{"uid":"cap_eRP80lwb1vWcV11M2riAA","slug":"null-coverage-grouped-numeric-summary-with-iqr-outlier-flags-d9cc6fc3","name":"Null Coverage Grouped Numeric Summary with IQR Outlier Flags","description":"Null coverage — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags 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-03-094?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"}}},"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_4MDD6u9r7IZ3Ec0vG8G4B","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":"Computes grouped numeric summary statistics and IQR-based outlier flags for null coverage data (missing_values, total_values) across up to 200 rows, producing 2 independent analytical reports.","exampleAgentPrompt":"Can you run a grouped numeric summary and IQR outlier detection on my null coverage records? I have rows with missing_values and total_values fields for groups like 'group-a' and 'group-b' across dates in October 2026 — flag any outliers and summarize the missing_values field by group.","exampleUseCases":[{"title":"Data quality audit by dataset group","prompt":"I have up to 150 rows of null coverage data with dataset groups like 'sales', 'inventory', and 'orders', each with a missing_values and total_values count per date. Can you group them and give me summary statistics plus flag any IQR outliers in the missing_values field?"},{"title":"Detecting anomalous completeness spikes","prompt":"My pipeline tracks total_values and missing_values daily across several dataset groups. Can you analyze these records and flag which rows are outliers based on IQR for total_values, so I can see where completeness drops unexpectedly?"},{"title":"Comparing null rates across two dataset groups","prompt":"I've got 80 rows of null coverage records split between 'group-a' and 'group-b' with dates from January 2026 and missing_values counts for each. Please give me a grouped numeric summary and highlight any IQR outliers in the missing_values column."}],"resultDescription":"Returns two independent analytical reports: (1) a grouped numeric summary with statistics (mean, median, min, max, std dev, etc.) segmented by dataset_group for the selected field, and (2) IQR-based outlier flags identifying which rows fall beyond 1.5× the interquartile range. Covers up to 200 input rows with no external data lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, dataset_group, date, missing_values, total_values) — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Numeric values outside allowed range (-1,000,000,000,000 to 1,000,000,000,000) — rejected","Empty rows array — no data to analyze","Invalid field enum value (not 'missing_values' or 'total_values') — error response"],"whenToPreferThis":"Choose this endpoint when you have pre-collected null coverage or data quality records (missing_values and total_values) that need grouped summary statistics and IQR-based outlier detection in a single call. It is ideal for offline or batch data quality analysis where you supply your own data rather than fetching live datasets. Prefer this over generic statistics endpoints when you specifically need group-aware summaries alongside outlier flagging for completeness metrics.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:14.279Z","isFirstParty":false,"canonicalSlug":"null-coverage-grouped-numeric-summary-with-iqr-outlier-flags-d9cc6fc3"}