{"uid":"cap_ckt1iRbnLdycgKoy-hr-H","slug":"transport-ridership-adjacent-row-changes-date-by-category-pivot-f1b32272","name":"Transport Ridership — Adjacent Row Changes + Date by Category Pivot","description":"Transport ridership — Adjacent row changes + Date by category pivot: Adjacent row changes + Date by category pivot for supplied transport ridership records (passenger_count, service_count). 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.","url":"https://market.datapackvibe.com/x402/assistant-ib26-economics-05-160?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","passenger_count","service_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"service_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"passenger_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","service_count":1,"passenger_count":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","service_count":2,"passenger_count":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","service_count":3,"passenger_count":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","service_count":4,"passenger_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["passenger_count","service_count"],"type":"string","default":"passenger_count"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_HKMUiAnhvaLOmzRWjwc3e","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs two local analytical reports on transport ridership data: adjacent row-to-row change detection and a date-by-category pivot table, for up to 200 rows.","exampleAgentPrompt":"I have a list of up to 50 transport ridership records with region, date, passenger_count, and service_count — can you run both an adjacent row changes report and a date-by-category pivot on passenger_count for me?","exampleUseCases":[{"title":"Daily ridership trend detection","prompt":"I've got 120 rows of daily transit records across regions A, B, and C with passenger_count and service_count fields — run the adjacent row changes and date-by-category pivot reports on passenger_count so I can see where ridership jumped or dropped."},{"title":"Regional service count pivot","prompt":"Take these 80 transit service records for Q3 — each has a date, region, passenger_count, and service_count — and give me the adjacent row changes plus a date by category pivot focused on service_count."},{"title":"Monthly transit data audit","prompt":"I'm reviewing last month's ridership logs — 200 rows with route regions and daily passenger counts — can you run the row-over-row change analysis and the date-by-region pivot on passenger_count?"}],"resultDescription":"Returns two analytical reports: (1) an adjacent row changes report showing the difference in the selected metric (passenger_count or service_count) between consecutive rows, and (2) a date-by-category pivot table cross-tabulating dates against region categories with the selected metric values. Both reports are computed locally on the supplied data with no external lookups.","failureModes":["More than 200 rows submitted — exceeds maxItems limit","Missing required fields (row_id, region, date, passenger_count, or service_count) on any row","Invalid date format (must match YYYY-MM-DD pattern)","Invalid field selector — must be 'passenger_count' or 'service_count'","Empty rows array — minItems is 1","Numeric values outside allowed range (±1,000,000,000,000)"],"whenToPreferThis":"Choose this endpoint when you have transport ridership data (up to 200 rows) and need both sequential row-change analysis and a date-by-region pivot in a single low-cost call. It is ideal for transit analysts who want quick local computation without live data lookups or external integrations. Prefer it over generic analytics tools when your data already contains passenger_count and service_count fields keyed by region and date.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:31:18.466Z","isFirstParty":false,"canonicalSlug":"transport-ridership-adjacent-row-changes-date-by-category-pivot-f1b32272"}