Choose pandas validation checks that match completeness, validity, uniqueness, and reconciliation risks.
sort-buckets stat-tile-trio common-trap-callout Sort each check by the data-quality risk it controls. Completeness Validity Uniqueness Timeliness Reconciliation Calculate missing rate for plan, region, and activation_date before grouping. Assert that plan values are only Basic, Pro, Enterprise, or Trial. Check duplicated account_id rows in a table that should be one row per account. Confirm the extract covers every day from the analysis window. Compare total revenue before and after a merge to catch row multiplication. Count accounts with no matching customer segment after the left join. Flag negative subscription prices in a field that should be nonnegative. Compare notebook output row…
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