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Feed Observations Secondary-Measure Weighted Mean and Explicit Missing-Value Fill
x402
Feed Observations Secondary-Measure Weighted Mean and 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 2026-11-01).
Computes a secondary-measure weighted mean and fills missing values in feed observation records (item_count, duplicate_items) for up to 200 rows, returning 2 analytical reports.
Price / call$0.01
Success—
StatusUnknown
How this service works
Feed observations — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + Explicit missing-value fill for supplied feed observations records (item_count, duplicate_items). 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 local analytical reports: (1) a secondary-measure weighted mean computed over the specified field (item_count or duplicate_items) across submitted rows, and (2) an explicit missing-value fill report replacing nulls using the chosen method (zero, mean, or median). No external lookups or predictions are made; all computation is performed on the supplied data.
Example use cases
Missing value imputation for feed data
“I have 50 rows of daily feed observations for site groups across October, some item_count values are null — can you fill those missing values using median imputation and also compute the weighted mean of item_count weighted by duplicate_items?”
Duplicate item ratio analysis by site group
“Here are 120 feed observation records across multiple site groups — can you compute the secondary-measure weighted mean for duplicate_items, using item_count as the weight, and fill any missing duplicate_items values with zero?”
Feed quality audit with aggregated statistics
“I need to audit my feed pipeline data: I have up to 200 rows with item_count and duplicate_items by date and site group — run the weighted mean and missing-value fill analysis using mean imputation so I can spot anomalies.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-web-11-187?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both weighted mean computation and missing-value imputation on feed observation data (item_count, duplicate_items) in a single $0.01 call for up to 200 rows. It is ideal when your data already exists locally and you need two analytical reports simultaneously without external data lookups, predictions, or live integrations. Prefer alternatives if you need more than 200 rows, real-time predictions, or reports beyond weighted mean and missing-value fill.
Side effects
- Rows array empty or exceeds 200 items — validation error returned
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- Null row_id or row_id exceeding 80 characters — rejected row
- Field enum value not 'item_count' or 'duplicate_items' — invalid input error
- Method enum value not 'zero', 'mean', or 'median' — invalid input error
- Numeric values outside ±1,000,000,000,000 range — boundary violation error
- Payment not provided or insufficient USDC — x402 payment required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string |
Tags
x402
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