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Price Basket Observations — Numeric Interval Filter + Explicit Missing-Value Fill
x402
Price Basket Observations — Numeric Interval Filter + 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 2026-10-24).
Filters price basket observation rows by a numeric interval on a chosen field and fills missing values using a specified method (zero, mean, or median), returning up to 2 analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Price basket observations — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + Explicit missing-value fill for supplied price basket observations records (item_price, quantity_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 local analytical reports covering the interval-filtered and missing-value-filled price basket observation rows. Reports summarize the cleaned dataset (up to 200 rows) based on item_price or quantity_weight, reflecting the chosen fill method (zero, mean, or median) and numeric min/max bounds. No external lookups or predictions are included.
Example use cases
Clean retail price basket for analysis
“I've got about 80 rows of retail basket observations with item_price and quantity_weight. Some item_price entries are missing — fill them with the median and only keep rows where item_price is between 5 and 250. Give me both analytical reports.”
Filter grocery quantity data by weight range
“Here are 120 grocery basket records. I want to filter so only rows with quantity_weight between 0.5 and 50 are kept, and fill any null quantity_weight values with the mean. Run the two reports on the cleaned data.”
Zero-fill missing prices in economics dataset
“I have an economics dataset of 60 price basket observations — some item_price values are null. Please fill the missing item_price values with zero and filter to only include rows where item_price is between 0 and 1000, then produce the two summary reports.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-07-135?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have tabular price basket observation data (item_price, quantity_weight) that needs both numeric range filtering and missing-value imputation in a single call, and you want two analytical reports on the cleaned result. It is ideal for small-to-medium datasets (up to 200 rows) where you want a fast, self-contained local computation without external lookups or live market data. Prefer it over general-purpose analytics tools when your specific workflow involves economics or retail basket data cleaning at low cost ($0.01 per call).
Side effects
- More than 200 rows submitted — request rejected or truncated
- Invalid date format in row (must match YYYY-MM-DD)
- Minimum value exceeds maximum value in interval filter — may return empty result set
- Null row_id or row_id exceeding 80 characters — validation error
- Field value outside allowed numeric range (±1,000,000,000,000) — schema rejection
- Payment of 0.01 USDC not completed — 402 response returned
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string | |
| method | string | |
| maximum | number | |
| minimum | number |
Tags
x402
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