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Join Coverage Secondary-Measure Weighted Mean with Missing-Value Fill
x402
Join Coverage Secondary-Measure Weighted Mean 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 2026-10-11).
Computes a secondary-measure weighted mean over join coverage records (matched_rows, source_rows) and fills explicit missing values, returning up to 2 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + 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
Returns up to 2 analytical reports containing the weighted mean of the selected field (matched_rows or source_rows) across supplied join coverage rows, with missing values filled using the chosen method (zero, mean, or median). Reports are computed locally with no external lookups.
Example use cases
Pipeline join completeness audit
“I have daily join coverage records for two dataset groups over the past week — some matched_rows values are null. Can you fill the missing values using the median and then compute the weighted mean of matched_rows so I can see overall join completeness? Groups are group-a and group-b, up to 20 rows.”
Source row weighted average reporting
“Compute a secondary-measure weighted mean for source_rows across my join coverage data, using zero-fill for any missing entries. I have about 50 rows spanning October 2026 across three dataset groups, and I need 2 reports out of it.”
Coverage ratio analysis with mean imputation
“For my data pipeline coverage audit, can you analyze these 100 join coverage records — fill missing matched_rows values with the mean and then calculate the weighted mean? I want to understand how well my joins are performing across different dataset groups dated between 2026-09-01 and 2026-09-30.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need to compute a weighted mean over join coverage metrics (matched_rows or source_rows) with explicit missing-value handling (zero/mean/median fill) on supplied row data up to 200 records, and you want 2 bundled analytical reports in a single low-cost call. Prefer it over generic stats tools when your data is already structured as join coverage records with dataset_group and date dimensions.
Side effects
- More than 200 rows submitted — request rejected due to maxItems constraint
- Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) cause validation errors
- Invalid date format (not YYYY-MM-DD) triggers pattern validation failure
- Numeric values outside allowed range (-1e12 to 1e12) are rejected
- Empty rows array (minItems: 1 not satisfied) returns error
- Invalid enum value for field or method parameter causes schema rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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