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Sampling Observations Date-by-Category Pivot with Missing-Value Fill
x402
Sampling Observations 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 supplied sampling observation rows (sample_value or sample_weight) by date and category, then fills missing 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
Sampling observations — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied sampling observations records (sample_value, sample_weight). 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 covering: (1) a date-by-category pivot table of the selected field (sample_value or sample_weight) across all supplied rows, and (2) that same pivot with missing cells explicitly filled using the chosen method (zero, mean, or median). No external data is fetched; all computation is local to the engine using only the supplied rows (max 200).
Example use cases
Survey response data pivot
“I collected weekly survey responses across five demographic groups — each row has a date, group name, sample_value (score), and sample_weight. Can you pivot this by date and group, and fill any missing scores using the median?”
Environmental sampling cross-tab
“I have environmental sensor readings tagged by date and monitoring zone, up to 180 rows. Please reshape them into a date-by-zone pivot table and use zero-fill for any missing sample_value readings.”
Clinical trial sample weight analysis
“Here are 120 clinical trial records with participant group, collection date, and sample_weight measurements. Can you pivot these by date and group and fill in any missing sample_weight values using the mean?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-189?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have flat sampling observation records (up to 200 rows) that need to be reshaped into a date-by-category matrix and you want explicit missing-value imputation (zero, mean, or median) in a single low-cost call. It is well-suited for survey data, environmental monitoring, or experimental sampling datasets where date and group dimensions need cross-tabulation without any external data lookups or predictions.
Side effects
- Input array has fewer than 1 or more than 200 rows — request rejected
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) on any row — validation error
- Date field does not match YYYY-MM-DD pattern — schema validation failure
- Numeric values outside allowed range (±1,000,000,000,000) — rejection
- Invalid enum value for field or method parameters — schema error
- Payment of 0.01 USDC not confirmed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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