bisect
— Array bisection algorithm¶
Source code: Lib/bisect.py
This module provides support for maintaining a list in sorted order without having to sort the list after each insertion. For long lists of items with expensive comparison operations, this can be an improvement over linear searches or frequent resorting.
The module is called bisect
because it uses a basic bisection
algorithm to do its work. Unlike other bisection tools that search for a
specific value, the functions in this module are designed to locate an
insertion point. Accordingly, the functions never call an __eq__()
method to determine whether a value has been found. Instead, the
functions only call the __lt__()
method and will return an insertion
point between values in an array.
The following functions are provided:
- bisect.bisect_left(a, x, lo=0, hi=len(a), *, key=None)¶
Locate the insertion point for x in a to maintain sorted order. The parameters lo and hi may be used to specify a subset of the list which should be considered; by default the entire list is used. If x is already present in a, the insertion point will be before (to the left of) any existing entries. The return value is suitable for use as the first parameter to
list.insert()
assuming that a is already sorted.The returned insertion point ip partitions the array a into two slices such that
all(elem < x for elem in a[lo : ip])
is true for the left slice andall(elem >= x for elem in a[ip : hi])
is true for the right slice.key specifies a key function of one argument that is used to extract a comparison key from each element in the array. To support searching complex records, the key function is not applied to the x value.
If key is
None
, the elements are compared directly and no key function is called.Changed in version 3.10: Added the key parameter.
- bisect.bisect_right(a, x, lo=0, hi=len(a), *, key=None)¶
- bisect.bisect(a, x, lo=0, hi=len(a), *, key=None)¶
Similar to
bisect_left()
, but returns an insertion point which comes after (to the right of) any existing entries of x in a.The returned insertion point ip partitions the array a into two slices such that
all(elem <= x for elem in a[lo : ip])
is true for the left slice andall(elem > x for elem in a[ip : hi])
is true for the right slice.Changed in version 3.10: Added the key parameter.
- bisect.insort_left(a, x, lo=0, hi=len(a), *, key=None)¶
Insert x in a in sorted order.
This function first runs
bisect_left()
to locate an insertion point. Next, it runs theinsert()
method on a to insert x at the appropriate position to maintain sort order.To support inserting records in a table, the key function (if any) is applied to x for the search step but not for the insertion step.
Keep in mind that the O(log n) search is dominated by the slow O(n) insertion step.
Changed in version 3.10: Added the key parameter.
- bisect.insort_right(a, x, lo=0, hi=len(a), *, key=None)¶
- bisect.insort(a, x, lo=0, hi=len(a), *, key=None)¶
Similar to
insort_left()
, but inserting x in a after any existing entries of x.This function first runs
bisect_right()
to locate an insertion point. Next, it runs theinsert()
method on a to insert x at the appropriate position to maintain sort order.To support inserting records in a table, the key function (if any) is applied to x for the search step but not for the insertion step.
Keep in mind that the O(log n) search is dominated by the slow O(n) insertion step.
Changed in version 3.10: Added the key parameter.
Performance Notes¶
When writing time sensitive code using bisect() and insort(), keep these thoughts in mind:
Bisection is effective for searching ranges of values. For locating specific values, dictionaries are more performant.
The insort() functions are O(n) because the logarithmic search step is dominated by the linear time insertion step.
The search functions are stateless and discard key function results after they are used. Consequently, if the search functions are used in a loop, the key function may be called again and again on the same array elements. If the key function isn’t fast, consider wrapping it with
functools.cache()
to avoid duplicate computations. Alternatively, consider searching an array of precomputed keys to locate the insertion point (as shown in the examples section below).
See also
Sorted Collections is a high performance module that uses bisect to managed sorted collections of data.
The SortedCollection recipe uses bisect to build a full-featured collection class with straight-forward search methods and support for a key-function. The keys are precomputed to save unnecessary calls to the key function during searches.
Searching Sorted Lists¶
The above bisect functions are useful for finding insertion points but can be tricky or awkward to use for common searching tasks. The following five functions show how to transform them into the standard lookups for sorted lists:
def index(a, x):
'Locate the leftmost value exactly equal to x'
i = bisect_left(a, x)
if i != len(a) and a[i] == x:
return i
raise ValueError
def find_lt(a, x):
'Find rightmost value less than x'
i = bisect_left(a, x)
if i:
return a[i-1]
raise ValueError
def find_le(a, x):
'Find rightmost value less than or equal to x'
i = bisect_right(a, x)
if i:
return a[i-1]
raise ValueError
def find_gt(a, x):
'Find leftmost value greater than x'
i = bisect_right(a, x)
if i != len(a):
return a[i]
raise ValueError
def find_ge(a, x):
'Find leftmost item greater than or equal to x'
i = bisect_left(a, x)
if i != len(a):
return a[i]
raise ValueError
Examples¶
The bisect()
function can be useful for numeric table lookups. This
example uses bisect()
to look up a letter grade for an exam score (say)
based on a set of ordered numeric breakpoints: 90 and up is an ‘A’, 80 to 89 is
a ‘B’, and so on:
>>> def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):
... i = bisect(breakpoints, score)
... return grades[i]
...
>>> [grade(score) for score in [33, 99, 77, 70, 89, 90, 100]]
['F', 'A', 'C', 'C', 'B', 'A', 'A']
The bisect()
and insort()
functions also work with
lists of tuples. The key argument can serve to extract the field used for ordering
records in a table:
>>> from collections import namedtuple
>>> from operator import attrgetter
>>> from bisect import bisect, insort
>>> from pprint import pprint
>>> Movie = namedtuple('Movie', ('name', 'released', 'director'))
>>> movies = [
... Movie('Jaws', 1975, 'Spielberg'),
... Movie('Titanic', 1997, 'Cameron'),
... Movie('The Birds', 1963, 'Hitchcock'),
... Movie('Aliens', 1986, 'Cameron')
... ]
>>> # Find the first movie released after 1960
>>> by_year = attrgetter('released')
>>> movies.sort(key=by_year)
>>> movies[bisect(movies, 1960, key=by_year)]
Movie(name='The Birds', released=1963, director='Hitchcock')
>>> # Insert a movie while maintaining sort order
>>> romance = Movie('Love Story', 1970, 'Hiller')
>>> insort(movies, romance, key=by_year)
>>> pprint(movies)
[Movie(name='The Birds', released=1963, director='Hitchcock'),
Movie(name='Love Story', released=1970, director='Hiller'),
Movie(name='Jaws', released=1975, director='Spielberg'),
Movie(name='Aliens', released=1986, director='Cameron'),
Movie(name='Titanic', released=1997, director='Cameron')]
If the key function is expensive, it is possible to avoid repeated function calls by searching a list of precomputed keys to find the index of a record:
>>> data = [('red', 5), ('blue', 1), ('yellow', 8), ('black', 0)]
>>> data.sort(key=lambda r: r[1]) # Or use operator.itemgetter(1).
>>> keys = [r[1] for r in data] # Precompute a list of keys.
>>> data[bisect_left(keys, 0)]
('black', 0)
>>> data[bisect_left(keys, 1)]
('blue', 1)
>>> data[bisect_left(keys, 5)]
('red', 5)
>>> data[bisect_left(keys, 8)]
('yellow', 8)