{"uid":"cap_BcGbcLpKCLFrIvxRrAtq8","slug":"transport-ridership-numeric-interval-filter-date-by-category-pivot-e29771ba","name":"Transport Ridership Numeric Interval Filter + Date by Category Pivot","description":"Transport ridership — Numeric interval filter + Date by category pivot: Numeric interval filter + 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-05-133?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_NDU1Y_L6WdaQnZNI1FCZ1","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":"Filters transport ridership rows by a numeric range on passenger_count or service_count, and generates a date-by-region pivot table — two analytical reports from user-supplied data, up to 200 rows.","exampleAgentPrompt":"I have ridership records for two regions over the past week — can you filter them to only show rows where passenger_count is between 10 and 50, and also give me a pivot of passenger_count by date and region? Here are the rows: row-1 group-a 2026-10-01 passenger_count=10 service_count=1, row-2 group-b 2026-10-02 passenger_count=20 service_count=2, row-3 group-a 2026-10-03 passenger_count=15 service_count=3.","exampleUseCases":[{"title":"Peak Hour Ridership Filtering","prompt":"I have daily bus ridership data for downtown and suburbs — filter it to rows where passenger_count is between 500 and 2000, and also show me how passenger counts break down by date and region in a pivot table. Here's my data: row-1 downtown 2026-09-01 passenger_count=1500 service_count=30, row-2 suburbs 2026-09-01 passenger_count=300 service_count=20, row-3 downtown 2026-09-02 passenger_count=1800 service_count=35."},{"title":"Low-Service Route Identification","prompt":"Can you run two reports on these transit records? First, filter to only routes where service_count is between 1 and 5 — I want to flag underserved routes. Also pivot the service_count by date and region so I can see patterns. Data: row-1 north 2026-10-01 passenger_count=50 service_count=2, row-2 south 2026-10-01 passenger_count=200 service_count=8, row-3 north 2026-10-02 passenger_count=40 service_count=3."},{"title":"Monthly Ridership Range Analysis","prompt":"I've got a month of ridership data across three regions and I need to know which records have passenger_count between 100 and 800, plus a date-by-region pivot of passenger_count so I can spot weekly patterns. Here's a sample: row-1 zone-a 2026-11-01 passenger_count=120 service_count=5, row-2 zone-b 2026-11-01 passenger_count=900 service_count=10, row-3 zone-c 2026-11-02 passenger_count=450 service_count=7."}],"resultDescription":"Returns two reports: (1) rows from the supplied dataset where the selected numeric field (passenger_count or service_count) falls within the specified minimum–maximum interval, and (2) a pivot table cross-tabulating that field's values by date and region/category. Both reports are computed locally on the supplied data with no external lookups.","failureModes":["Missing required fields (row_id, region, date, passenger_count, service_count) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Submitting more than 200 rows exceeds the maximum and will be rejected","Minimum greater than maximum produces an empty filter result","Null values in the selected numeric field are excluded from interval filtering","Payment of 0.01 USDC must be included; missing payment causes 402 error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of transport ridership data and need both a numeric range filter and a date-by-region pivot in a single call. It is ideal for local, offline-style analysis of supplied data without any live lookup, prediction, or external data enrichment. Prefer it over general-purpose analytics tools when you want a low-cost ($0.01 USDC), deterministic, two-report batch for transit datasets.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T12:43:07.656Z","isFirstParty":false,"canonicalSlug":"transport-ridership-numeric-interval-filter-date-by-category-pivot-e29771ba"}