{"uid":"cap_QBx8BY7HI-dntsTHc_qa9","slug":"join-coverage-analytics-iqr-outlier-flags-missing-value-fill-f6b2ae0f","name":"Join Coverage Analytics: IQR Outlier Flags + Missing-Value Fill","description":"Join coverage — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied join coverage records (matched_rows, source_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-09-180?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"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","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_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_1iiqW4eWtXkc_DB2JPIPc","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":"Runs IQR-based outlier detection and missing-value imputation on join coverage records (matched_rows, source_rows) for up to 200 rows, returning two independent analytical reports.","exampleAgentPrompt":"Check my join coverage records for outliers and fill any missing matched_rows values using median imputation — I have 4 rows across group-a and group-b dated 2026-10-01 through 2026-10-04 with source_rows 1–4 and matched_rows 10, 20, 15, and 30.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I have a list of up to 50 daily join coverage records with matched_rows and source_rows counts — can you flag any IQR outliers in matched_rows and fill in any missing values using median imputation?"},{"title":"Dataset group anomaly detection","prompt":"We track source_rows daily across two dataset groups and some rows have weird spikes — run IQR outlier detection on source_rows and use zero-fill for any nulls across my 30 records."},{"title":"Data reconciliation pre-check","prompt":"Before I publish my reconciliation report, check these 100 join coverage rows for statistical outliers in matched_rows using IQR, and impute any blanks with the mean so my downstream report is clean."}],"resultDescription":"Returns two independent analytical reports: (1) IQR outlier flags identifying which rows have values outside the interquartile range for the selected field, and (2) an explicit missing-value fill report showing which null entries were imputed and what values were substituted, based on the chosen method (zero, mean, or median).","failureModes":["Fewer than 1 row submitted — request rejected","More than 200 rows submitted — exceeds batch limit","Invalid date format (not YYYY-MM-DD) — schema validation error","Missing required fields (row_id, dataset_group, date) — validation failure","Field enum value not in ['matched_rows','source_rows'] — rejected","Method enum not in ['zero','mean','median'] — rejected","Payment not included or insufficient — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need both IQR outlier flagging and missing-value imputation on join coverage metrics (matched_rows, source_rows) in a single low-cost call. It is ideal for data engineers and analysts monitoring ETL pipeline join quality who want statistical anomaly detection without external lookups or ML inference. Prefer it over general-purpose analytics when your data fits the join coverage schema and you need two bundled reports for $0.01 USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T06:46:35.535Z","isFirstParty":false,"canonicalSlug":"join-coverage-analytics-iqr-outlier-flags-missing-value-fill-f6b2ae0f"}