Arrays & Sorting Overview

An Array is a fundamental linear data structure that stores collection of elements sequentially in contiguous memory locations. Because memory addresses are calculated directly via index math, accessing any item takes instant constant time $O(1)$.

         Key Array Characteristics:
        Direct Index Access: Accessing array[i] is $O(1)$.

            Insertion / Deletion: Inserting or deleting in the middle requires
            shifting elements: $O(n)$.
          
            Contiguous Memory: Elements are placed side-by-side in RAM, making
            arrays CPU-cache friendly.

Explore Sorting Algorithms

Click on any card below to open its dedicated deep-dive theory sheet with step-by-step code and execution details:

Bubble Sort

Repeatedly steps through the list, compares adjacent elements, and swaps them if they are in the wrong order.

Simple • $O(n^2)$

Quick Sort

Divide-and-conquer algorithm that selects a 'pivot' element and partitions the array around it.

Fast • $O(n \log n)$

Insertion Sort

Builds the sorted array one item at a time. Extremely efficient for small or nearly-sorted datasets.

Adaptive • $O(n^2)$

Merge Sort

Recursively splits array in halves, sorts each half, and merges them back together predictably.

Stable • $O(n \log n)$

Sorting Algorithms Comparison

Here is a summary of theoretical performance, space complexity, and properties across different algorithms:

Algorithm	Best Time	Average Time	Worst Time	Space	Stable?	In-Place?
Bubble Sort	$O(n)$	$O(n^2)$	$O(n^2)$	$O(1)$	Yes	Yes
Insertion Sort	$O(n)$	$O(n^2)$	$O(n^2)$	$O(1)$	Yes	Yes
Quick Sort	$O(n \log n)$	$O(n \log n)$	$O(n^2)$	$O(\log n)$	No	Yes
Merge Sort	$O(n \log n)$	$O(n \log n)$	$O(n \log n)$	$O(n)$	Yes	No