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pyredblack

Red/black trees in C++ for Python

0.1.1
pipPyPI
Maintainers
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============ pyredblack

.. image:: https://travis-ci.org/wroberts/pyredblack.svg?branch=master :target: https://travis-ci.org/wroberts/pyredblack

.. image:: https://coveralls.io/repos/wroberts/pyredblack/badge.svg?branch=master :target: https://coveralls.io/r/wroberts/pyredblack?branch=master :alt: Test code coverage

.. image:: https://img.shields.io/pypi/v/pyredblack.svg :target: https://pypi.python.org/pypi/pyredblack/ :alt: Latest Version

Copyright (c) 2015 Will Roberts wildwilhelm@gmail.com

Licensed under the MIT License (see LICENSE.rst for details).

Cython interface to red-black trees implemented in C++.

Red-black trees_ are a kind of self-balancing binary tree. They maintain their entries in sorted order and have O(log n) for insertion, lookup, and deletion. You can read more about red-black trees and see animations of insertion, lookup, and deletion_.

.. _Red-black trees: http://en.wikipedia.org/wiki/Red%E2%80%93black_tree .. _self-balancing binary tree: http://en.wikipedia.org/wiki/Self-balancing_binary_search_tree .. _read more about red-black trees: http://www.eternallyconfuzzled.com/tuts/datastructures/jsw_tut_rbtree.aspx .. _see animations of insertion, lookup, and deletion: https://www.cs.usfca.edu/~galles/visualization/RedBlack.html

This package provides dictionary and set objects based on red-black-trees; these can be used as drop-in replacements for the built-in dict and set types, except that they maintain their contents in sorted order.

Dictionary (rbdict)::

>>> import pyredblack
>>> d = pyredblack.rbdict(Germany = 'Berlin',
                          Hungary = 'Budapest',
                          Ireland = 'Dublin',
                          Portugal = 'Lisbon',
                          Cyprus = 'Nicosia',
                          Greenland = 'Nuuk',
                          Iceland = 'Reykjavik',
                          Macedonia = 'Skopje',
                          Bulgaria = 'Sofia',
                          Sweden = 'Stockholm')
>>> len(d)
10
>>> d['Ireland']
'Dublin'
>>> d.keys()
['Bulgaria', 'Cyprus', 'Germany', 'Greenland', 'Hungary',
 'Iceland', 'Ireland', 'Macedonia', 'Portugal', 'Sweden']
>>> d.values()
['Sofia', 'Nicosia', 'Berlin', 'Nuuk', 'Budapest',
 'Reykjavik', 'Dublin', 'Skopje', 'Lisbon', 'Stockholm']
>>> d.popitem()
('Bulgaria', 'Sofia')
>>> d.popitem()
('Cyprus', 'Nicosia')
>>> d.popitem()
('Germany', 'Berlin')

Set (rbset)::

>>> fruit = pyredblack.rbset(['apple', 'orange', 'apple', 'pear',
                              'orange', 'banana'])
>>> 'orange' in fruit
True
>>> 'crabgrass' in fruit
False
>>> a = pyredblack.rbset('abracadabra')
>>> b = pyredblack.rbset('alacazam')
>>> list(a)
['a', 'b', 'c', 'd', 'r']
>>> list(a - b)
['b', 'd', 'r']
>>> list(a | b)
['a', 'b', 'c', 'd', 'l', 'm', 'r', 'z']
>>> list(a & b)
['a', 'c']
>>> list(a ^ b)
['b', 'd', 'l', 'm', 'r', 'z']
>>> a.pop()
'a'
>>> a.pop()
'b'
>>> a.pop()
'c'

Requirements

  • Python 2.7, Python 3.2+
  • Cython (and a C++ compiler)

Todo

  • implement slicing on dictionaries

Keywords

data structure algorithm

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U.S. Patent No. 12,346,443 & 12,314,394. Other pending.