{"uid":"cap_ft917OAHQiljriaDwgTdc","slug":"road-segment-observations-numeric-row-ordering-missing-value-fill-06ad2563","name":"Road Segment Observations — Numeric Row Ordering + Missing-Value Fill","description":"Road segment observations — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied road segment observations records (length_m, travel_seconds). 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-weather-15-124?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","station_group","date","length_m","travel_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"length_m":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"station_group":{"type":"string","default":"","maxLength":80},"travel_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","length_m":10,"station_group":"group-a","travel_seconds":1},{"date":"2026-10-02","row_id":"row-2","length_m":20,"station_group":"group-b","travel_seconds":2},{"date":"2026-10-03","row_id":"row-3","length_m":15,"station_group":"group-a","travel_seconds":3},{"date":"2026-10-04","row_id":"row-4","length_m":30,"station_group":"group-b","travel_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["length_m","travel_seconds"],"type":"string","default":"length_m"},"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_FiPYptmxcM37OHnngUa75","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":"Applies numeric row ordering and explicit missing-value fill (zero, mean, or median) to road segment observation records containing length_m and travel_seconds fields, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a batch of road segment observations with some missing length_m values — can you order the rows numerically and fill those nulls using median imputation? Here are up to 50 records with row_id, station_group, date, length_m, and travel_seconds fields.","exampleUseCases":[{"title":"Impute missing travel times in sensor data","prompt":"I've got road sensor records from last month where some travel_seconds entries are null — can you fill those gaps using the mean of the other values and return the ordered dataset? I have about 120 rows with row_id, station_group, date, length_m, and travel_seconds."},{"title":"Prepare road length data for reporting","prompt":"Can you take my road segment observations and fill in any missing length_m values with zero, then return them in numeric row order? I have around 80 records covering multiple station groups and dates."},{"title":"Audit and clean highway segment records","prompt":"I need to clean up my highway segment dataset — there are nulls in both length_m and travel_seconds. Please order the rows and fill the missing travel_seconds using median imputation across my 150 road observation records."}],"resultDescription":"Returns two analytical reports based on the supplied road segment data: one applying numeric row ordering and one applying explicit missing-value fill for the specified field (length_m or travel_seconds) using the chosen method (zero, mean, or median). No external data is fetched; all computation is performed locally on the submitted rows.","failureModes":["Rows array is empty or missing — validation error","row_id missing or blank — schema rejection","Date field does not match YYYY-MM-DD pattern — validation error","More than 200 rows submitted — exceeds maxItems limit","field or method enum value not recognized — schema rejection","All values for chosen field are null — mean/median may be undefined or zero"],"whenToPreferThis":"Use this endpoint when you have up to 200 road segment observation records containing length_m or travel_seconds fields with missing values that need structured imputation (zero, mean, or median fill) combined with numeric row ordering, and you want two distinct analytical reports without any live data lookup or external API calls.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-30T19:10:36.743Z","isFirstParty":false,"canonicalSlug":"road-segment-observations-numeric-row-ordering-missing-value-fill-06ad2563"}