$ show jackfrued/Python-100-Days
Python-100-Days (Python 100ๅคฉไปๆฐๆๅฐๅคงๅธ) is one of the most widely referenced Chinese-language Python learning curricula on GitHub (~186k stars). Despite its scale, it is fundamentally an educational artifact rather than a deployable software system: the dominant content is a 100-day sequence of Markdown tutorials (Day01-100) augmented with teaching-oriented code examples, SQL schema scripts, and Jupyter notebooks. The closest software classification is data_research, driven by the substantial data-analysis track โ Day66-80 covers NumPy, pandas, and Matplotlib against real bundled datasets (scraped BOSS็ด่ job postings across nine Chinese cities, 2020 sales data, 2023 Beijing hukou data), and Day81-90 covers scikit-learn machine learning with classic datasets (iris, Titanic, MPG, Boston housing). The repo's character is hybrid: it also carries a Django web-development track (Day46-60), a web-scraping track with Scrapy/Selenium (Day61-65), and database/DevOps chapters (Day36-45, Day91-100), but none of these constitute a runnable production application โ they are teaching material. Ground truth from the code: the only real end-to-end 'data pipeline' is the job-market dataset pipeline. Raw CSVs under Day66-80/code/res/jobs/ contain scraped listings from zhipin.com with a consistent schema (company_name, uri, salary, site, year, edu, job_name, city, pos_count); the notebook workflow produces a cleaned artifact (cleaned_jobs-checkpoint.csv) with salary ranges parsed into salary_lower/salary_upper bounds. This demonstrates a reusable salary-parsing and job-market normalization pattern. The machine-learning chapters are textbook-style reproductions of classical algorithms for pedagogy, not original research, and the scraping data is a timestamp-less snapshot. Maintenance is active: the author continues to revise the curriculum (a 2025 version reset of the introductory series) and the last commit is recent. Technical drift is medium โ older chapters reference Django 2.2, Vue 2.6.10, ECharts 4.2.1, and Python 3.7-era idioms, while some third-party API examples (TianAPI, chaojiying captcha OCR, SMS gateways) depend on external services that may have changed. Reproducibility is partial: dataset files needed by notebooks are checked in, but the main curriculum has no pinned dependency manifest (only the opencourse subproject has requirements.txt), no conda environment file, and the scraped data has no crawl timestamp.
The LLM identified 4 files worth highlighting for reuse:
Entry notebook of the data-analysis track (with day02-day06 siblings). Demonstrates a complete pandas/NumPy cleaning-and-analysis workflow over bundled real datasets โ multi-city scraped job postings, a 2020 sales workbook, and a 2023 Beijing hukou CSV. Because the data files are checked into the repo, the analysis is reproducible without external access. The pipeline that produced cleaned_jobs-checkpoint.csv (with raw '15-30Kยท14่ช' salary strings parsed into salary_lower/salary_upper) is a transferable pattern for any Chinese job-market or salary-range dataset.
Nine city-partitioned CSV datasets scraped from BOSS็ด่ (zhipin.com) sharing a consistent schema: company_name, uri, salary, site, year (experience required), edu (education), job_name, city, pos_count. This is a well-normalized multi-city panel that supports salary, education, and experience-demand analysis, and the schema/scraping approach is a reusable template for any job-board market study. Caveat: it is a static snapshot with no collection timestamp and some noisy fields (trailing spaces, mixed Chinese/English titles).
A suite of ~24 standalone, dependency-free teaching scripts (example01-example24.py) plus two matching test files covering advanced Python: metaclasses, decorators, generators, and classic design patterns (strategy, factory, thread-safe singleton, observer). Several utilities are genuinely extractable into new projects โ notably a CAPTCHA/verification-code generator, timing decorators, and a singleton decorator โ because they import nothing internal and are self-contained. This is the most reuse-friendly code in the repository.
Part of a set of course-specific SQL schema scripts (HR system, student registration, library, MOOC, order system, stock, app store, shared bike) demonstrating relational modeling, composite indexing, seed data, and MySQL 8 features such as window functions. Useful as a pedagogical reference or as starting templates for schema design; they are curriculum artifacts rather than drop-in reusable libraries, but they exhibit notably careful index and constraint design for teaching purposes.