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Museum Footfall – Date by Category Pivot
x402
Museum Footfall – 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 2026-10-17).
Pivots museum visitor footfall data by date and exhibit category, returning a cross-tabulated summary of visitors or dwell time across up to 200 records.
Price / call$0.01
Success—
StatusUnknown
How this service works
Museum footfall - Date by category pivot: Date by category pivot for Museum footfall. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded date by category pivot report.
Output
Returns a bounded pivot table cross-tabulating the selected numeric field (visitors or dwell_minutes) by date (rows) and exhibit category (columns), derived entirely from the supplied records with no external enrichment or forecasting.
Example use cases
Daily exhibit attendance comparison
“I have 150 daily footfall records for our science museum with exhibit names, dates, and visitor counts. Can you pivot them so I can see total visitors per exhibit for each day?”
Dwell time breakdown by gallery
“We track how long visitors spend in each gallery — can you pivot our last month of dwell-minute records by date and gallery category so we can spot which exhibits hold attention longest on which days?”
Weekend vs weekday exhibit traffic
“I've got footfall logs for our art museum covering October — around 180 rows with exhibit and date fields. Please pivot the visitor counts by date and exhibit so I can see the traffic patterns across categories.”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-ar-analytics-museum-footfall-pivot?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you have raw museum footfall records (up to 200 rows) and need a fast, local pivot table by date and exhibit category without any enrichment, forecasting, or external data. Ideal for descriptive analytics on attendance or dwell data collected in-house.
Side effects
- Exceeds 200-row limit — request will be rejected or truncated
- Invalid date format (not YYYY-MM-DD) causes validation failure
- Missing required fields (row_id, exhibit, date, visitors, dwell_minutes) cause schema errors
- Null numeric values may produce incomplete aggregations
- Invalid field enum value (not 'visitors' or 'dwell_minutes') causes rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| rows | array | |
| field | string |
Tags
x402
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