{"uid":"cap_SMRYiJOa41dHCCulBD1bG","slug":"transformation-throughput-adjacent-row-changes-explicit-missing-value-b2ff419e","name":"Transformation Throughput — Adjacent Row Changes + Explicit Missing-Value Fill","description":"Transformation throughput — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). 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-analytics-13-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"},"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_mrKXeOxYPCMEyMmmqdDlZ","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 transformation throughput records by computing adjacent row changes and filling missing values using a chosen strategy (zero, mean, or median) for up to 200 rows.","exampleAgentPrompt":"I have throughput records for my ETL pipeline — rows with date, dataset_group, processed_rows, and duration_seconds — can you run adjacent row change detection and fill any missing processed_rows values using median imputation across these 12 rows?","exampleUseCases":[{"title":"ETL pipeline throughput gap detection","prompt":"Here are my daily ETL job records with processed_rows and duration_seconds for the last 30 days — can you compute adjacent row changes and fill any missing processed_rows values using median fill so I can spot anomalies in my pipeline?"},{"title":"Data warehouse load monitoring","prompt":"I've got 50 rows of transformation throughput data for two dataset groups, and some duration_seconds values are null — please fill those nulls with the mean and show me the row-over-row changes for duration_seconds."},{"title":"Missing-value imputation before reporting","prompt":"Before I generate my weekly data pipeline report, can you fill the missing processed_rows entries using zero fill and compute adjacent changes for all 80 rows in my throughput log?"}],"resultDescription":"Returns two analytical reports: (1) an adjacent row changes report showing row-over-row deltas for the selected field across consecutive records, and (2) an explicit missing-value fill report where null values in the chosen field (processed_rows or duration_seconds) are replaced using the selected strategy (zero, mean, or median). Both reports are computed locally with no external lookups.","failureModes":["Input array exceeds 200 rows — request will be rejected","Missing required fields (row_id, date, dataset_group) in any row — validation error","Invalid date format (not YYYY-MM-DD) — schema validation failure","Field enum value not in [processed_rows, duration_seconds] — rejected","Method enum value not in [zero, mean, median] — rejected","All values for selected field are null — mean/median fill may return zero or undefined"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously detect row-over-row changes and impute missing numeric values in transformation throughput data (processed_rows, duration_seconds) without any external API calls, predictions, or real-time lookups. It is purpose-built for ETL and data pipeline analytics on datasets up to 200 rows at very low cost ($0.01 USDC for both reports). Prefer alternatives if you need more than 200 rows, more than two analytical operations, or different domain data beyond throughput metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:49.845Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-adjacent-row-changes-explicit-missing-value-b2ff419e"}