# --- AUDIT RESULTS --- # 1. CRITICAL MISTAKE: Modifying a list while iterating over it causes the loop # to skip the element immediately following a removal. This is why [1, 3, 5] # might look right here, but larger datasets will fail silently. # 2. OPTIMIZATION: List comprehensions are faster and more "Pythonic." # --- REFACTORED SOLUTION --- numbers = [1, 2, 3, 4, 5, 6] # Using a list comprehension creates a new filtered list safely. numbers = [num for num in numbers if num % 2 != 0] print(numbers) # Output: [1, 3, 5] # --- EDUCATIONAL SUMMARY --- # Concept: "Iterator Invalidation." In Python, never mutate the collection # you are currently looping through. Instead, create a copy or use a filter.