# JushuangQiao/Python-Offer

《剑指Offer》面试题Python实现

Repository: https://github.com/JushuangQiao/Python-Offer
Canonical: https://ross.abutalabs.com/products/python-offer
Language: Python
License Family: other
Last push: 2019-03-09T04:14:28+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": 3236, "days_push": 2734, "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 2263, forks 574 (observed 2026-08-28T04:06:32.028103+00:00)

## What it is
A collection of Python implementations of the coding interview questions from the book 'Coding Interviews' (剑指Offer). It organizes solutions by book chapter with linked explanations for each problem.

## Use cases
- prepare for python coding interviews
- practice data structure and algorithm problems in python
- find solutions to sword offer interview questions
- study linked list and binary tree problems
- learn common interview algorithm patterns

## When to choose
- you are preparing for Chinese tech company coding interviews based on the 剑指Offer book
- you want readable Python reference solutions to classic interview problems
- you want problems organized by book chapter with explanations

## When to avoid
- you need a maintained library or package - this is a study repo with no license and no releases since 2019
- you want an interactive judge or automated testing like LeetCode
- you need solutions in languages other than Python

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: developer-tools
- domain: education, programming-languages
- platform: python
- tags: coding-interview, data-structures, algorithms, interview-preparation, chinese

## Member repositories
- JushuangQiao/Python-Offer (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:32.028103+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 2026-08-30T02:42:27.759348+00:00, confidence not recorded.
  - readme: https://github.com/JushuangQiao/Python-Offer (fetched 2026-08-28T04:06:32.028103+00:00, sha b49942be5be7)
- Data as of 2026-08-30T08:39:29.467469+00:00.