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

Source: Dev.to

Benchmark Results

Key Takeaways

• Insertion sort beats Merge/Quick on very small inputs; Python’s function calls are costly: they involve method dispatch and type checks, not a single CPU instruction.
• Recursion overhead makes Quick sort’s function calls expensive.
• Memory allocations: Merge sort creates thousands of temporary lists, leading to GC pauses.
• Timsort is a hybrid genius: it detects runs, uses insertion sort for small chunks, and merges adaptively, all in optimized C.

Insertion Sort Example

def insertion_sort(arr):
    """Return a sorted copy of *arr* using insertion sort."""
    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

Never implement your own sort in production Python.
Use sorted() or list.sort() — they’re faster, stable, and battle‑tested. Write your own only for learning purposes or rare edge cases.

Further Resources

• Detailed explanations – (link to the full article)
• All source code – (link to the repository)
• Benchmark script – (link to the script)
• Raw benchmark data – (link to the data)

External Links

• Full blog post:
• GitHub repository:

What surprised you most about Python’s sorting performance?
Feel free to share your thoughts in the comments.