{"uid":"cap_JW2vDERIV2zfZb34Xvw9K","slug":"customer-segment-observations-adjacent-row-changes-explicit-missing-0ede57c3","name":"Customer Segment Observations — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Customer segment observations — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied customer segment observations records (account_count, revenue_amount). 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-16-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","account_count","revenue_amount"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"account_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"revenue_amount":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","account_count":10,"company_group":"group-a","revenue_amount":1},{"date":"2026-10-02","row_id":"row-2","account_count":20,"company_group":"group-b","revenue_amount":2},{"date":"2026-10-03","row_id":"row-3","account_count":15,"company_group":"group-a","revenue_amount":3},{"date":"2026-10-04","row_id":"row-4","account_count":30,"company_group":"group-b","revenue_amount":4}],"maxItems":200,"minItems":1},"field":{"enum":["account_count","revenue_amount"],"type":"string","default":"account_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_OiiEg5wsO61I-zskFlH5o","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 customer segment observation rows (account_count, revenue_amount) by computing adjacent row changes and filling missing values using zero, mean, or median methods, returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a dataset of customer segment observations with account_count and revenue_amount by company group and date — can you compute the adjacent row changes for account_count and fill any missing values using median imputation? I have about 50 rows covering groups like 'group-a' and 'group-b'.","exampleUseCases":[{"title":"Segment revenue gap imputation","prompt":"I've got monthly revenue_amount readings for several company groups but there are nulls scattered throughout — can you fill the missing revenue values using the mean and also show me how the numbers changed row by row? Here are my 80 rows of segment data."},{"title":"Account count delta analysis","prompt":"Can you run adjacent row change analysis on my account_count data across these 120 customer segment observations grouped by company? I also need any missing account_count values filled using zero so the series is complete."},{"title":"Multi-group sequential change report","prompt":"I need two reports on this customer segment dataset: one showing the row-over-row differences in revenue_amount for each company group, and one where missing revenue values are filled using median. It's 150 rows covering groups like 'enterprise' and 'smb' from 2025."}],"resultDescription":"Two analytical reports: (1) an adjacent row changes report showing the delta between consecutive rows for the selected field (account_count or revenue_amount), and (2) a missing-value filled version of the series using the chosen method (zero, mean, or median). Both reports cover all submitted rows up to 200 records.","failureModes":["More than 200 rows submitted — request exceeds maxItems limit","Invalid date format (not YYYY-MM-DD) — schema validation error","Missing required fields (row_id, company_group, date) — validation failure","Field value out of range (exceeds ±1,000,000,000,000) — rejected","Invalid method enum value — must be zero, mean, or median","Invalid field enum — must be account_count or revenue_amount","Empty rows array — minItems 1 not met"],"whenToPreferThis":"Choose this endpoint when you have tabular customer segment data with sequential rows and need (a) row-over-row delta computation and (b) missing value imputation in a single $0.01 call. It is ideal for offline/batch analytical preparation of segment metrics (account_count or revenue_amount) without requiring any live data lookup, prediction, or external service. Prefer this over general-purpose analytics tools when working within an x402 micropayment workflow and needing both transformations bundled.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T12:44:30.521Z","isFirstParty":false,"canonicalSlug":"customer-segment-observations-adjacent-row-changes-explicit-missing-0ede57c3"}