{"uid":"cap_1hv2EW-UJjPleVnBR-0Xw","slug":"agricultural-output-numeric-row-ordering-explicit-missing-value-fill-4260bea0","name":"Agricultural Output — Numeric Row Ordering + Explicit Missing-Value Fill","description":"Agricultural output — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied agricultural output records (output_tonnes, cultivated_hectares). 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-16-124?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","output_tonnes","cultivated_hectares"],"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},"output_tonnes":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"cultivated_hectares":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","output_tonnes":10,"cultivated_hectares":1},{"date":"2026-10-02","region":"group-b","row_id":"row-2","output_tonnes":20,"cultivated_hectares":2},{"date":"2026-10-03","region":"group-a","row_id":"row-3","output_tonnes":15,"cultivated_hectares":3},{"date":"2026-10-04","region":"group-b","row_id":"row-4","output_tonnes":30,"cultivated_hectares":4}],"maxItems":200,"minItems":1},"field":{"enum":["output_tonnes","cultivated_hectares"],"type":"string","default":"output_tonnes"},"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_PSwhmSXDE0J-DBYhFn4HT","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":"Sorts agricultural output rows numerically and fills missing output_tonnes or cultivated_hectares values using zero, mean, or median imputation for up to 200 records.","exampleAgentPrompt":"I have a batch of up to 50 agricultural output records with some missing output_tonnes values — can you sort them by numeric row order and fill in the missing values using median imputation?","exampleUseCases":[{"title":"Crop yield data cleanup before reporting","prompt":"I've got regional crop records for the past quarter with some null output_tonnes entries — sort the rows numerically and fill those missing values using median so the dataset is ready for our monthly report."},{"title":"Filling missing hectares for land-use analysis","prompt":"My cultivated_hectares column has some gaps across about 80 farm records — can you order those rows numerically and impute the missing hectares values using the mean?"},{"title":"Zero-filling sparse harvest data","prompt":"We have sparse harvest data from 3 regions with nulls in both output_tonnes and cultivated_hectares — sort everything by row number and fill all missing output_tonnes values with zero for our agricultural dashboard."}],"resultDescription":"Returns two analytical reports covering: (1) the input rows reordered by numeric row identifier, and (2) the specified field (output_tonnes or cultivated_hectares) with all null/missing values replaced using the chosen method (zero, mean, or median). Up to 200 rows are processed per call.","failureModes":["More than 200 rows submitted — request rejected with validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Unknown imputation method specified (not zero/mean/median) — enum validation error","Empty rows array — minimum items constraint violated","row_id missing or blank — required field error","Numeric values outside allowed range (±1 trillion) — schema rejection"],"whenToPreferThis":"Choose this endpoint when you have tabular agricultural output data (crop tonnes, hectares) with missing numeric values that need imputation (zero, mean, or median) alongside numeric row ordering, and your dataset is up to 200 rows. Ideal for preprocessing pipelines before analytics or reporting. Not suitable for live data lookup, forecasting, prediction, or datasets exceeding 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T12:47:33.374Z","isFirstParty":false,"canonicalSlug":"agricultural-output-numeric-row-ordering-explicit-missing-value-fill-4260bea0"}