{"uid":"cap_fJofsShJBjX3h5McCSaUd","slug":"transport-ridership-weighted-mean-missing-value-fill-b939b787","name":"Transport Ridership Weighted Mean & Missing Value Fill","description":"Transport ridership — Secondary-measure weighted mean + Explicit missing-value fill: Secondary-measure weighted mean + 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-05-187?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_p-sJn_YUgE-1g5koDksJ9","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 a secondary-measure weighted mean and fills missing values for supplied transport ridership records (passenger_count, service_count) across up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have 150 rows of daily transit ridership data with some missing passenger_count values — can you compute the service_count-weighted mean for passenger_count and fill in the missing values using median imputation? Here are the records with row_id, region, date, passenger_count, and service_count fields.","exampleUseCases":[{"title":"Weekly transit ridership gap filling","prompt":"I've got 80 rows of weekly bus ridership data where some passenger_count entries are null — can you fill those missing values using median and also compute the service-count-weighted mean for passenger_count across all regions?"},{"title":"Multi-region rail service analysis","prompt":"Here are 120 records of regional rail data with row_id, region, date, passenger_count, and service_count. Can you run a weighted mean on service_count using passenger_count as the weight, and fill any missing service_count values with zero?"},{"title":"Transit planning data quality prep","prompt":"I'm preparing a ridership dataset for a planning report — 60 rows covering several city zones. Can you compute the passenger_count weighted mean weighted by service_count and replace any missing passenger_count values with the mean, so the data is clean for analysis?"}],"resultDescription":"Returns 2 analytical reports for up to 200 supplied rows: (1) a secondary-measure weighted mean result for the specified field (passenger_count or service_count), and (2) a missing-value fill report where nulls are replaced using the chosen method (zero, mean, or median). No external data is fetched; all computation is local to the supplied rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, region, date) — validation error","Invalid date format not matching YYYY-MM-DD pattern — schema rejection","All values for target field are null — weighted mean cannot be computed","Numeric values outside allowed range (±1,000,000,000,000) — schema rejection"],"whenToPreferThis":"Choose this endpoint when you have tabular transport ridership data (passenger_count, service_count) that needs both a weighted-mean aggregation and missing-value imputation in a single call, at very low cost ($0.01 USDC for 2 reports). Best for batch offline analysis of up to 200 rows where no live data lookup or external prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:41:31.268Z","isFirstParty":false,"canonicalSlug":"transport-ridership-weighted-mean-missing-value-fill-b939b787"}