# leeguandong/Interview-code-practice-python

面试题

Repository: https://github.com/leeguandong/Interview-code-practice-python
Canonical: https://ross.abutalabs.com/products/interview-code-practice-python
Language: Python
License Family: other
Last push: 2019-10-28T02:03:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3168, "days_push": 2502, "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 1575, forks 542 (observed 2026-08-28T04:05:05.899517+00:00)

## What it is
A collection of Python solutions to coding interview questions, primarily covering the 'Sword to Offer' (Jianzhi Offer) problem set. It serves as personal interview preparation material shared publicly on GitHub.

## Use cases
- practice python coding interview questions
- study sword to offer solutions in python
- prepare for software engineer interviews
- learn common algorithm problems with python implementations
- review data structure interview exercises

## When to choose
- you are preparing for Chinese tech company coding interviews
- you want Python reference solutions to classic interview problems
- you are studying the Sword to Offer problem book

## When to avoid
- you need a maintained library or tool rather than study material
- you want interview prep in another programming language
- you need up-to-date interview questions or active community support

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: developer-tools
- domain: education, programming-languages
- platform: python
- tags: interview-preparation, coding-interview, algorithms, data-structures, sword-to-offer, practice-problems

## Member repositories
- leeguandong/Interview-code-practice-python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:05.899517+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 2026-08-30T03:57:36.008299+00:00, confidence not recorded.
  - readme: https://github.com/leeguandong/Interview-code-practice-python (fetched 2026-08-28T04:05:05.899517+00:00, sha bbe22baffeb5)
- Data as of 2026-08-30T08:39:29.467469+00:00.