Top-K Select - Data Handling in Pure Python

Find the three largest values in a list by repeated max-pick: locate the current maximum, record it, remove it from the working copy, and repeat. The replay shows work shrinking and result filling one element at a time.

By hand

The Pythonic way

heapq.nlargest(3, values) returns the three largest values in descending order without modifying the input. Internally it maintains a min-heap of size k, scanning the list once in O(n log k) — more efficient than repeated max for large k.

naive.py

values = [4, 7, 2, 9, 5, 8, 1, 6]
work = list(values)
result = []
for _ in range(3):
    best = max(work)
    result.append(best)
    work.remove(best)
print('RESULT:', result)

library.py

import heapq
values = [4, 7, 2, 9, 5, 8, 1, 6]
result = heapq.nlargest(3, values)
print('RESULT:', result)

RESULT: [9, 8, 7]

Implementation notes

work.remove(best) removes the first occurrence of the maximum; since all values here are distinct, this is unambiguous.
The loop variable _ signals "the count matters, the value does not"; it still appears in the trace as 0, 1, 2.
For k ≪ n, prefer heapq.nlargest; for k close to n, sorted(values, reverse=True)[:k] is simpler; for k = 1, just max(values).