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Description
From Nikhil's example:
" If validation fails, you get useful error messages explaining exactly what's wrong.
For example, a person DataFrame in the UK might require columns like person_id, age, employment_income, and hours_worked. The validation would ensure person_id is unique, age is a positive integer, employment_income is non-negative, and hours_worked falls within reasonable bounds. This catches data quality issues early, before they can affect simulation results."
Some of this functionality may be county-specific and some may be agnostic.
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