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Join Coverage — Base-100 Series + Explicit Missing-Value Fill
x402
Join Coverage — Base-100 Series + Explicit 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).
Computes a base-100 indexed time series and fills missing values (zero, mean, or median) for join coverage metrics (matched_rows, source_rows) across up to 200 supplied rows, returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Join coverage — Base-100 series + Explicit missing-value fill: Base-100 series + 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 2 analytical reports: (1) a base-100 indexed time series for the selected field (matched_rows or source_rows), normalized relative to the specified base_index row; (2) a missing-value filled version of the same series using the chosen method (zero, mean, or median). All computation is performed locally on the supplied data with no external lookups.
Example use cases
Track join quality trends over time
“I have daily join coverage data for two dataset groups across October 2026. Can you normalize the matched_rows values to a base-100 series starting from the first row, and fill any gaps using median so I can compare trends between groups?”
Impute missing source rows before reporting
“My pipeline has some null source_rows values across about 150 rows of join coverage records. Can you fill those nulls using mean imputation and return a base-100 indexed series so I can see how source coverage evolved relative to day zero?”
Audit join coverage with zero-fill baseline
“I want to analyze join coverage quality across my dataset groups. Please take my 80 rows of matched_rows data dated from 2025, apply zero-fill for any missing values, and produce a base-100 series indexed from row 5 so I can benchmark against that point.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-09-169?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both a base-100 normalized time series AND missing-value imputation for join coverage metrics (matched_rows or source_rows) in a single call. It is ideal for data engineering workflows where you want to track join quality trends relative to a baseline row while also cleaning nulls before downstream analysis. Prefer it over separate normalization or imputation tools when your data fits within 200 rows and you want two complementary reports (indexed series + filled series) for $0.01 USDC per call.
Side effects
- Input array exceeds 200 rows — rejected with validation error
- base_index out of range for the supplied rows array — validation failure
- Invalid date format (not YYYY-MM-DD) in any row — schema rejection
- All values for selected field are null — imputation may be undefined or return zeros
- Missing required fields (row_id, dataset_group, date) on any row — request rejected
- Field enum value not in ['matched_rows','source_rows'] — rejected
- Method enum value not in ['zero','mean','median'] — rejected
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| base_index | integer |
Tags
x402
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