data-cleaning
Clean and prepare datasets — missing values, types, duplicates, outliers, and consistency — with documented, reversible transformations. Use when preparing data for analysis, modeling, or reporting pipelines.
- License 许可
- MIT
- 最近更新
- 2026-07-22
- Stars
- ★ 7
- Forks
- 1
01 前言 Front Matter
- name
- data-cleaning
- description
- Clean and prepare datasets — missing values, types, duplicates, outliers, and consistency — with documented, reversible transformations. Use when preparing data for analysis, modeling, or reporting pipelines.
03 SKILL.md 正文
Data Cleaning
Overview
Data cleaning turns raw extracts into analysis-ready tables. Transparent, auditable cleaning prevents silent bias and unreproducible results.
When to Use
- Preparing analysis or model training data
- Integrating multiple sources
- Fixing recurring quality issues in pipelines
- Handing off clean tables to others
Core Practices
- Define “clean enough” for the specific question
- Handle missing data with explicit strategy (drop, impute, flag)
- Standardize types, categories, and units
- Deduplicate with clear entity keys
- Treat outliers with investigation, not automatic deletion
- Log every transformation for reproducibility
Principles
- Cleaning choices are analytical choices — document them
- Silent row drops are a common source of error
- Fix upstream when the same dirt reappears
- Never overwrite raw source data
Verification
- [ ] Transformation log exists
- [ ] Row counts and key aggregates are reconciled pre/post
- [ ] Missing-data strategy is justified
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