Time and space complexity of sorting algorithms

Here is a visual comparison of common sorting algorithms, showing how different strategies rearrange unordered data into sorted order 😎👆 ✅ Bubble sort: Painfully slow at O(n²) — compares everything to everything. ✅ Insertion sort: Also O(n²), but smoother when data is already mostly sorted. ✅ Merge sort: Consistently fast at O(n log n) — splits, sorts, and merges like clockwork. ✅ Quick sort: Average O(n log n) and blazing fast in practice, but worst-case O(n²) if pivots go wrong. That’s why s

Visualization and Comparison of 9 Sorting Algorithms (with 4 Different Data Type) Using Python