{"uid":"cap_KC9zexHHC7r261qregMz_","slug":"transport-ridership-adjacent-row-changes-missing-value-fill-9c2716c3","name":"Transport Ridership Adjacent Row Changes + Missing Value Fill","description":"Transport ridership — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill 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-05-162?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"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_wuavY20On5jrkXq8RQALh","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":"Computes adjacent row changes and fills missing values for transport ridership records (passenger_count, service_count) across up to 200 rows.","exampleAgentPrompt":"I have transit ridership data for two regions over the past week — can you compute the row-by-row changes in passenger_count and fill any missing values using median imputation? Here are up to 50 rows with row_id, region, date, passenger_count, and service_count.","exampleUseCases":[{"title":"Daily bus ridership change tracking","prompt":"I have daily bus passenger counts for 3 regions over the last month — can you calculate the adjacent row changes in passenger_count and fill any nulls using median, so I can see where ridership jumped or dropped day-over-day?"},{"title":"Transit service count gap filling","prompt":"My service_count column has some missing values in this 80-row dataset of weekly transit records. Can you fill those gaps using mean imputation and also show me the row-by-row changes in service_count?"},{"title":"Multi-region ridership data cleanup","prompt":"Before I run my analysis, I need to clean this 150-row transport ridership CSV — fill nulls in passenger_count with zeros and compute how each row's passenger count changed from the one before it, broken out by region."}],"resultDescription":"Two analytical reports: (1) an adjacent row changes report showing the delta between consecutive rows for the specified field, and (2) a missing value fill report with nulls replaced using the chosen method (zero, mean, or median) — applied to passenger_count or service_count across all supplied rows.","failureModes":["Exceeds 200-row limit — request rejected","Missing required fields (row_id, date, region) — validation error","Invalid date format not matching YYYY-MM-DD pattern — rejected","Invalid method enum value (must be zero, mean, or median) — error","Invalid field enum value (must be passenger_count or service_count) — error","Numeric values outside allowed range (-1T to +1T) — validation failure","Empty rows array — error"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously compute sequential row deltas AND impute missing values in transport ridership data (passenger_count or service_count) in a single API call. Ideal for transit analysts cleaning and exploring time-series ridership datasets of up to 200 rows without needing external data sources, predictions, or live lookups. The bundled two-report output makes it cost-effective at $0.01 USDC per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:31:34.822Z","isFirstParty":false,"canonicalSlug":"transport-ridership-adjacent-row-changes-missing-value-fill-9c2716c3"}