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Join Coverage Date-by-Category Pivot with Missing-Value Fill
x402
Join Coverage Date-by-Category Pivot with 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 2026-10-15).
Pivots join coverage data (matched_rows, source_rows) by date and category, then fills missing values using zero, mean, or median imputation — producing two analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied join coverage records (matched_rows, source_rows). 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 of the specified field (matched_rows or source_rows) across dataset groups, and (2) the same dataset with explicit missing-value imputation applied using the chosen fill method (zero, mean, or median). Both reports are computed locally from the supplied rows, with no external lookups.
Example use cases
Pipeline join quality audit
“I have daily join coverage records for two dataset groups — group-a and group-b — across October 2026, with matched_rows and source_rows. Can you pivot this data by date and category and fill any missing matched_rows using median so I can see where my pipeline has coverage gaps?”
Zero-fill missing source rows for reporting
“I need to build a weekly report on source_rows coverage across my dataset groups. I have about 80 rows of coverage records — can you pivot them by date and group, and fill any missing source_rows values with zero so nothing is left blank?”
Mean imputation for join coverage trend analysis
“I've got 150 join coverage records with some null matched_rows values scattered across a few dataset groups. Can you pivot the data by date and dataset_group and use mean fill to impute those missing values so I can spot trends over time?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-189?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw join coverage records (matched_rows, source_rows) keyed by date and dataset group and need both a pivot-table view and missing-value imputation in a single call. It is ideal for data engineers auditing pipeline join quality across categories over time without needing external data sources, live lookups, or custom code execution. Prefer it over general analytics tools when your dataset fits within 200 rows and you want two pre-packaged report types (pivot + fill) for a flat $0.01 fee.
Side effects
- Fewer than 1 row supplied — validation error
- More than 200 rows supplied — rejected, exceeds limit
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Invalid field enum value (not matched_rows or source_rows) — rejected
- Invalid method enum (not zero, mean, or median) — rejected
- Numeric values outside allowed range (±1,000,000,000,000) — rejected
- Missing required fields (row_id, dataset_group, date) — schema validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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