Standardize Messy Dataset With Python
List the current messy column names, the target clean names, and your formatting rules for dates, phone numbers, and category mappings. - Generates a runnable pandas script standardizing dates and currency - Splits addresses into street, city, state, and zip - Splits full names into first_name and last_name, formats phone numbers - Ends with a before/after validation summary of row counts and samples
Prompt
Write a Python/pandas script that standardizes this dataset: Current column names: {messy_columns} Target column names: {clean_columns} Standardization rules: - Dates in {date_format} - Currency values converted to numeric (strip $, commas, handle parentheses as negatives) - Phone numbers formatted as {phone_format} - Addresses split into: street, city, state, zip - Full names split into first_name and last_name - All text columns: trim whitespace, fix encoding problems - Categorical columns: {category_mappings} Produce a complete, runnable script with comments explaining each step. Add a validation summary at the end printing row counts before and after, plus sample rows.
