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Sampling Observations — String Normalisation + Date by Category Pivot
x402
Sampling Observations — String Normalisation + Date by Category Pivot is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-19).
Runs two local analytical reports on up to 200 sampling observation rows: string normalisation and a date-by-category pivot for sample_value or sample_weight fields.
Price / call$0.01
Success—
StatusUnknown
How this service works
Sampling observations — String normalisation + Date by category pivot: String normalisation + Date by category pivot 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 generated locally from the supplied rows: (1) a string normalisation report cleaning and standardising text fields such as dataset_group, and (2) a date-by-category pivot table showing the selected field (sample_value or sample_weight) broken down by date and dataset_group — all computed without external lookups or live data.
Example use cases
Survey sample pivot by date and group
“I've got 80 survey sample records with group labels and dates — can you pivot the sample_value field by category and date, and also normalise the string fields in the dataset?”
Weighted sample distribution by category
“Here are 120 sampling observations across three dataset groups over Q3 — run the string normalisation and date-by-category pivot on sample_weight so I can see how weights break down across groups over time.”
Data prep for sampling QA pipeline
“Before I hand off these 200 sampling observation rows to QA, can you do a string normalisation pass and generate the date-by-category pivot on sample_value to flag any grouping anomalies?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ib26-analytics-15-035?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need two bundled local analytical operations — string normalisation and a date-by-category pivot — on sampling observation data (with sample_value and sample_weight fields) in a single low-cost call. It is ideal for lightweight, offline-style data prep or QA tasks on datasets up to 200 rows where no external lookup, prediction, or live enrichment is needed.
Side effects
- Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) causes validation failure
- More than 200 rows submitted exceeds the maxItems limit
- Date strings not matching YYYY-MM-DD pattern are rejected
- Invalid field selector value (not 'sample_value' or 'sample_weight') causes schema error
- Numeric values outside ±1,000,000,000,000 are rejected
- Empty rows array triggers minItems validation error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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