Is there a module for balanced binary tree in Python's standard library?

Is there a module for an AVL tree or a red–开发者_如何学Cblack tree or some other type of a balanced binary tree in the standard library of Python?

No, there is not a balanced binary tree in the stdlib. However, from your comments, it sounds like you may have some other options:

• You say that you want a BST instead of a list for O(log n) searches. If searching is all you need and your data are already sorted, the bisect module provides a binary search algorithm for lists.
• Mike DeSimone recommended sets and dicts and you explained why lists are too algorithmically slow. Sets and dicts are implemented as hash tables, which have O(1) lookup. The solution to most problems in Python really is "use a dict".

If neither solution works well for you, you'll have to go to a third party module or implement your own.

there is nothing of this sort in stdlib, as far as I can see, but quick look at pypi brings up a few alternative:

• rbtree
• pyavl
• blist

There have been a few instances where I have found the heapq package (in the stadndard library) to be useful, especially if at any given time you want O(1) access time to the smallest element in your collection.

For me, I was keeping track of a collection of timers and was usually just interested in checking if the smallest time (the one to be executed first) was ready to go as of yet.

Check out also the Sorted Containers project.

Here's a PyCon talk about it: https://www.youtube.com/watch?v=7z2Ki44Vs4E

There is a new package called "bintrees" which supports ubalanced, AVL and RB trees. You can find it here.

No, but there's AVL Tree Objects for Python (very old!) and a (closed) project on SourceForge - avl-trees for Python.

I wrote a Python version of the Java TreeMap/TreeSet, of which the underlying data structure is a balanced binary tree (Red-Black tree to be precise).

Source code and documentation can be accessed in this repo

You can install with pip install pytreemap. Tested for Python >=3.5