{"uid":"cap_0XzLexINXqVUjmqD0SvoZ","slug":"business-event-participation-adjacent-row-changes-explicit-missing-85422724","name":"Business Event Participation — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Business event participation — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied business event participation records (attendee_count, company_count). 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-19-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","attendee_count","company_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"company_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"company_group":{"type":"string","default":"","maxLength":80},"attendee_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_count":1,"company_group":"group-a","attendee_count":10},{"date":"2026-10-02","row_id":"row-2","company_count":2,"company_group":"group-b","attendee_count":20},{"date":"2026-10-03","row_id":"row-3","company_count":3,"company_group":"group-a","attendee_count":15},{"date":"2026-10-04","row_id":"row-4","company_count":4,"company_group":"group-b","attendee_count":30}],"maxItems":200,"minItems":1},"field":{"enum":["attendee_count","company_count"],"type":"string","default":"attendee_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_GTltASnmnJDw17QcAKkaK","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":"Computes adjacent-row delta changes and fills missing values in business event participation data (attendee_count, company_count) for up to 200 rows, returning 2 analytical reports.","exampleAgentPrompt":"I have a dataset of business event participation records with some missing attendee counts — can you compute the adjacent row changes and fill the nulls using median imputation? Here are up to 200 rows with row_id, company_group, date, attendee_count, and company_count fields.","exampleUseCases":[{"title":"Clean event attendance time series","prompt":"I have 150 rows of daily business event attendance data with some null attendee_count values — please fill those missing values using the median method and also show me the row-over-row changes in attendee_count."},{"title":"Analyze company participation deltas","prompt":"Can you take my business event participation records (up to 80 rows) and compute the adjacent row changes for company_count, filling any missing company_count values with zero so I can see the sequential trends?"},{"title":"Impute missing values before reporting","prompt":"I need to prepare my event participation dataset for a board report — fill the missing attendee_count fields using mean imputation and give me the adjacent row difference calculations across all 200 rows grouped by company_group."}],"resultDescription":"Returns 2 analytical reports: (1) adjacent row change calculations showing delta between consecutive rows for the specified field, and (2) explicit missing-value fill results replacing nulls using the chosen method (zero, mean, or median) — all processed locally with no external lookups.","failureModes":["Input array exceeds 200 rows — rejected with validation error","Invalid date format (not YYYY-MM-DD) causes schema rejection","Missing required fields (row_id, company_group, date) causes validation failure","Invalid method enum value (not zero/mean/median) causes rejection","Empty rows array (minItems:1 not met) causes validation error"],"whenToPreferThis":"Choose this endpoint when you need both row-over-row delta computation AND missing value imputation on business event participation data in a single call. Ideal when your dataset has up to 200 rows of attendance/company records with nulls that need cleaning before sequential analysis. Prefer over general analytics tools when you want a low-cost ($0.01 USDC), self-contained local computation with no external data lookups or predictions.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:48:47.788Z","isFirstParty":false,"canonicalSlug":"business-event-participation-adjacent-row-changes-explicit-missing-85422724"}