# joowani/binarytree

Python Library for Studying Binary Trees

Repository: https://github.com/joowani/binarytree
Canonical: https://ross.abutalabs.com/products/binarytree
Homepage: http://binarytree.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: python, python-3, python-2, binary-tree, interview-practice, algorithm, data-structures, heap, heaps, bst, binary-search-tree
Last push: 2023-10-04T20:59:57+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3634, "days_push": 1064, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1813, forks 174 (observed 2026-08-28T04:05:39.687461+00:00)

## What it is
A Python library for generating, visualizing, inspecting, and manipulating binary trees, including binary search trees and heaps. It is designed to help students and interview candidates practice tree algorithms without the tedium of building test data by hand.

## Use cases
- generate random binary trees for algorithm practice
- visualize binary trees in the terminal or Jupyter notebooks
- create test data for binary search tree problems
- practice coding interview tree questions
- generate heaps for studying heap algorithms
- inspect and manipulate tree node structures in Python

## When to avoid
- you need production-grade, self-balancing trees like red-black or AVL trees
- you need a high-performance tree data structure for a real application
- you need trees other than binary trees (e.g., n-ary or B-trees)

## Facets
- artifact type: library
- maturity: maintenance
- function: data-visualization, developer-tools
- domain: education, developer-tools
- platform: python, cross-platform
- tags: binary-tree, binary-search-tree, heap, data-structures, interview-practice, tree-visualization, jupyter, algorithms

## Member repositories
- joowani/binarytree (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:39.687461+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:21:10.373590+00:00, confidence not recorded.
  - readme: https://github.com/joowani/binarytree (fetched 2026-08-28T04:05:39.687461+00:00, sha 43daf4e37b88)
  - registry_pypi: https://pypi.org/pypi/binarytree/json (fetched 2026-08-29T11:00:27.297553+00:00, sha e0b0adc21094)
- Data as of 2026-08-30T08:39:29.467469+00:00.