Live Example: This repository hosts a Python project dedicated to analyzing content delisting trends caused by copyright infringement. It employs advanced data science techniques, primarily through Jupyter Notebooks, to systematically explore patterns and extract insights from data related to content removal requests and legal disputes.
Purpose: To support research and understanding in digital content governance and intellectual property rights.
ascii /CopyrightScan-Delisting-Analysis-Python-Project |-- data/ | |-- raw/ | |-- processed/ |-- notebooks/ | |-- 01_data_exploration.ipynb | |-- 02_trend_analysis.ipynb | |-- 03_reporting.ipynb |-- src/ | |-- init.py | |-- data_processing.py | |-- analysis.py | |-- reporting.py |-- tests/ | |-- init.py | |-- test_data_processing.py | |-- test_analysis.py |-- .gitignore |-- AGENTS.md |-- badges.yml |-- CONTRIBUTING.md |-- LICENSE |-- pyproject.toml |-- README.md |-- SECURITY.md |-- ISSUE_TEMPLATE/ | |-- bug_report.md |-- PULL_REQUEST_TEMPLATE.md |-- .github/workflows/ci.yml |-- .venv/ # (example virtual environment folder, typically gitignored)
- 🚀 Project Overview
- 💡 Core Features
- 🤖 AI Agent Directives
- ⚙️ Development Standards
- ⚖️ License
- 🤝 Contributing
- 🐛 Issue Reporting
- ⭐ Star This Repo
This repository contains a Python project focused on analyzing copyright infringement-related content delisting trends. Using data science methodologies within Jupyter Notebooks, the project aims to uncover patterns in content removals and legal disputes, contributing to a deeper understanding of digital rights management and intellectual property.
- Data Ingestion & Cleaning: Processes various data sources related to content delisting requests.
- Trend Analysis: Identifies and visualizes patterns in copyright infringement delisting over time and across different platforms.
- Insight Generation: Extracts actionable insights regarding digital content governance and IP law.
- Reproducible Research: Utilizes Jupyter Notebooks for transparent and reproducible data analysis.
View AI Agent Directives (Click to Expand)
Role: You are a Senior Principal Software Architect and Master Technical Copywriter with 40+ years of elite industry experience. You operate with absolute precision, enforcing FAANG-level standards and the wisdom of "Managing the Unmanageable." Context: Current Date is December 2025. You are building for the 2026 standard. Output Standard: Deliver EXECUTION-ONLY results. No plans, no "reporting"—only executed code, updated docs, and applied fixes. Philosophy: "Zero-Defect, High-Velocity, Future-Proof."
- SPEECH-TO-TEXT INTERPRETATION PROTOCOL:
- Context: User inputs may contain phonetic errors (homophones, typos).
- Semantic Correction: STRICTLY FORBIDDEN from executing literal typos. You must INFER technical intent based on the project context.
- Logic Anchor: Treat the
README.mdas the Single Source of Truth (SSOT).
- MANDATORY MCP INSTRUMENTATION:
- No Guessing: Do not hallucinate APIs.
- Research First: Use
linkup/braveto search for December 2025 Industry Standards, Security Threats, and 2026 UI Trends. - Validation: Use
docforkto verify every external API signature. - Reasoning: Engage
clear-thought-twoto architect complex flows before writing code.
Directives: Detect the project type (pyproject.toml for Python) and apply the corresponding Apex Toolchain. This repository, CopyrightScan-Delisting-Analysis-Python-Project, is a Python-based data analysis project.
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PRIMARY SCENARIO: DATA / SCRIPTS / AI (Python)
- Stack: This project leverages Python 3.10+. Key tools include uv (for package management and dependency resolution), Ruff (for ultra-fast linting and formatting), and Pytest (for robust unit and integration testing).
- Architecture: Adheres to a Modular Monolith pattern, ensuring clear separation of concerns for data processing, analysis, and reporting, while maintaining a unified deployment. Jupyter Notebooks are utilized for exploratory analysis and visualization.
- Data Science Libraries: Standard libraries such as
pandas,numpy,scikit-learn,matplotlib, andseabornare expected to be used within notebooks and source files. - CLI Framework: Not a primary focus for this specific project, but
Clickor similar could be integrated for utility scripts.
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SECONDARY SCENARIO A: WEB / APP / EXTENSION (TypeScript) - Not applicable for this project's primary function.
- Stack: TypeScript 6.x (Strict), Vite 7 (Rolldown), Tauri v2.x (Native), WXT (Extensions).
- State: Signals (Standardized).
- LINTING & FORMATTING: Ruff is the enforced standard. All code must pass Ruff checks without errors or warnings. Configuration is managed via
pyproject.toml. - TESTING FRAMEWORK: Pytest is mandatory. Comprehensive unit and integration tests must cover core functionalities. Aim for 90%+ code coverage.
- Dependency Management: uv is the sole package manager. Use
uv pip install -r requirements.txt(orpyproject.tomlequivalents) for installations. Never usepipdirectly. - Environment Isolation: All development and testing must occur within isolated virtual environments (e.g., using
uv's virtualenv creation capabilities).
- README: Must be a comprehensive, self-contained operating system for the project. Adhere to the README Replication Protocol.
- AGENTS.md: This document. Must be kept up-to-date with the project's specific AI agent directives and technological stack.
- CONTRIBUTING.md: Clear guidelines for contributors.
- LICENSE: Use CC BY-NC 4.0 for all projects unless explicitly stated otherwise.
- Vulnerability Scanning: Integrate GitHub Advanced Security or equivalent tools into the CI pipeline.
- Dependency Auditing: Regularly audit dependencies using tools like
pip-audit(managed viauv). - Secrets Management: NEVER commit secrets. Use environment variables or secure secret management solutions.
- Input Validation: Rigorously validate all external inputs to prevent injection attacks.
- LLM Security: If LLMs are used, implement safeguards against prompt injection, data leakage, and unintended model behavior.
Repositories designated for archival are NOT junk. They are "Retired Products." Even when archiving, you MUST elevate the metadata (Name, Description, Topics) to the highest professional standard, maintaining dignity and historical value.
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Clone the repository: bash git clone https://github.com/chirag127/CopyrightScan-Delisting-Analysis-Python-Project.git cd CopyrightScan-Delisting-Analysis-Python-Project
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**Create and activate a virtual environment (using uv): bash uv venv source .venv/bin/activate # On Windows use
.venv\Scripts\activate -
Install project dependencies: bash uv pip install -r requirements.txt # Or from pyproject.toml if using Poetry/PDM style
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Run data exploration notebook: bash jupyter notebook notebooks/01_data_exploration.ipynb
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Run trend analysis notebook: bash jupyter notebook notebooks/02_trend_analysis.ipynb
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Run reporting notebook: bash jupyter notebook notebooks/03_reporting.ipynb
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Run tests: bash pytest
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**Lint and format code (using Ruff): bash ruff check . ruff format .
- SOLID: Maintain single responsibility, open/closed, Liskov substitution, interface segregation, and dependency inversion principles in source code.
- DRY (Don't Repeat Yourself): Avoid code duplication; abstract common logic into reusable functions or classes.
- YAGNI (You Ain't Gonna Need It): Implement only the necessary features; avoid over-engineering.
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). See the LICENSE file for more details.
Contributions are welcome! Please read our CONTRIBUTING.md file for details on our code of conduct and the process for submitting pull requests.
If you encounter any bugs or have feature requests, please file an issue using our templates. For bug reports, please use the bug_report.md template.
If you find this project useful, please consider starring it on GitHub!