{"uid":"cap_s9iNA2tezooPMPdLMXsZX","slug":"survey-response-weights-adjacent-row-changes-missing-value-fill-fe4fa244","name":"Survey Response Weights — Adjacent Row Changes & Missing Value Fill","description":"Survey response weights — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied survey response weights records (response_value, 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-economics-24-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","region","date","response_value","sample_weight"],"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},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"response_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","region":"group-a","row_id":"row-1","sample_weight":1,"response_value":10},{"date":"2026-10-02","region":"group-b","row_id":"row-2","sample_weight":2,"response_value":20},{"date":"2026-10-03","region":"group-a","row_id":"row-3","sample_weight":3,"response_value":15},{"date":"2026-10-04","region":"group-b","row_id":"row-4","sample_weight":4,"response_value":30}],"maxItems":200,"minItems":1},"field":{"enum":["response_value","sample_weight"],"type":"string","default":"response_value"},"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_SgH7iw4svPI31pkFdimEK","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":"Analyzes survey response weight records to detect adjacent row changes and fill explicit missing values, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 50 rows of survey data with response_value and sample_weight columns — some response_value entries are missing. Can you fill those missing values using median imputation and also show me where adjacent rows changed in the response_value field?","exampleUseCases":[{"title":"Imputing missing survey weights for analysis","prompt":"I've got 120 survey records with region, date, response_value, and sample_weight fields, but sample_weight has some nulls. Please fill those missing sample_weight values using the mean method and give me the adjacent row change report too."},{"title":"Detecting response value shifts across survey waves","prompt":"I need to spot where response_value jumps between consecutive rows in my 80-row survey dataset. Can you run the adjacent row changes report on the response_value field and also fill any missing response_value entries using zero?"},{"title":"Cleaning weighted survey panel for econometric model","prompt":"Before I run my econometric model, I need to clean this panel survey dataset — 200 rows with group regions and dates. Please fill missing response_value entries using median and flag all adjacent row changes in that field."}],"resultDescription":"Returns two analytical reports: (1) an adjacent row changes report showing where the selected field (response_value or sample_weight) changed between consecutive rows, and (2) a missing value fill report showing which null entries were filled and what values were substituted using the chosen method (zero, mean, or median). Both reports operate on the same submitted batch of up to 200 rows.","failureModes":["More than 200 rows submitted — request rejected with validation error","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Numeric values outside allowed range (±1,000,000,000,000) rejected","Missing required fields (row_id, region, date) cause row-level errors","Invalid enum value for field or method parameters causes validation error","Empty rows array (minItems:1 not met) returns error"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 survey records with response_value and sample_weight columns and need both missing value imputation and adjacent row change detection in a single low-cost call ($0.01 USDC). Ideal for survey panel data cleaning pipelines where you want two complementary analytical reports without an external data connection or prediction model.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-02T12:36:09.953Z","isFirstParty":false,"canonicalSlug":"survey-response-weights-adjacent-row-changes-missing-value-fill-fe4fa244"}