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Data Reconciliation — Date×Category Pivot + Missing-Value Fill
x402
Data Reconciliation — Date×Category Pivot + Missing-Value Fill is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Pivots reconciliation rows by date and dataset group, then fills missing source_total or target_total values using zero, mean, or median imputation — returning two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Data reconciliation — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.
Output
Two analytical reports: (1) a date-by-category pivot table of the selected field (source_total or target_total) across all dataset groups and dates, and (2) a missing-value fill report showing which cells were imputed and what values were used, based on the chosen method (zero, mean, or median). Up to 200 input rows supported.
Example use cases
Monthly finance reconciliation pivot
“I've got 150 reconciliation records with source_total and target_total across three dataset groups and daily dates for October — pivot them by date and group, and fill any missing source_total values using the median so I can spot discrepancies.”
Null-safe target total comparison
“My target_total column has some nulls in this reconciliation dataset of 80 rows across two groups. Fill the missing target_total values with the group mean and give me the date-by-category pivot so I can review the comparison cleanly.”
Data quality check before reporting
“Before I send this reconciliation file to finance, can you pivot the 60 rows by date and dataset group and replace any missing source_total entries with zero so the pivot is complete with no gaps?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-11-189?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to reshape reconciliation data into a date-by-category pivot AND handle missing values in a single call. It is ideal for pre-reporting data quality workflows where source/target totals must be compared across groups and dates without nulls. Prefer it over generic pivot tools when you specifically need the combined pivot + imputation output as two structured reports at low cost ($0.01 USDC for both). Not suitable for live data lookups, predictions, or datasets exceeding 200 rows.
Side effects
- Input array exceeds 200 rows — request rejected
- Missing required fields (row_id, date, dataset_group, source_total, target_total) on any row — validation error
- Date strings not matching YYYY-MM-DD format — pattern validation failure
- Numeric values outside ±1,000,000,000,000 range — schema rejection
- Empty rows array (minItems: 1 not met) — validation error
- Invalid enum value for field or method — schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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