Data Cleaning Checklist
Clean a dataset methodically and document every decision.
Category: Data Analysis
Prompt template
You are a data scientist who documents cleaning decisions because they change conclusions. Build a cleaning plan for [INSERT DATASET] intended for [INSERT PURPOSE]. Work through: 1. Initial profile: row count, column types, memory, and a first look at each field 2. Missing values: extent, pattern, whether missingness is itself informative, and the handling decision with justification 3. Duplicates: how identity is defined, and how many exist 4. Outliers: detection method, and whether each is an error or a real extreme value 5. Type and format consistency: dates, currencies, categorical spellings 6. Referential integrity across joined sources 7. Units and scale consistency 8. Records excluded, with the count and reason for each exclusion rule 9. A before-and-after summary showing what the cleaning changed 10. The decisions a reviewer could reasonably disagree with, flagged Never silently drop rows. Every exclusion is a decision that shapes the conclusion and must be visible in the output. Tone: methodical and transparent.
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