Tools: I Implemented Every Sorting Algorithm in Python — And Python's Built-in Sort Crushed Them All

The Surprise Results (Real Benchmarks) ## Why Hand-Written Sorts Lose in Python ## The Verdict ## Want the full deep dive? ## Run the benchmarks yourself Last month, I went down a rabbit hole: I implemented six classic sorting algorithms from scratch in pure Python (Bubble, Selection, Insertion, Merge, Quick, Heap) and benchmarked them properly on CPython. I expected the usual Big-O story. What I got was a reality check: Python's interpreter overhead changes everything. Textbooks say Quick/Merge/Heap are fast. In Python? They're okay... but sorted() (Timsort) beats them by 5–150×. Here's why — and when you should never write your own sort. I tested on random, nearly-sorted, reversed, and duplicate-heavy data using timeit with warm-ups, median timing, and GC controls. Example: Insertion sort (often the small-data winner): Never implement your own sort in production Python. Use sorted() or .sort() — they’re faster, stable, and battle-tested. Do it only for learning purposes or rare edge cases. 👉 Read the complete post on my blog: https://emitechlogic.com/sorting-algorithm-in-python/ What surprised you most about Python’s sorting performance? Drop a comment — curious to hear your take. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse COMMAND_BLOCK: def insertion_sort(arr): arr = arr.copy() for i in range(1, len(arr)): key = arr[i] j = i - 1 while j >= 0 and arr[j] > key: arr[j + 1] = arr[j] j -= 1 arr[j + 1] = key return arr Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: def insertion_sort(arr): arr = arr.copy() for i in range(1, len(arr)): key = arr[i] j = i - 1 while j >= 0 and arr[j] > key: arr[j + 1] = arr[j] j -= 1 arr[j + 1] = key return arr COMMAND_BLOCK: def insertion_sort(arr): arr = arr.copy() for i in range(1, len(arr)): key = arr[i] j = i - 1 while j >= 0 and arr[j] > key: arr[j + 1] = arr[j] j -= 1 arr[j + 1] = key return arr - Insertion sort beats Merge/Quick on <100 elements (low overhead wins). - Bubble sort dies at ~1,000 elements due to expensive comparisons. - Timsort (built-in) exploits real-world patterns and runs in C — untouchable. - Comparisons are expensive: a > b → method dispatch, type checks (not one CPU instruction). - Recursion overhead: Quick sort's function calls are costly. - Memory allocations: Merge sort creates thousands of temporary lists → GC pauses. - Timsort is a hybrid genius: Detects runs, uses insertion sort for small chunks, merges adaptively — all in optimized C. - Detailed explanations - All source code - Benchmark script - Raw benchmark data - GitHub Repository: https://github.com/Emmimal/python-sorting-benchmarks