Implementing the QuickSort Algorithm in Python
Quick Sort is based on the "divide and conquer" principle, with the core being selecting a pivot value to partition the array and recursively sorting the subarrays. The basic idea is: select a pivot value (e.g., the first element of the array), partition the array into two parts—elements less than and greater than the pivot—and then recursively process the subarrays. The partitioning process is critical: using left and right pointers to traverse, the right pointer moves left to find elements smaller than the pivot, while the left pointer moves right to find elements larger than the pivot. After swapping these elements, the process continues until the pointers meet. The pivot is then swapped to its final position, completing the partition. In Python implementation, the `partition` function determines the pivot position, and `quick_sort` recursively processes the left and right subarrays. Test code verifies the sorting effect. Complexity: Average case O(n log n) (when partitioning is balanced), worst case O(n²) (e.g., sorted arrays with the pivot chosen as the first element, which can be optimized by randomly selecting the pivot). Quick Sort is an efficient and practical sorting algorithm widely applied in real-world scenarios. Understanding its partitioning logic and recursive process is key to mastering sorting algorithms.
Read MoreFrom Insertion Sort to Quick Sort: A Beginner's Comparison of Sorting Algorithms
Sorting algorithms are methods to convert unordered data into ordered sequences. They are fundamental core algorithms in computer science, enabling optimization of subsequent operations such as searching and statistics. This article introduces four typical sorting algorithms: Insertion sort is similar to sorting playing cards, gradually building an ordered sequence. It has a time complexity of O(n²), space complexity of O(1), is stable, and is suitable for small-scale or nearly ordered data. Bubble sort involves comparing and swapping adjacent elements, allowing larger elements to "bubble up". It also has O(n²) time complexity, is stable but inefficient, and is only suitable for extremely small-scale data or educational purposes. Merge sort is based on the divide-and-conquer principle, decomposing arrays and merging ordered subarrays. It has O(n log n) time complexity, O(n) space complexity, is stable, and is suitable for large-scale data or scenarios requiring high stability. Quick sort combines divide-and-conquer with pivot partitioning. It has an average time complexity of O(n log n), O(log n) space complexity, is unstable, and is the most commonly used efficient algorithm in engineering, suitable for large-scale data. The article compares and summarizes the time complexity, space complexity, stability, and applicable scenarios of these algorithms. It suggests that beginners first understand the core ideas, learn from simple to complex cases gradually, and deepen their understanding through hands-on simulation.
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