Data selection

Enables efficient retrieval of top-k elements without full sort

Data analysis: finding top 10 sales figures from a large dataset, Web scraping: extracting highest-priced items from product listings, Statistics: calculating top percentiles from numerical samples

Data science, algorithm competitions, backend services processing large logs

heapq.nlargest(n, iterable)

heapq.nlargest(..., ...)

n: int ≥ 0, iterable: iterable object

• heapq.nlargest
• heapq.nsmallest
• sorted() with reverse=True

• sorted(iterable
• reverse=True)[:n]: simpler but O(n log n) vs O(n log k) for heapq
• manual iteration: O(nk) time
• inefficient for large n

Using nlargest(0, iterable): returns empty list but wastes computation; Passing non-iterable: causes TypeError; Forgetting import heapq: NameError

heapq.nsmallest: returns smallest elements instead; sorted()[:n]: sorts entire iterable; max(): only returns single maximum

Coming from SQL: may use LIMIT n after ORDER BY DESC — heapq.nlargest is more efficient for large datasets as it avoids full sort

• heapq.nsmallest
• sorted()
• min()/max()

Not suitable when n approaches iterable size (use sorted instead); Memory efficient as it maintains heap of size n; Requires elements to be comparable

Reduces time complexity from O(n log n) to O(n log k) for top-k selection

Heapq nlargest: like keeping a leaderboard of top scores while scanning through players

heapq.nlargest with custom key function for complex objects