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Data Cleaning Playbook

$19

Step-by-step data quality frameworks: deduplication, standardization, outlier detection, missing value strategies, and validation rules.

📁 7 files🏷 v1.0.0
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📁 File Structure 7 files

data-cleaning-playbook/ ├── LICENSE ├── README.md ├── config.example.yaml ├── docs/ │ ├── checklists/ │ │ └── pre-deployment.md │ ├── overview.md │ └── patterns/ │ └── pattern-01-standard.md └── templates/ └── config.yaml

📖 Documentation Preview README excerpt

Data Cleaning Playbook

Step-by-step data quality frameworks: deduplication, standardization, outlier detection, missing value strategies, and validation rules.

Contents

  • config.example.yaml
  • docs/checklists/pre-deployment.md
  • docs/overview.md
  • docs/patterns/pattern-01-standard.md
  • templates/config.yaml

Quick Start

1. Extract the ZIP archive

2. Review the README and documentation

3. Customize configuration files for your environment

4. Follow the setup guide for your specific use case

Requirements

  • Python 3.10+ (for Python scripts)
  • Relevant CLI tools for your platform
  • Access to your target environment

License

MIT License — see LICENSE file.

Support

Questions or issues? Email megafolder122122@hotmail.com

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Part of [Data Analyst](https://inity13.github.io/data-analyst-pro/)

📄 Code Sample .yaml preview

config.example.yaml # Data Cleaning Playbook — Example Configuration # Copy to config.yaml and customize for your environment project_name: "my-project" environment: "development" # Add your settings below settings: enabled: true log_level: "INFO"
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