"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 the more common approach.  The module is called
"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:

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 *i* partitions the array *a* into two
   halves so that "all(val < x for val in a[lo : i])" for the left
   side and "all(val >= x for val in a[i : hi])" for the right side.

   *key* specifies a *key function* of one argument that is used to
   extract a comparison key from each input element.  The default
   value is "None" (compare the elements directly).

   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 *i* partitions the array *a* into two
   halves so that "all(val <= x for val in a[lo : i])" for the left
   side and "all(val > x for val in a[i : hi])" for the right side.

   *key* specifies a *key function* of one argument that is used to
   extract a comparison key from each input element.  The default
   value is "None" (compare the elements directly).

   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.

   *key* specifies a *key function* of one argument that is used to
   extract a comparison key from each input element.  The default
   value is "None" (compare the elements directly).

   This function first runs "bisect_left()" to locate an insertion
   point. Next, it runs the "insert()" method on *a* to insert *x* at
   the appropriate position to maintain sort order.

   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*.

   *key* specifies a *key function* of one argument that is used to
   extract a comparison key from each input element.  The default
   value is "None" (compare the elements directly).

   This function first runs "bisect_right()" to locate an insertion
   point. Next, it runs the "insert()" method on *a* to insert *x* at
   the appropriate position to maintain sort order.

   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']

One technique to avoid repeated calls to a key function is to search 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)
