What is an “indexed” priority queue?

A priority queue allows you to serve or retrieve items in a prioritized fashion. A priority queue supports inserting elements with priorities, and removing or peeking at the top priority element. The vanilla priority queue interface can be extended to support random access, insertion, removal and changing the priority of any element in the queue. An indexed priority queue does these latter operations efficiently.

The priority queue is implemented as a binary heap of (key, priority value) pairs, which supports:

  • O(1) search for the item with highest priority

  • O(log n) removal of the item with highest priority

  • O(log n) insertion of a new item

An internal index maps elements to their location in the heap and is kept up to date as the heap is manipulated. As a result, pqdict also supports:

  • O(1) lookup of any item by key

  • O(log n) removal of any item

  • O(log n) updating of any item’s priority level

Indexed priority queues are useful in applications where priorities of already enqueued items may frequently change (e.g., schedulers, optimization algorithms, simulations, etc.).


The default heap is a min-heap, meaning smaller values give an item higher priority.

>>> from pqdict import pqdict
>>> pq = pqdict({'a':3, 'b':5, 'c':8})
>>> list(pq.popkeys())
['a', 'b', 'c']

To create a max-heap instead (larger values give higher priority), pass in the option reverse=True.

>>> from pqdict import pqdict
>>> pq = pqdict({'a':3, 'b':5, 'c':8}, reverse=True)
>>> list(pq.popkeys())
['c', 'b', 'a']

Alternatively, you can use the alias functions minpq() and maxpq().

>>> from pqdict import minpq
>>> pq = minpq(a=3, b=5, c=8)

By default, items are ordered by value. Analogous to the built-in sorted(), you can provide a priority key function to transform the values for sorting.

>>> from pqdict import pqdict
>>> pq = pqdict({'a':(2,3), 'b':(8,5), 'c':(10,8)}, key=lambda x: x[1])

Views and regular iteration don’t affect the heap, but the output is unsorted. This applies to pq.keys(), pq.values(), pq.items() and using iter() (e.g., in a for loop).

queue = pqdict({'Alice':1, 'Bob':2})
for customer in queue:
    serve(customer) # Bob may be served before Alice!
>>> list(pqdict({'a': 1, 'b': 2, 'c': 3, 'd': 4}).keys())
['a', 'c', 'b', 'd']

“Heapsort iterators” output data in descending order of priority by removing items from the collection. The following methods return heapsort iterators: pq.popkeys(), pq.popvalues(), and pq.popitems().

for customer in queue.popkeys():
    serve(customer) # Customer satisfaction guaranteed :)
# queue is now empty

pqdict supports all Python dictionary methods…

>>> from pqdict import pqdict
>>> pq = pqdict({'a':3, 'b':5, 'c':8})
>>> pq['d'] = 6.5
>>> pq['e'] = 2
>>> pq['f'] = -5
>>> 'f' in pq
>>> pq['f']
>>> pq.pop('f')
>>> 'f' in pq
>>> del pq['e']
>>> pq.get('e', None)

...and exposes a priority queue API.

>>> pq.top()
>>> pq.topitem()
('c', 1)
# manual heapsort...
>>> pq.pop()  # no args
>>> pq.popitem()
('a', 3)
>>> pq.popitem()
('b', 5)
>>> pq.popitem()
('d', 6.5)
>>> pq.popitem()  # ...and we're empty!


Value mutability. If you use mutable objects as values in a pqdict, changes to the state of those objects can break the priority queue. If this does happen, the data structure can be repaired by calling pq.heapify(). (But you probably shouldn’t be using mutable values in the first place.)


Custom precedence function. The only difference between a min-pq and max-pq is that precedence of items is determined by comparing priority keys with the builtin < and > operators, respectively. If you would like to further customize the way items are prioritized, you can pass a boolean function precedes(pkey1, pkey2) to the initializer.

The module functions nsmallest and nlargest work like the same functions in heapq but act on mappings instead of sequences, sorting by value:

>>> from pqdict import nlargest
>>> billionaires = {'Bill Gates': 72.7, 'Warren Buffett': 60.0, ...}
>>> top5_names = nlargest(5, billionaires)


This module is released under the MIT license. The augmented heap implementation was adapted from the heapq module in the Python standard library, which was written by Kevin O’Connor and augmented by Tim Peters and Raymond Hettinger.


Documentation is available at http://pqdict.readthedocs.org/en/latest/.