AI summary
Three Python resources for data cleaning: the "Python Data Cleaning Cookbook" offers a recipe-style approach with practical examples; the "sfu-db/dataprep" library provides a low-code solution with a clean API for data preparation; and "VIDA-NYU/openclean" is a comprehensive framework for profiling and building data cleaning pipelines, emphasizing modular design and separation of concerns.
Type
📖 1. Python Data Cleaning Cookbook
A comprehensive, recipe-style codebase supporting the Python Data Cleaning Cookbook by Packt. It includes practical examples for handling:
- Missing values
- Outliers
- Data profiling
- Reusable functions and classes for pipelines Reddit+11GitHub+11GitHub+11
✅ What to adopt:
- Recipe-based structure — organize code in small, understandable files.
- Use of functions and classes for reusability.
⚙️ 2. sfu-db/dataprep
An open-source, low-code data preparation library:
- Offers modules like
dataprep.cleanfor cleaning tasks - Clean, consistent API design
- Focus on ease-of-use, modularity, and documentation arXiv+3Stack Overflow+3Reddit+3GitHubGitHub
✅ What to adopt:
- Clear module breakdown (e.g.,
.clean,.eda) - Well-documented functions and consistent naming conventions.
🧹 3. VIDA-NYU/openclean
A full-featured data profiling and cleaning framework:
- Toolkit for profiling and building data cleaning pipelines
- Extensible and modular design, with comprehensive APIs Reddit+3Stack Overflow+3Reddit+3Stack Overflow+5GitHub+5arXiv+5GitHub+1Reddit+1
✅ What to adopt:
- Pipeline architecture—chain modular operations.
- Separation of concerns: profiling vs. cleaning modules.