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Price Basket Observations — String Normalisation + Equal-Width Histogram
x402
Price Basket Observations — String Normalisation + Equal-Width Histogram is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).
Applies string normalisation and equal-width histogram binning to price basket observation records (item_price, quantity_weight), returning two independent analytical reports for up to 200 rows.
Price / call$0.01
Success—
StatusUnknown
How this service works
Price basket observations — String normalisation + Equal-width histogram: String normalisation + Equal-width histogram 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
Returns two independent analytical reports: (1) a string normalisation report that standardises text fields across the supplied rows, and (2) an equal-width histogram report that divides the chosen numeric field (item_price or quantity_weight) into the specified number of equal-width bins and provides frequency counts per bin — all computed locally without external lookups or predictions.
Example use cases
Regional price distribution audit
“I have 50 rows of price basket observations across two regions with item prices and quantity weights — can you normalise the strings and generate an equal-width histogram with 10 bins on the item_price field so I can see how prices are distributed?”
Quantity weight binning for basket analysis
“Run a string normalisation plus equal-width histogram analysis on my basket data using 8 bins on the quantity_weight field — I've got 120 rows with row IDs, dates, regions, item prices, and weights I want to summarise.”
Spot-check basket data before reporting
“Before I write up my economics report, can you normalise and bin these 30 price basket observation rows using 5 bins on item_price to make sure the distribution looks reasonable and the strings are clean?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-economics-07-031?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need both string normalisation and equal-width histogram binning applied simultaneously to price basket observation data (item_price and/or quantity_weight fields) in a single low-cost call. It is ideal for small-to-medium datasets (up to 200 rows) where you want two bundled analytical reports without any live lookups, predictions, or external side effects. Prefer it over general-purpose analytics tools when working specifically with price basket economics data and you want deterministic, local computation at minimal cost.
Side effects
- More than 200 rows submitted — request rejected with validation error
- Invalid date format (not YYYY-MM-DD) causes schema validation failure
- bins value outside 1–30 range returns validation error
- Missing required row fields (row_id, region, date, item_price, quantity_weight) causes rejection
- Null values in numeric fields may affect histogram computation
- Payment of 0.01 USDC not completed — 402 Payment Required response
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| bins | integer | |
| rows | array | |
| field | string |
Tags
x402
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