# qiwsir/algorithm

Repository: https://github.com/qiwsir/algorithm
Canonical: https://ross.abutalabs.com/products/algorithm
Language: Python
License Family: other
Last push: 2022-06-08T09:26:15+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4482, "days_push": 1547, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3299, forks 1040 (observed 2026-08-28T04:07:55.657124+00:00)

## What it is
A collection of classic algorithm and data structure problems implemented in Python, each accompanied by explanatory markdown documents. It covers topics like sorting, binary search, binary trees, Dijkstra's shortest path, dynamic programming, and Fibonacci sequences.

## Use cases
- learn algorithms in python with worked examples
- study sorting and searching implementations
- prepare for coding interview algorithm questions
- understand binary tree traversal with code
- see dijkstra shortest path implemented in python
- compare greedy vs dynamic programming coin change solutions

## When to choose
- you are a beginner learning algorithms through readable Python code
- you want explanations paired with implementations for classic problems
- you read Chinese and want algorithm tutorials in that language

## When to avoid
- you need a production-ready algorithm library with tested, maintained APIs
- you need guaranteed support or a clear license for commercial use
- you want actively updated content - the repo has not seen recent releases

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages, tutorials
- platform: python, cross-platform
- tags: algorithms, python-examples, interview-prep, data-structures, chinese-language, tutorial-collection

## Member repositories
- qiwsir/algorithm (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:55.657124+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 2026-08-29T18:42:57.532016+00:00, confidence not recorded.
  - readme: https://github.com/qiwsir/algorithm (fetched 2026-08-28T04:07:55.657124+00:00, sha 9cf9d55efd73)
- Data as of 2026-08-30T08:39:29.467469+00:00.