{"uid":"cap_m-aizkrfzs-DqkQgNvhTH","slug":"market-survey-observations-numeric-row-ordering-explicit-missing-bab64058","name":"Market Survey Observations — Numeric Row Ordering + Explicit Missing-Value Fill","description":"Market survey observations — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied market survey observations records (respondent_count, sample_weight). 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-company-06-124?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","respondent_count","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"company_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"respondent_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_group":"group-a","sample_weight":1,"respondent_count":10},{"date":"2026-10-02","row_id":"row-2","company_group":"group-b","sample_weight":2,"respondent_count":20},{"date":"2026-10-03","row_id":"row-3","company_group":"group-a","sample_weight":3,"respondent_count":15},{"date":"2026-10-04","row_id":"row-4","company_group":"group-b","sample_weight":4,"respondent_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["respondent_count","sample_weight"],"type":"string","default":"respondent_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_SnevUuiZVwdKgYaIxbJVW","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 market survey observation rows numerically and fills missing respondent_count or sample_weight values using zero, mean, or median imputation, returning 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a batch of market survey observations with some missing respondent_count values — can you sort the rows numerically and fill in the missing values using median imputation? Here are up to 200 rows with row_id, company_group, date, respondent_count, and sample_weight fields.","exampleUseCases":[{"title":"Clean survey panel before analysis","prompt":"I've got market survey rows from multiple company groups and some respondent_count values are null — please sort all the rows numerically and fill the missing respondent_count entries using median imputation so I can run my analysis cleanly."},{"title":"Zero-fill missing weights for weighting engine","prompt":"My survey dataset has blank sample_weight values for a handful of rows — can you sort the records by numeric row order and fill those missing sample_weight fields with zero so my weighting engine doesn't choke?"},{"title":"Mean-impute before cross-group comparison","prompt":"I need to compare survey results across group-a and group-b but the data has missing respondent_count entries — sort the rows numerically and use mean fill to replace the nulls before I do my cross-group comparison."}],"resultDescription":"Two analytical reports covering: (1) the survey observation rows sorted in numeric row order, and (2) the specified numeric field (respondent_count or sample_weight) with missing values replaced by the chosen imputation method (zero, mean, or median). Up to 200 rows processed per call.","failureModes":["More than 200 rows submitted — request rejected due to maxItems constraint","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required field (row_id, company_group, date) — validation error","Unknown imputation method or field enum value — rejected by schema","Empty rows array — minItems violation","Numeric values outside allowed range (±1,000,000,000,000) — schema rejection"],"whenToPreferThis":"Choose this endpoint when you need both numeric row ordering and explicit missing-value imputation on market survey data (respondent_count or sample_weight) in a single call, producing 2 structured reports. It is ideal for preprocessing survey datasets before downstream analysis when rows may be out of order and have null values. Use it over general-purpose data tools when you want a deterministic, low-cost ($0.01 USDC) local analytical result without any live data lookups or external model inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T06:44:24.653Z","isFirstParty":false,"canonicalSlug":"market-survey-observations-numeric-row-ordering-explicit-missing-bab64058"}