{"uid":"cap_HjCvslHgCbcz-xEVL8OcY","slug":"transformation-throughput-iqr-outlier-flags-explicit-missing-value-fill-c2dea7d9","name":"Transformation Throughput IQR Outlier Flags + Explicit Missing-Value Fill","description":"Transformation throughput — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + 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-13-180?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_uAxcJ6dCe8R1bNbQIdCW5","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":"Detects IQR-based outliers and fills missing values in transformation throughput records (processed_rows, duration_seconds) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Can you run IQR outlier detection and fill missing values using median on my transformation throughput data — here are 5 records with row_id, dataset_group, date, processed_rows, and duration_seconds — and give me both analytical reports?","exampleUseCases":[{"title":"ETL pipeline anomaly detection","prompt":"I have 50 daily ETL run records with processed_rows and duration_seconds. Can you flag any IQR outliers in processed_rows and fill in any missing values using mean, then give me both reports?"},{"title":"Data quality audit for batch jobs","prompt":"Here are 120 transformation throughput records across two dataset groups from October. Flag outliers using IQR and fill any nulls in duration_seconds with median — I need the two analytical reports for our QA review."},{"title":"Missing value imputation for pipeline metrics","prompt":"I've got some gaps in my pipeline metrics — processed_rows is null for a few rows. Can you fill those missing values using zero and also flag any IQR outliers in the same dataset? Up to 80 rows."}],"resultDescription":"Returns two analytical reports: (1) IQR-based outlier flags for each row indicating whether processed_rows or duration_seconds values fall outside the interquartile range, and (2) a missing-value fill report showing which null values were imputed and what values were substituted using the chosen fill method (zero, mean, or median). All analysis is performed locally on supplied data with no external lookups.","failureModes":["More than 200 rows submitted — exceeds maximum batch size","Missing required fields (row_id, date, dataset_group) cause row rejection","Invalid date format (not YYYY-MM-DD) fails schema validation","Invalid enum for field or method parameter causes request rejection","All rows have no nulls — missing-value fill report will show no imputations","All values within IQR — outlier flag report will show no flagged rows"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local statistical analysis on transformation throughput data (processed_rows and duration_seconds) without any external API calls or predictions. Ideal for ETL monitoring, data quality pipelines, or pre-processing audits where you want both IQR outlier detection and missing-value imputation in a single low-cost call for up to 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:52.400Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-iqr-outlier-flags-explicit-missing-value-fill-c2dea7d9"}