Sorting Algorithms

Sorting is one of the most studied problems in computer science. In interviews, you'll rarely implement sort from scratch - but understanding how they work helps you choose the right approach and recognize when sorting simplifies a problem.

Quick Reference

# Python's built-in (Timsort) - use this!
nums.sort()           # In-place
sorted(nums)          # Returns new list

# Custom sort key
nums.sort(key=lambda x: x[1])  # Sort by second element
nums.sort(key=abs)             # Sort by absolute value
nums.sort(reverse=True)        # Descending

Merge Sort (O(n log n), stable)

Divide, sort halves, merge. Great for linked lists and external sorting.

def mergeSort(arr):
    if len(arr) <= 1:
        return arr

    mid = len(arr) // 2
    left = mergeSort(arr[:mid])
    right = mergeSort(arr[mid:])

    return merge(left, right)

def merge(left, right):
    result = []
    i = j = 0
    while i < len(left) and j < len(right):
        if left[i] <= right[j]:
            result.append(left[i])
            i += 1
        else:
            result.append(right[j])
            j += 1
    result.extend(left[i:])
    result.extend(right[j:])
    return result

Quick Select (O(n) average)

Find kth smallest without fully sorting:

def quickSelect(nums, k):
    pivot = nums[len(nums) // 2]
    left = [x for x in nums if x < pivot]
    mid = [x for x in nums if x == pivot]
    right = [x for x in nums if x > pivot]

    if k <= len(left):
        return quickSelect(left, k)
    elif k <= len(left) + len(mid):
        return pivot
    else:
        return quickSelect(right, k - len(left) - len(mid))