{"uid":"cap_92U9VsS9TfqDCWaSthik_","slug":"null-coverage-iqr-outlier-flags-and-missing-value-fill-8bb2ef76","name":"Null Coverage IQR Outlier Flags and Missing Value Fill","description":"Null coverage — IQR outlier flags + Explicit missing-value fill: IQR outlier flags + Explicit missing-value fill for supplied null coverage records (missing_values, total_values). 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-03-180?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"},"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_kzTUB30kyLKVIDCg4Fw4c","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 null coverage records (missing_values, total_values) for up to 200 rows, returning two analytical reports per call.","exampleAgentPrompt":"I have up to 50 rows of null coverage data with row_id, dataset_group, date, missing_values, and total_values fields — can you run IQR outlier flagging and fill any missing values using median imputation on the missing_values field?","exampleUseCases":[{"title":"Outlier detection in pipeline data quality logs","prompt":"I have 80 rows of daily data quality records from our ETL pipeline with row_id, group name, date, missing_values, and total_values columns — flag any IQR outliers and fill missing entries using median on the missing_values field."},{"title":"Impute missing total_values before dashboard reporting","prompt":"Before I publish our monthly data completeness dashboard, can you fill in any null total_values fields across these 120 records using mean imputation and also flag any outlier rows based on IQR?"},{"title":"Audit dataset group null rates for data governance","prompt":"I'm auditing null coverage across five dataset groups — here are 150 rows with date, group, missing_values, and total_values. Run IQR outlier flags and fill missing_values gaps with zero so I can see which groups look anomalous."}],"resultDescription":"Two analytical reports per call: (1) an IQR-based outlier flag report identifying which rows have anomalous missing_values or total_values, and (2) a missing-value fill report with imputed values using the chosen method (zero, mean, or median) for the specified field, covering up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Missing required fields (row_id, date, missing_values, total_values) in any row — validation error","Date field not in YYYY-MM-DD format — schema validation failure","Invalid method enum value (not zero/mean/median) — rejected","Invalid field enum value (not missing_values/total_values) — rejected","Numeric values outside allowed range (-1T to 1T) — validation error","Empty rows array — minimum items constraint violation"],"whenToPreferThis":"Use this endpoint when you need both IQR-based outlier detection and missing value imputation on null coverage metrics (missing_values, total_values) in a single low-cost call. It is ideal for data quality monitoring pipelines, pre-reporting audits, and dataset governance workflows where you have up to 200 rows of structured null coverage records and want two independent analytical reports without external lookups or model inference.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T00:58:07.815Z","isFirstParty":false,"canonicalSlug":"null-coverage-iqr-outlier-flags-and-missing-value-fill-8bb2ef76"}