If there are no artificial restrictions, bisect.insort() should be used as described by stanga. However, as Velda mentioned in a comment, most real-world problems go beyond sorting pure numbers.
Fortunately, as commented by drakenation, the solution applies to any comparable objects. For example, bisect.insort() also works with a custom dataclass that implements __lt__():
from bisect import insort
@dataclass
class Person:
first_name: str
last_name: str
age: int
def __lt__(self, other):
return self.age < other.age
persons = []
insort(persons, Person('John', 'Doe', 30))
insort(persons, Person('Jane', 'Doe', 28))
insort(persons, Person('Santa', 'Claus', 1750))
# [Person(first_name='Jane', last_name='Doe', age=28), Person(first_name='John', last_name='Doe', age=30), Person(first_name='Santa', last_name='Claus', age=1750)]
However, in the case of tuples, it would be desirable to sort by an arbitrary key. By default, tuples are sorted by their first item (first name), then by the next item (last name), and so on.
As a solution you can manage an additional list of keys:
from bisect import bisect
persons = []
ages = []
def insert_person(person):
age = person[2]
i = bisect(ages, age)
persons.insert(i, person)
ages.insert(i, age)
insert_person(('John', 'Doe', 30))
insert_person(('Jane', 'Doe', 28))
insert_person(('Santa', 'Claus', 1750))
Official solution: The documentation of bisect.insort() refers to a recipe how to use the function to implement this functionality in a custom class SortedCollection, so that it can be used as follows:
>>> s = SortedCollection(key=itemgetter(2))
>>> for record in [
... ('roger', 'young', 30),
... ('angela', 'jones', 28),
... ('bill', 'smith', 22),
... ('david', 'thomas', 32)]:
... s.insert(record)
>>> pprint(list(s)) # show records sorted by age
[('bill', 'smith', 22),
('angela', 'jones', 28),
('roger', 'young', 30),
('david', 'thomas', 32)]
Following is the relevant extract of the class required to make the example work. Basically, the SortedCollection manages an additional list of keys in parallel to the items list to find out where to insert the new tuple (and its key).
from bisect import bisect_left
class SortedCollection(object):
def __init__(self, iterable=(), key=None):
self._given_key = key
key = (lambda x: x) if key is None else key
decorated = sorted((key(item), item) for item in iterable)
self._keys = [k for k, item in decorated]
self._items = [item for k, item in decorated]
self._key = key
def __getitem__(self, i):
return self._items[i]
def __iter__(self):
return iter(self._items)
def insert(self, item):
'Insert a new item. If equal keys are found, add to the left'
k = self._key(item)
i = bisect_left(self._keys, k)
self._keys.insert(i, k)
self._items.insert(i, item)
Note that list.insert() as well as bisect.insort() have O(n) complexity. Thus, as commented by nz_21, manually iterating through the sorted list, looking for the right position, would be just as good in terms of complexity. In fact, simply sorting the array after inserting a new value will probably be fine, too, since Python's Timsort has a worst-case complexity of O(n log(n)). For completeness, however, note that a binary search tree (BST) would allow insertions in O(log(n)) time.
.insert()is what makes it confusing for the OP. Not very expectable for any amount of search to find anyone a good focus on this in the particular context.