{"uid":"cap_ZS7HeYvgY5JMtQKq1E_Ho","slug":"transport-ridership-data-quality-audit-string-normalisation-dataset-c1988cf9","name":"Transport Ridership Data Quality Audit, String Normalisation & Dataset Comparison","description":"Transport ridership — Record quality audit + String normalisation + Keyed dataset comparison: Record quality audit + String normalisation + Keyed dataset comparison 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-05-207?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}$","maxLength":10},"region":{"type":"string","default":"","maxLength":80},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"service_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"passenger_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","service_count":1,"passenger_count":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","service_count":2,"passenger_count":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","service_count":3,"passenger_count":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","service_count":4,"passenger_count":30}],"maxItems":200,"minItems":1},"other":{"type":"array","items":{"type":"object","required":["row_id","region","date","passenger_count","service_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","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":21},{"date":"2026-10-03","region":"group-a","row_id":"row-3","service_count":3,"passenger_count":17},{"date":"2026-10-04","region":"group-b","row_id":"row-4","service_count":4,"passenger_count":33}],"maxItems":200,"minItems":0}}},"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_rDkJelkEMQ8ANQ2GfE9hG","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 three analytical reports on transport ridership records: record quality audit, string normalisation, and keyed dataset comparison (up to 200 rows, no external lookups).","exampleAgentPrompt":"Can you audit the quality of these transit ridership records, normalise the region name strings, and compare them against our previous export? I've got two datasets of up to 200 rows each, with row_id, region, date, passenger_count, and service_count fields.","exampleUseCases":[{"title":"Pre-load ridership data validation","prompt":"Before I load this month's transit ridership CSV into our data warehouse, can you run a quality audit on all 150 rows — check for missing values, bad formats in the date field, and any weird passenger_count or service_count numbers? Here are the rows with row_id, region, date, passenger_count, and service_count."},{"title":"Comparing two ridership data exports","prompt":"I have two exports of the same ridership dataset — one from last week and one from today — both with the same row IDs. Can you compare them side by side and tell me which rows have changed in passenger_count or service_count?"},{"title":"Normalising inconsistent region names","prompt":"Our ridership data has messy region name strings — some are uppercase, some have trailing spaces, some use hyphens vs underscores. Can you normalise all the region strings across these 80 rows so they're consistent?"}],"resultDescription":"Returns three independent analytical reports: (1) a record quality audit flagging missing, out-of-range, or malformed values in passenger_count, service_count, date, and region fields; (2) a string normalisation report showing cleaned/standardised versions of string fields like region; and (3) a keyed dataset comparison report detailing row-level differences between the primary and secondary datasets matched on row_id.","failureModes":["Exceeding 200 rows returns a validation error","Rows missing required fields (row_id, region, date, passenger_count, service_count) are rejected","Malformed date strings not matching YYYY-MM-DD pattern cause schema validation failure","Numeric values outside the allowed range (-1e12 to 1e12) are rejected","Empty rows array returns an error","Payment not included or insufficient USDC causes x402 payment-required response"],"whenToPreferThis":"Choose this endpoint when you have transport ridership data (passenger_count, service_count per region and date) that needs offline quality auditing, string normalisation, and/or comparison against a second dataset in a single low-cost call. It is ideal for pre-ingestion validation pipelines, reconciling two data exports, or cleaning region name inconsistencies — all without sending data to an external live lookup or prediction service. Best suited for batch sizes up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T06:48:27.247Z","isFirstParty":false,"canonicalSlug":"transport-ridership-data-quality-audit-string-normalisation-dataset-c1988cf9"}