{"id":3416,"date":"2020-06-28T13:25:33","date_gmt":"2020-06-28T07:55:33","guid":{"rendered":"http:\/\/www.pythonpool.com\/?p=3416"},"modified":"2026-07-13T12:27:01","modified_gmt":"2026-07-13T06:57:01","slug":"python-bisect","status":"publish","type":"post","link":"https:\/\/www.pythonpool.com\/python-bisect\/","title":{"rendered":"Python bisect: Search and Insert in Sorted Lists"},"content":{"rendered":"

Quick answer:<\/strong> bisect searches a sorted sequence for an insertion point without sorting it on every operation. bisect_left places a new value before equal entries, while bisect_right places it after them. The search is logarithmic, but list.insert() still shifts later elements, so the data structure choice matters for frequent updates.<\/p>\n

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bisect searches a sorted list for an insertion point; bisect_left and bisect_right make duplicate handling explicit.<\/figcaption><\/figure>\n

Python’s bisect<\/code> module finds insertion points in sorted lists. It is useful when a program needs to search an ordered sequence or add new items while keeping that order intact. bisect works on Python sequences; NumPy searchsorted Guide for Sorted Arrays<\/a> applies the same insertion-point idea to NumPy arrays and vectorized query values.<\/p>\n

The official Python documentation for bisect<\/a> is the main reference for these functions. The Python Sorting HOWTO<\/a> explains sort keys and ordering behavior, and the list documentation<\/a> covers the list operations used below.<\/p>\n

Use bisect<\/code> when the data is already sorted and the program needs positions. The module does not check that the list is sorted for you. If the input order is wrong, the returned position can also be wrong.<\/p>\n

The two search functions are bisect_left()<\/code> and bisect_right()<\/code>. The left form returns the position before equal items. The right form returns the position after equal items. The shorter name bisect()<\/code> is an alias for bisect_right()<\/code>.<\/p>\n

The insert helpers are insort_left()<\/code> and insort_right()<\/code>. They first find the correct insertion point, then insert the item into the list. This keeps the list ordered, but insertion into the middle of a list still has to move later items.<\/p>\n

That tradeoff matters. Searching with bisect is efficient, but inserting into a Python list can still cost time proportional to the list length. For a few updates or medium-sized ordered lists, the simple list approach is often excellent. For very heavy update workloads, a different data structure may fit better. bisect searches a sorted sequence, while Python Trie Data Structure: Prefix Search Guide<\/a> is the better comparison when the keys are strings and prefix lookup matters.<\/p>\n

Think of bisect<\/code> as a tool for ordered positions, not as a general search replacement. It shines when sorted order is part of the design: grade boundaries, time slots, thresholds, duplicate ranges, leaderboards, cut points, and ordered lookup tables.<\/p>\n

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