| name | sorting-choice |
| description | Choose the right sorting approach using Python's built-in Timsort, heapq, or custom strategies. Use when sorting arrays, finding top-k elements, or ordering by multiple keys. |
| topic | Sorting |
| token_cost | 90 |
| related | ["binary-search","two-pointers","hash-vs-tree"] |
| keywords | ["sort","order","rank","largest","smallest","kth","median","arrange","compare","stable","priority","heap","nlargest","nsmallest","key","reverse","sorted"] |
When to use
Python's built-in sorted()/list.sort() is Timsort — O(n log n), stable, and almost always the right choice.
Rules
- Use key= for custom ordering
- For top-k elements, use heapq.nlargest/nsmallest (O(n log k)) instead of full sort
- For finding just the kth element, consider quickselect or statistics.median
- Counting sort / radix sort help only when values are bounded integers
- When the problem says "sort by X then by Y," use a tuple key: key=lambda x: (x.a, x.b)
- For reverse on one field only, negate it or use functools.cmp_to_key
- ALWAYS prefer built-in sort — it's optimized and stable
- NEVER implement your own sort algorithm unless the problem requires it
Complexity
Timsort: O(n log n). heapq.nlargest/nsmallest: O(n log k).
Example
"Top 3 scores" → heapq.nlargest(3, scores) in O(n log 3). "Sort by name then age" → sorted(items, key=lambda x: (x.name, x.age)).
