{"uid":"cap_XKVMgRammBYM4Lu8n3LJI","slug":"null-coverage-analytics-numeric-distribution-date-by-category-pivot-f7eeb4c4","name":"Null Coverage Analytics: Numeric Distribution + Date-by-Category Pivot","description":"Null coverage — Numeric distribution + Date by category pivot: Numeric distribution + Date by category pivot 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-03-083?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_jLwWL4AkXJunizFBxumli","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":"Analyzes null coverage records (missing_values, total_values) to produce two reports: a numeric distribution and a date-by-category pivot, for up to 200 rows using local analytical engines.","exampleAgentPrompt":"Analyze these null coverage records for up to 4 rows — row-1 (group-a, 2026-10-01, missing=10, total=1), row-2 (group-b, 2026-10-02, missing=20, total=2), row-3 (group-a, 2026-10-03, missing=15, total=3), row-4 (group-b, 2026-10-04, missing=30, total=4) — and give me both a numeric distribution and a date-by-category pivot on the missing_values field.","exampleUseCases":[{"title":"Data quality audit by team","prompt":"I have null coverage records for two dataset groups across a week — each row has a group name, date, missing_values, and total_values. Can you give me a numeric distribution of the missing_values and a pivot breaking it down by date and group, for all 12 rows?"},{"title":"Monthly completeness report for analytics pipeline","prompt":"Run a distribution analysis and date-by-category pivot on these 50 null coverage records from our analytics pipeline — I want to see how missing_values are spread across groups like 'sales', 'inventory', and 'returns' over October."},{"title":"Comparing total values across dataset groups","prompt":"I've got 30 rows of null coverage data with dataset_group, date, missing_values, and total_values columns. Can you compute the numeric distribution and a date-by-category pivot on total_values so I can see which groups have the highest counts?"}],"resultDescription":"Returns two analytical reports computed locally on the supplied rows: (1) a numeric distribution summarizing the selected field (missing_values or total_values) — including statistical spread, bins, or frequency counts; and (2) a date-by-category pivot table breaking down the selected field by date and dataset_group. Both reports are based solely on the input data, with no external lookups or live data.","failureModes":["Missing required fields (row_id, date, dataset_group) cause validation errors","Date strings not matching YYYY-MM-DD pattern are rejected","Supplying more than 200 rows exceeds the maxItems limit","null or out-of-range numeric values for missing_values or total_values may cause computation errors","Invalid field enum value (only 'missing_values' or 'total_values' accepted)","Empty rows array (minItems: 1 not satisfied) returns an error"],"whenToPreferThis":"Choose this endpoint when you have pre-collected null coverage data (missing_values, total_values per row, grouped by dataset_group and date) and need two complementary analytical outputs — a numeric distribution and a date-by-category pivot — in a single low-cost call. It is ideal for data quality workflows, completeness audits, and pipelines where you want local computation without external lookups, predictions, or live data enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:10.849Z","isFirstParty":false,"canonicalSlug":"null-coverage-analytics-numeric-distribution-date-by-category-pivot-f7eeb4c4"}