Skip to main content

bisect and heapq for ordered data

bisect and heapq solve two common performance problems without extra dependencies. bisect helps you work with sorted lists efficiently, and heapq helps you keep track of smallest or largest items.

What are bisect and heapq?​

bisect finds insertion points in sorted lists:

from bisect import bisect_left

scores = [10, 20, 30, 40]
index = bisect_left(scores, 25)
print(index)

Output:

2

heapq maintains a heap, which is useful for priority queues and top-N problems:

import heapq

values = [5, 1, 8, 3]
print(heapq.nsmallest(2, values))

Output:

[1, 3]

Why they are useful​

These modules help when:

  • a list stays sorted and needs fast lookups
  • you need to insert into an ordered list
  • you want the smallest or largest few items
  • you need a priority queue

They let you use simple built-in data structures more effectively.

When not to use them​

bisect assumes the list is already sorted. heapq is great for top-priority access, but not for keeping a fully sorted list at all times.

Rules of thumb​

  • Use bisect for searching or inserting in sorted lists.
  • Use heapq for priority queues and top-N tasks.
  • Reach for these before writing custom ordered-collection logic.