{"uid":"cap_5yAeXbe2ZuHLvuSnJr9Jg","slug":"duplicate-observations-numeric-interval-filter-iqr-outlier-flags-9ce226ab","name":"Duplicate Observations — Numeric Interval Filter + IQR Outlier Flags","description":"Duplicate observations — Numeric interval filter + IQR outlier flags: Numeric interval filter + IQR outlier flags for supplied duplicate observations records (duplicate_rows, total_rows). 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-04-130?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","duplicate_rows","total_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"duplicate_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_rows":1,"dataset_group":"group-a","duplicate_rows":10},{"date":"2026-10-02","row_id":"row-2","total_rows":2,"dataset_group":"group-b","duplicate_rows":20},{"date":"2026-10-03","row_id":"row-3","total_rows":3,"dataset_group":"group-a","duplicate_rows":15},{"date":"2026-10-04","row_id":"row-4","total_rows":4,"dataset_group":"group-b","duplicate_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["duplicate_rows","total_rows"],"type":"string","default":"duplicate_rows"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_0tE6xnh4CUQFF33PrK7zP","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":"Filters duplicate observation records by a numeric interval and flags IQR-based outliers across the duplicate_rows and total_rows fields, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"Analyze my duplicate observation records for the 'sales' dataset group — filter the duplicate_rows field between 0 and 100, and flag any IQR outliers. Here are my rows: row-1 on 2024-01-01 with 10 duplicate_rows out of 500 total, row-2 on 2024-01-02 with 85 duplicate_rows out of 600 total, row-3 on 2024-01-03 with 200 duplicate_rows out of 700 total.","exampleUseCases":[{"title":"Data pipeline quality audit","prompt":"I have 50 daily records tracking duplicate_rows and total_rows across our ETL pipeline groups. Can you filter duplicate_rows between 0 and 150 and flag any IQR outliers so I can see which days had abnormal duplication levels?"},{"title":"Monthly dataset deduplication review","prompt":"Run an IQR outlier analysis on my duplicate observation data for three dataset groups — filter total_rows between 1000 and 500000 — I want to see which groups are statistically anomalous across 30 rows of records."},{"title":"Data quality regression check","prompt":"After our latest data migration I have 120 records with duplicate_rows and total_rows counts per group. Filter duplicate_rows from 0 to 50 and give me IQR outlier flags so I can spot which records are behaving unusually compared to the rest."}],"resultDescription":"Returns two independent analytical reports: (1) records from the input that fall within the specified numeric interval for the chosen field (duplicate_rows or total_rows), and (2) IQR-based outlier flags identifying which records are statistically extreme. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — rejected by minItems constraint","More than 200 rows supplied — rejected by maxItems constraint","Invalid date format (not YYYY-MM-DD) — schema validation error","Numeric values outside ±1 trillion range — rejected by schema bounds","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) — validation error","Payment not fulfilled — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need both a numeric range filter and IQR-based outlier detection on duplicate observation records in a single paid call. It is ideal for data quality workflows where you want to audit duplicate row counts across dataset groups without any external data lookup or prediction — purely statistical analysis on your supplied rows. Prefer this over generic statistics tools when you need two distinct reports (interval filter + outlier flags) computed together cost-effectively for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:33.611Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-numeric-interval-filter-iqr-outlier-flags-9ce226ab"}