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:mod:`bisect` --- Array bisection algorithm
===========================================
.. module:: bisect
:synopsis: Array bisection algorithms for binary searching.
.. sectionauthor:: Fred L. Drake, Jr. <fdrake@acm.org>
.. example based on the PyModules FAQ entry by Aaron Watters <arw@pythonpros.com>
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 the more common
approach. The module is called :mod:`bisect` because it uses a basic bisection
algorithm to do its work. The source code may be most useful as a working
example of the algorithm (the boundary conditions are already right!).
The following functions are provided:
.. function:: bisect_left(a, x, lo=0, hi=len(a))
Locate the proper 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()``. This assumes that *a* is already sorted.
.. function:: bisect_right(a, x, lo=0, hi=len(a))
bisect(a, x, lo=0, hi=len(a))
Similar to :func:`bisect_left`, but returns an insertion point which comes
after (to the right of) any existing entries of *x* in *a*.
.. function:: insort_left(a, x, lo=0, hi=len(a))
Insert *x* in *a* in sorted order. This is equivalent to
``a.insert(bisect.bisect_left(a, x, lo, hi), x)``. This assumes that *a* is
already sorted.
Also note that while the fast search step is O(log n), the slower insertion
step is O(n), so the overall operation is slow.
.. function:: insort_right(a, x, lo=0, hi=len(a))
insort(a, x, lo=0, hi=len(a))
Similar to :func:`insort_left`, but inserting *x* in *a* after any existing
entries of *x*.
Also note that while the fast search step is O(log n), the slower insertion
step is O(n), so the overall operation is slow.
Searching Sorted Lists
----------------------
The above :func:`bisect` functions are useful for finding insertion points, but
can be tricky or awkward to use for common searching tasks. The following three
functions show how to transform them into the standard lookups for sorted
lists::
def find(a, key):
'''Find leftmost item exact equal to the key.
Raise ValueError if no such item exists.
'''
i = bisect_left(a, key)
if i < len(a) and a[i] == key:
return a[i]
raise ValueError('No item found with key equal to: %r' % (key,))
def find_le(a, key):
'''Find largest item less-than or equal to key.
Raise ValueError if no such item exists.
If multiple keys are equal, return the leftmost.
'''
i = bisect_left(a, key)
if i < len(a) and a[i] == key:
return a[i]
if i == 0:
raise ValueError('No item found with key at or below: %r' % (key,))
return a[i-1]
def find_ge(a, key):
'''Find smallest item greater-than or equal to key.
Raise ValueError if no such item exists.
If multiple keys are equal, return the leftmost.
'''
i = bisect_left(a, key)
if i == len(a):
raise ValueError('No item found with key at or above: %r' % (key,))
return a[i]
Other Examples
--------------
.. _bisect-example:
The :func:`bisect` function is generally useful for categorizing numeric data.
This example uses :func:`bisect` to look up a letter grade for an exam total
(say) based on a set of ordered numeric breakpoints: 85 and up is an 'A', 75..84
is a 'B', etc.
>>> grades = "FEDCBA"
>>> breakpoints = [30, 44, 66, 75, 85]
>>> from bisect import bisect
>>> def grade(total):
... return grades[bisect(breakpoints, total)]
...
>>> grade(66)
'C'
>>> map(grade, [33, 99, 77, 44, 12, 88])
['E', 'A', 'B', 'D', 'F', 'A']
Unlike the :func:`sorted` function, it does not make sense for the :func:`bisect`
functions to have *key* or *reversed* arguments because that would lead to an
inefficent design (successive calls to bisect functions would not "remember"
all of the previous key lookups).
Instead, it is better to search a list of precomputed keys to find the index
of the record in question::
>>> data = [('red', 5), ('blue', 1), ('yellow', 8), ('black', 0)]
>>> data.sort(key=lambda r: r[1])
>>> keys = [r[1] for r in data] # precomputed 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)
.. seealso::
`SortedCollection recipe
<http://code.activestate.com/recipes/577197-sortedcollection/>`_ that
encapsulates precomputed keys, allowing straight-forward insertion and
searching using a *key* function.
|
