{"uid":"cap_hBvQqodsWwqSKBw9k9d1N","slug":"transport-ridership-data-quality-audit-string-normalisation-numeric-2c4b64b1","name":"Transport Ridership Data Quality Audit, String Normalisation & Numeric Interval Filter","description":"Transport ridership — Record quality audit + String normalisation + Numeric interval filter: Record quality audit + String normalisation + Numeric interval filter for supplied transport ridership records (passenger_count, service_count). Up to 200 rows; 3 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 3 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-economics-05-197?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_N7k7sizbenCWX5I4xiX6x","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":"Audits record quality, normalises region strings, and filters passenger_count or service_count by numeric interval for up to 200 transport ridership rows, returning three independent analytical reports.","exampleAgentPrompt":"Can you run a record quality audit, string normalisation, and numeric interval filter on my transport ridership data — here are 50 rows with row_id, region, date, passenger_count, and service_count — I want to filter on passenger_count between 0 and 500?","exampleUseCases":[{"title":"Transit agency data quality check","prompt":"I have 120 rows of daily ridership data from our regional bus network with passenger_count and service_count fields — can you audit the record quality, normalise the region names, and filter passenger_count values between 10 and 5000 so I can spot bad records before importing them into our dashboard?"},{"title":"Rail ridership anomaly pre-screening","prompt":"Before I load this week's train ridership CSV into our pipeline, please run a quality audit and filter service_count to only rows between 1 and 200, and normalise the region strings — I'm sending 80 rows with row_id, region, date, passenger_count, and service_count."},{"title":"Multi-region transit data cleaning","prompt":"I've got ridership records from five regions mixed together — about 150 rows — and I need the region strings normalised, a quality audit to catch nulls or formatting issues, and a numeric filter on passenger_count from 0 to 10000. Can you generate all three reports?"}],"resultDescription":"Three independent analytical reports: (1) a record quality audit flagging missing values, malformed dates, or schema violations across the submitted rows; (2) a string normalisation report showing standardised region name variants; (3) a numeric interval filter report listing which rows fall within the specified minimum–maximum range for the chosen field (passenger_count or service_count). All results are derived locally from the submitted data with no external lookups.","failureModes":["Submitting more than 200 rows returns an error — batch must be split","Missing required fields (row_id, region, date, passenger_count, service_count) causes row-level validation failure","Invalid date format (not YYYY-MM-DD) triggers schema rejection","minimum greater than maximum causes the interval filter to return no matching rows","Null numeric values for both passenger_count and service_count may degrade numeric filter results","Payment failure or insufficient USDC balance prevents processing"],"whenToPreferThis":"Choose this endpoint when you need three simultaneous local analytical operations — quality audit, string normalisation, and numeric interval filtering — on transport ridership data in a single $0.01 call. It is ideal for pre-processing transit datasets before ingestion into dashboards or pipelines, particularly when you want to catch bad records, standardise region labels, and range-filter numeric fields in one pass without making external API calls or incurring per-operation costs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:32:13.948Z","isFirstParty":false,"canonicalSlug":"transport-ridership-data-quality-audit-string-normalisation-numeric-2c4b64b1"}