{"uid":"cap_9x4eNNCt9yr6C90O5fsQk","slug":"dataset-storage-analytics-iqr-outlier-flags-missing-value-fill-05ad8b6c","name":"Dataset Storage Analytics: IQR Outlier Flags + Missing Value Fill","description":"Dataset storage — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied dataset storage records (used_mb, row_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-analytics-18-180?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","used_mb","row_count"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"used_mb":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"row_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","used_mb":10,"row_count":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","used_mb":20,"row_count":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","used_mb":15,"row_count":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","used_mb":30,"row_count":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["used_mb","row_count"],"type":"string","default":"used_mb"},"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_dSzC5oBAd5kaC-ujpR_Vv","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 dataset storage records (used_mb, row_count) for up to 200 rows, returning two analytical reports.","exampleAgentPrompt":"Analyze these 12 storage records for IQR outliers and fill any missing used_mb values using median — the dataset has groups like 'warehouse-a' and 'warehouse-b' with dates in October 2026.","exampleUseCases":[{"title":"Storage spike anomaly detection","prompt":"I have 50 rows of daily storage usage data across three dataset groups from last month — can you flag any used_mb values that look like IQR outliers so I can investigate unexpected spikes?"},{"title":"Missing row count imputation","prompt":"My storage dataset has some null row_count values mixed in — run the IQR outlier detection and fill those missing row_count entries using the mean, and give me back both reports."},{"title":"Pre-pipeline data quality check","prompt":"Before I load this batch of 150 dataset storage records into our warehouse, flag any outliers in the row_count field using IQR and fill any missing values with zero — I need both the outlier flags and the fill report."}],"resultDescription":"Returns two analytical reports: (1) IQR-based outlier flags per row for the selected field (used_mb or row_count), identifying which rows fall outside the interquartile range bounds; (2) a missing-value fill report showing which null values were replaced and what fill value (zero, mean, or median) was used. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Input rows exceed 200 item limit — request rejected","Missing required fields (row_id, dataset_group, date, used_mb, row_count) — validation error","Invalid date format (must match YYYY-MM-DD) — schema validation failure","Invalid field enum value (must be 'used_mb' or 'row_count') — rejected","Invalid method enum value (must be 'zero', 'mean', or 'median') — rejected","Empty rows array — minItems violation","Numeric values outside allowed range (-1e12 to 1e12) — schema error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, low-cost statistical data quality analysis on dataset storage metrics (used_mb, row_count) without any external API dependencies. It is ideal when you want both IQR outlier detection and missing value imputation in a single $0.01 call for up to 200 rows. Prefer it over heavier ML-based anomaly detection services when your data is tabular, small-to-medium scale, and interpretability of IQR bounds matters. Not suitable for real-time streaming, prediction, or datasets larger than 200 rows per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:46:47.967Z","isFirstParty":false,"canonicalSlug":"dataset-storage-analytics-iqr-outlier-flags-missing-value-fill-05ad8b6c"}