and algorithms.

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What is Sorting in Data Structure?

Sorting in data structures is the process of rearranging a collection of elements in a specific order, such as numerical value, alphabetical order, or chronological sequence, to make them easier to search, retrieve, or manipulate efficiently.

Sorting is used in databases to optimize search operations, in algorithms to solve problems more efficiently, and in various data analysis tasks to organize data for better insights. By arranging data in a particular order, sorting algorithms enable faster retrieval and comparison of elements, which ultimately enhances the performance of software applications.

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Why is Sorting in Data Structure Important?

Sorting holds a crucial role in data structures due to its fundamental impact on data organization and retrieval efficiency. Here are some key points highlighting the importance of sorting in data structures:

  • Efficient Searching: Sorting enables quicker and more efficient searching of data. When data is ordered, searching in data structure provides algorithms like binary search that can be employed to dramatically reduce search time compared to unsorted data.
  • Improved Retrieval: In various applications, including databases and information retrieval systems, sorted data can be retrieved faster. This is crucial for systems handling large datasets that require rapid access to specific information.
  • Algorithm Performance: Many algorithms perform optimally on sorted data. Sorting enhances the efficiency of algorithms like merge sort and quicksort, resulting in faster processing times.
  • Identifying Patterns: Sorted data makes patterns and trends more apparent, aiding in data analysis. It simplifies tasks like identifying outliers, understanding distributions, and drawing meaningful insights.

Types of Sorting in Data Structure

Sorting in data structures can be broadly categorized into two types: internal sorting and external sorting.

Internal Sorting

Internal sorting refers to sorting data that can fit entirely within the computer’s main memory (RAM). In this type of sorting, the entire dataset to be sorted is loaded into memory, and sorting operations are performed using the available memory. Since memory access is significantly faster than accessing external storage devices, internal sorting algorithms tend to be more efficient in terms of speed.

External Sorting

External sorting is used when the dataset to be sorted is too large to fit entirely in memory. In this scenario, data must be read from and written to external storage devices, such as hard drives. External sorting aims to minimize the number of input/output operations and efficiently use available memory buffers to manage the sorting process.

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Key Sorting Algorithms in Data Structure

In the following segment, we will delve into different categories of sorting algorithms within the domain of data structures and algorithms, along with an understanding of their mechanisms. This insight will equip you to readily apply these algorithms to practical scenarios and real-world applications.

Bubble Sort

Bubble Sort stands as a straightforward sorting algorithm, methodically progressing through a list, scrutinizing neighboring elements, and effecting swaps whenever their order is wrong. The pass through the list is repeated until no swaps are needed, indicating that the list is sorted. 

Here’s the step-by-step breakdown of the Bubble Sort algorithm applied to the array [5, 1, 4, 2, 8]:

Initial Array: [5, 1, 4, 2, 8]

Pass 1:

Comparing 5 and 1: [1, 5, 4, 2, 8]

Comparing 5 and 4: [1, 4, 5, 2, 8]

Comparing 5 and 2: [1, 4, 2, 5, 8]

Comparing 5 and 8: [1, 4, 2, 5, 8]

Pass 2:

Comparing 1 and 4: [1, 4, 2, 5, 8]

Comparing 4 and 2: [1, 2, 4, 5, 8]

Comparing 4 and 5: [1, 2, 4, 5, 8]

Comparing 5 and 8: [1, 2, 4, 5, 8]

Pass 3:

Comparing 1 and 2: [1, 2, 4, 5, 8]

Comparing 2 and 4: [1, 2, 4, 5, 8]

Comparing 4 and 5: [1, 2, 4, 5, 8]

Comparing 5 and 8: [1, 2, 4, 5, 8]

Since there were no swaps in the last pass, the array is now sorted.

Here’s the corresponding Bubble Sort code in Python:

def bubble_sort(arr):
    n = len(arr)
    for i in range(n):
        swapped = False
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
                swapped = True
            print(arr)  # Print the array at each step
        if not swapped:
            break
array = [5, 1, 4, 2, 8]
bubble_sort(array)
print("Sorted Array:", array)

You can run this code to see the step-by-step process of the Bubble Sort algorithm and the final sorted array.

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Insertion Sort

Insertion Sort is a simple sorting algorithm that builds the sorted list by iteratively inserting each element into its correct position, shifting larger elements to the right. It has a quadratic time complexity of O(n^2), making it efficient for small lists or partially sorted data.

Here’s the step-by-step working process of the Insertion Sort algorithm applied to the array [5, 1, 6, 2, 4, 3]:

Initial Array: [5, 1, 6, 2, 4, 3]

Pass 1:

Key element: 1

[1, 5, 6, 2, 4, 3]

Pass 2:

Key element: 6

[1, 5, 6, 2, 4, 3]

Pass 3:

Key element: 2

[1, 2, 5, 6, 4, 3]

Pass 4:

Key element: 4

[1, 2, 4, 5, 6, 3]

Pass 5:

Key element: 3

[1, 2, 3, 4, 5, 6]

The array is now sorted.

Here’s the Insertion Sort code in 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
        print(arr)  # Print the array at each step
array = [5, 1, 6, 2, 4, 3]
insertion_sort(array)
print("Sorted Array:", array)

You can run this code to see the step-by-step process of the Insertion Sort algorithm and the final sorted array.

Selection Sort

Selection Sort is a basic sorting algorithm that repeatedly selects the smallest element from the unsorted part of the list and swaps it with the leftmost unsorted element. This process divides the list into two parts: sorted on the left and unsorted on the right. It has a time complexity of O(n^2) and is more efficient for small lists or mostly sorted data.

Here’s a simple breakdown of the Selection Sort algorithm applied to the array [7, 5, 4, 2]:

Initial Array: [7, 5, 4, 2]

Pass 1:

Minimum element: 2 (at index 3)

Swapping 7 and 2

[2, 5, 4, 7]

Pass 2:

Minimum element: 4 (at index 2)

Swapping 5 and 4

[2, 4, 5, 7]

Pass 3:

Minimum element: 5 (at index 2)

No swapping needed

The array is now sorted.

Here’s the corresponding Selection Sort code in Python:

def selection_sort(arr):
    n = len(arr)
    for i in range(n - 1):
        min_index = I
        for j in range(i + 1, n):
            if arr[j] < arr[min_index]:
                min_index = j
        arr[i], arr[min_index] = arr[min_index], arr[I]
        print(arr)  # Print the array at each step
array = [7, 5, 4, 2]
selection_sort(array)
print("Sorted Array:", array)

Merge Sort

Merge Sort is a divide-and-conquer sorting algorithm. It recursively divides the array into halves, sorts them, and then merges them back together. It ensures a stable and efficient O(n log n) time complexity, making it suitable for large datasets.

We are providing a step-by-step breakdown of the Merge Sort algorithm applied to the array [38, 27, 43, 3, 9, 82, 10]:

Initial Array: [38, 27, 43, 3, 9, 82, 10]

Step 1: Divide

Splitting the array into smaller subarrays:

Left: [38, 27, 43, 3]

Right: [9, 82, 10]

Step 2: Divide

Continuing to divide the subarrays:

Left: [38, 27]

Right: [43, 3]

Left: [9, 82]

Right: [10]

Step 3: Merge

Merging the divided subarrays in a sorted manner:

Merging [38] and [27]: [27, 38]

Merging [43] and [3]: [3, 43]

Merging [27, 38] and [3, 43]: [3, 27, 38, 43]

Merging [9] and [82]: [9, 82]

Merging [10]: [10]

Merging [9, 82] and [10]: [9, 10, 82]

Step 4: Merge

Final merge of the two sorted subarrays:

Merging [3, 27, 38, 43] and [9, 10, 82]: [3, 9, 10, 27, 38, 43, 82]

The array is now sorted.

Here’s the corresponding Merge Sort code in Python:

def merge_sort(arr):
    if len(arr) > 1:
        mid = len(arr) // 2
        left_half = arr[:mid]
        right_half = arr[mid:]
        merge_sort(left_half)
        merge_sort(right_half)
        i = j = k = 0
        while i < len(left_half) and j < len(right_half):
            if left_half[i] < right_half[j]:
                arr[k] = left_half[I]
                i += 1
            else:
                arr[k] = right_half[j]
                j += 1
            k += 1
        while i < len(left_half):
            arr[k] = left_half[I]
            i += 1
            k += 1
        while j < len(right_half):
            arr[k] = right_half[j]
            j += 1
            k += 1
        print(arr)  # Print the array at each step
array = [38, 27, 43, 3, 9, 82, 10]
merge_sort(array)
print("Sorted Array:", array)

Run this code to see the step-by-step process of the Merge Sort algorithm and the final sorted array.

Quick Sort 

QuickSort is a popular sorting algorithm that uses a divide-and-conquer approach to sort an array by selecting a pivot element and partitioning the array into two sub-arrays – elements less than the pivot and elements greater than the pivot. These sub-arrays are then recursively sorted.

Let’s walk through the QuickSort algorithm applied to the array [4, 2, 6, 5, 3, 9] with the pivot element being 5:

Initial Array: [4, 2, 6, 5, 3, 9]

Step 1: Choose Pivot and Partition

Pivot: 5

Partitioning the array:

Left: [4, 2, 3]

Pivot: [5]

Right: [6, 9]

Step 2: Recursively Sort Subarrays

Recursively sort the left and right subarrays:

Left: [2, 3, 4]

Right: [6, 9]

Step 3: Combine Sorted Subarrays

Combining the sorted subarrays and the pivot:

[2, 3, 4] + [5] + [6, 9]

Sorted array: [2, 3, 4, 5, 6, 9]

The array is now sorted.

<Image Required>

Alt text -> Quicksort 

Here’s the corresponding QuickSort code in Python, specifically using the pivot as the middle element:

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quick_sort(left) + middle + quick_sort(right)
array = [4, 2, 6, 5, 3, 9]
sorted_array = quick_sort(array)
print("Sorted Array:", sorted_array)

You can run this code to see the step-by-step process of the QuickSort algorithm and the final sorted array using the pivot element as 5 (in the middle of the array).

Heap Sort

HeapSort is a comparison-based sorting algorithm that uses the concept of a binary heap to achieve sorting. It has two main steps: building a max-heap from the input data and repeatedly extracting the maximum element from the heap to form the sorted array.

Step-by-step HeapSort algorithm is applied to the array [23, 1, 6, 19, 14, 18, 8, 24, 15]:

Step 1: Build Min-Heap

Building a min-heap involves arranging the elements in a way that satisfies the min-heap property: the value of each parent node is smaller than or equal to the values of its child nodes. Starting with the initial array [23, 1, 6, 19, 14, 18, 8, 24, 15], we will build the min-heap:

The first step is to convert the array into a nearly complete binary tree (heapify).

  • Starting from the last non-leaf node (index 4), apply the heapify procedure.
  • After heapifying the entire array, we get [1, 14, 6, 19, 15, 18, 8, 24, 23].

Heapify recursively applies the min-heap property. The key operation in heapify is to compare a parent node with its children and swap them if necessary.

Step 2: Sort the Heap

In the min-heap, the root node holds the smallest element. To sort the array in ascending order, we will repeatedly extract the minimum element from the root and replace it with the last element in the heap, then re-heapify to maintain the min-heap property.

Extract the minimum element (1) from the root and place it in the sorted portion of the array.

  • Swap 1 with the last element (23) in the heap, and remove it from the heap.
  • Heap after extraction: [14, 15, 6, 19, 23, 18, 8, 24].
  • Sorted portion: [1].

Re-heapify the heap.

  • Apply heapify to the root (index 0) to maintain the min-heap property.
  • Heap after re-heapify: [6, 15, 8, 19, 23, 18, 14, 24].

Repeat the extraction and re-heapify steps.

  • Extract 6 from the root and append it to the sorted portion.
  • Heap after extraction: [8, 15, 14, 19, 23, 18, 24].
  • Sorted portion: [1, 6].
  • Re-heapify: [8, 15, 14, 19, 23, 18, 24].

Continue the extraction and re-heapify steps until the heap is empty.

  • Extract 8, 14, 15, 18, 19, 23, 24 from the heap and add them to the sorted portion.

Step 3: Combine Sorted Elements

The sorted elements extracted from the min-heap are combined to form the final sorted array:

[1, 6, 8, 14, 15, 18, 19, 23, 24]

And that’s the sorted array using the HeapSort algorithm!

Here’s the corresponding HeapSort code in Python:

def heapify(arr, n, i):
    smallest = i
    left = 2 * i + 1
    right = 2 * i + 2
    if left < n and arr[left] < arr[smallest]:
        smallest = left
    if right < n and arr[right] < arr[smallest]:
        smallest = right
    if smallest != i:
        arr[i], arr[smallest] = arr[smallest], arr[i]
        heapify(arr, n, smallest)
def heap_sort(arr):
    n = len(arr)
    # Build min-heap
    for i in range(n // 2 - 1, -1, -1):
        heapify(arr, n, i)
    # Extract elements and sort
    for i in range(n - 1, 0, -1):
        arr[0], arr[i] = arr[i], arr[0]
        heapify(arr, i, 0)
array = [23, 1, 6, 19, 14, 18, 8, 24, 15]
heap_sort(array)
print("Sorted Array:", array)

Want to know the various methods to sort a string in Python, then do check out our blog on How to Sort a String in Python.

Complexity Analysis Comparison among Sorting Algorithms

Here’s a comparison of time complexity for various sorting algorithms in tabular form:

Sorting AlgorithmBest Case Time ComplexityAverage Case Time ComplexityWorst Case Time ComplexitySpace Complexity
Bubble SortO(n)O(n^2)O(n^2)O(1)
Insertion SortO(n)O(n^2)O(n^2)O(1)
Selection SortO(n^2)O(n^2)O(n^2)O(1)
Merge SortO(n log n)O(n log n)O(n log n)O(n)
Quick SortO(n log n)O(n log n)O(n^2)O(log n)
Heap SortO(n log n)O(n log n)O(n log n)O(1)
Radix SortO(nk)O(nk)O(nk)O(n + k)
Bucket SortO(n^2)O(n + k)O(n^2)O(n + k)

Note:

  • “n” represents the number of elements in the dataset.
  • “k” represents the range of values in the dataset.

Get a comprehensive understanding of Recursion in Data Structure with our in-depth blog post!

Applications of Sorting in Data Structure

Sorting in data structures finds applications in various domains and scenarios due to its fundamental role in optimizing data organization and enhancing algorithmic efficiency. Here are some key applications of sorting:

  • Databases: Sorting is crucial in databases for optimizing search operations. Indexes are often created using sorted keys, allowing for rapid data retrieval and efficient query processing.
  • Search Algorithms: Many search algorithms, like binary search, work efficiently on sorted data. Sorting is essential for reducing the time required to locate specific elements in large datasets.
  • Information Retrieval: Search engines and information retrieval systems benefit from sorting, as it accelerates the retrieval of relevant documents, websites, or information based on user queries.
  • Data Analysis: Sorting helps in identifying patterns, trends, and outliers in datasets. It plays a vital role in statistical analysis, financial modeling, and other data-driven fields.

Conclusion

As data continues to grow exponentially, the demand for more efficient sorting methods will grow. Innovations are likely to focus on parallel processing and distributed sorting techniques, maximizing the power of modern hardware and cloud computing. Additionally, adapting sorting algorithms to handle streaming data, where information flows in real-time, presents exciting challenges and opportunities for mankind.

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About the Author

Senior Consultant Analytics & Data Science

Presenting Sahil Mattoo, a Senior Consultant Analytics & Data Science at Eli Lilly and Company is an accomplished professional with 14 years of experience across data science, analytics, and technical leadership domains, demonstrates a remarkable ability to drive business insights. Sahil holds a Post Graduate Program in Business Analytics and Business Intelligence from Great Lakes Institute of Management.