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mediumDSA

Compare common sorting algorithms.

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01

Understand the problem

Explain bubble, merge, quick sort.

sorting
02

Attempt it yourself

Sketch your approach before reading the solution — that's what interviews test.

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03

Study the solution

Bubble/insertion are O(n²), simple, fine for tiny inputs. Merge sort is stable O(n log n) with O(n) extra space. Quicksort averages O(n log n) in place but O(n²) worst case with bad pivots. Most language libraries use hybrids (e.g. Timsort, introsort).

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04

Read the code

Quicksort + mergesort
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def quicksort(a):
    if len(a) <= 1: return a
    pivot = a[len(a) // 2]
    less = [x for x in a if x < pivot]
    eq   = [x for x in a if x == pivot]
    more = [x for x in a if x > pivot]
    return quicksort(less) + eq + quicksort(more)

def mergesort(a):
    if len(a) <= 1: return a
    mid = len(a) // 2
    l, r = mergesort(a[:mid]), mergesort(a[mid:])
    out, i, j = [], 0, 0
    while i < len(l) and j < len(r):
        if l[i] <= r[j]: out.append(l[i]); i += 1
        else: out.append(r[j]); j += 1
    return out + l[i:] + r[j:]


# --- demo ---
print(quicksort([5, 2, 8, 1, 9]))   # [1, 2, 5, 8, 9]
print(mergesort([5, 2, 8, 1, 9]))   # [1, 2, 5, 8, 9]
05

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