Generate Python/R scripts for cleaning messy CSV data with standardization, deduplication, and validation.
Act as a data engineer cleaning messy datasets. Given a CSV file with [COLUMNS] columns and approximately [ROWS] rows containing [DATA_ISSUES]. Generate a complete Python pandas script that: loads the CSV with proper encoding handling, identifies and reports data quality issues (missing values, duplicates, outliers, inconsistent formats), cleans each column based on its data type, standardizes date formats to [DATE_FORMAT], handles missing values with appropriate strategy (imputation/removal/flagging), removes exact and fuzzy duplicates, validates data against business rules [VALIDATION_RULES], exports a clean CSV and a data quality report. Include logging, error handling, and comments explaining each transformation step.
Free to copy and use. Compatible with Claude 4 Opus, GPT-5, Gemini 2.5 Pro, Llama 4.
Describe [COLUMNS], approximate [ROWS], [DATA_ISSUES], [DATE_FORMAT], and [VALIDATION_RULES].
Initial release
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