$ show Asabeneh/30-Days-Of-Python
This repository is a 30-day Python tutorial that originated as an application-focused learning project but has evolved into a hybrid data_research resource. The core is a structured curriculum covering Python fundamentals through 30 days of markdown documentation and accompanying Python scripts. However, the repository's most substantial and unique content lies in its data folder, which contains rich datasets including presidential speeches (Obama, Trump, Michelle Obama, Melina Trump), Fortune 500 company data, Hacker News posts, country statistics, email exchanges, and weight-height CSV data. The data_research character emerges from the later days which cover web scraping (with actual scraped JSON output from Boston University), Pandas data analysis, NumPy statistics, MongoDB database operations, and API consumption. The repository includes Flask web applications for text analysis, demonstrating practical data processing pipelines. While the tutorial content is well-structured and professionally presented across multiple languages (English, German, Spanish, Chinese, Portuguese, Persian, Korean, Ukrainian), the actual executable Python code is minimal compared to the markdown documentation. The data assets are the most valuable component - they're real, substantial datasets that could be used for NLP analysis, statistical exercises, and data visualization projects. However, the repository lacks proper dependency management beyond a basic requirements.txt for the Flask app, has no environment configuration, and the data sources are not systematically documented with metadata or provenance information.
The LLM identified 5 files worth highlighting for reuse:
A comprehensive English stop words list (250+ words) commonly used in NLP text analysis tasks. This is a self-contained, reusable data asset that can be extracted and used in any text processing pipeline for speech analysis, word frequency counting, or text cleaning operations. Unlike NLTK's stopwords, this is a hardcoded list with no external dependencies.
Real scraped JSON data from Boston University's facts and stats page, containing 9 categories of institutional data (Community, Campus, Academics, Research, Athletics, etc.). This demonstrates a complete web scraping pipeline output and serves as a reference for scraping tabular data from HTML pages. The nested structure with category keys and key-value pairs is a common pattern in scraped data.
A Flask web application with routes for home, about, text input, and result pages. It demonstrates form handling, template rendering with Jinja2, and basic text analysis. The app uses layout inheritance, static CSS, and proper route separation. While the actual text analysis logic is minimal (just redirects), the structure is a clean, reusable pattern for building Flask-based data processing web apps.
A structured JSON dataset containing country information likely including names, capitals, populations, languages, and geographic data. This is referenced in multiple tutorial exercises for data analysis tasks. The file enables exercises on finding most spoken languages, largest countries, and filtering operations without requiring external API calls.
A well-structured Python module demonstrating basic arithmetic operations (add, subtract, multiply, divide, remainder, power) with proper docstrings and type handling. The add_numbers function uses *args for variable arguments. This serves as a template for creating importable utility modules and demonstrates the __init__.py pattern for package creation.