# taizilongxu/interview_python

关于Python的面试题

Repository: https://github.com/taizilongxu/interview_python
Canonical: https://ross.abutalabs.com/products/interview_python
Language: Shell
License Family: other
Last push: 2025-03-05T09:14:51+00:00

## Health v2 (maintenance only)
Score: 36/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 9, release rhythm 35, longevity 100
- inputs: {"age_days": 4168, "days_push": 546, "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 17371, forks 5483 (observed 2026-08-28T04:11:19.585708+00:00)

## What it is
A curated collection of Python interview questions and answers covering language features, operating systems, databases, and networking. It is a Chinese-language study guide maintained as a markdown document.

## Use cases
- prepare for a Python developer interview
- review Python language internals like GIL and metaclasses
- study common OS, database, and networking interview questions
- brush up on Python 2 vs 3 differences
- find explanations of decorators, generators, and closures

## When to choose
- you are preparing for backend or Python job interviews
- you want a free, community-vetted question bank with answers
- you need a quick refresher on core CS topics alongside Python

## When to avoid
- you need an interactive course or exercises with automated grading
- you require up-to-date content on the latest Python versions
- you cannot read Chinese and machine translation is unacceptable

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, developer-tools
- domain: tutorials, programming-languages, education
- platform: python
- tags: interview-questions, python-interview, study-guide, cheatsheet

## Member repositories
- taizilongxu/interview_python (main) score 36

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:19.585708+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 2026-08-29T17:03:01.447740+00:00, confidence not recorded.
  - readme: https://github.com/taizilongxu/interview_python (fetched 2026-08-28T04:11:19.585708+00:00, sha 6a8fa5b112f6)
- Data as of 2026-08-30T08:39:29.467469+00:00.