{"uid":"cap_3Gm1nBi5qih6fWXUADqjA","slug":"transport-ridership-string-normalisation-and-numeric-interval-filter-d0eb70a9","name":"Transport Ridership String Normalisation and Numeric Interval Filter","description":"Transport ridership — String normalisation + Numeric interval filter: String normalisation + Numeric interval filter 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-05-026?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_Ina5vySND05HflLGWUVKk","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":"Normalises string fields and filters rows by numeric interval for transport ridership records (passenger_count, service_count) across up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"Can you run string normalisation on the region field and filter by passenger_count between 50 and 500 for these 120 transit ridership rows I'm uploading — I need both reports back?","exampleUseCases":[{"title":"Bus ridership range audit","prompt":"I have 180 rows of daily bus ridership data with region names and passenger counts — can you normalise the region strings and filter to only keep rows where passenger_count is between 0 and 300? I need both the normalisation report and the filtered results."},{"title":"Rail service count validation","prompt":"Can you take these 95 rail service records, normalise the region labels, and then filter on service_count so I only see rows where it falls between 10 and 200? Give me both output reports."},{"title":"Transit data quality cleanup","prompt":"I've got a messy dataset of 60 transit ridership rows with inconsistent region strings and some extreme passenger_count outliers — please normalise the strings and filter rows where passenger_count is between 5 and 1000 so I can see what's in range."}],"resultDescription":"Two analytical reports: one covering string normalisation applied to the region and date fields in the supplied rows, and one showing which rows have the specified numeric field (passenger_count or service_count) within the given minimum–maximum interval, for up to 200 input rows.","failureModes":["Input array exceeds 200 rows — request rejected","row_id missing or empty — row validation fails","date field does not match YYYY-MM-DD pattern — schema validation error","minimum greater than maximum — filter produces empty result set or error","passenger_count or service_count outside allowed numeric bounds — schema rejection","Empty rows array — minimum 1 row required"],"whenToPreferThis":"Use this endpoint when you have up to 200 transport ridership records and need both string normalisation on region/date fields and a numeric interval filter on passenger_count or service_count in a single low-cost call. It is purpose-built for transit domain data and returns two independent reports at $0.01 USDC — prefer it over general-purpose data cleaning tools when your data matches the ridership schema exactly.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-29T00:35:51.796Z","isFirstParty":false,"canonicalSlug":"transport-ridership-string-normalisation-and-numeric-interval-filter-d0eb70a9"}