{"uid":"cap_yuvmXAWSX9OCw_Ak3QO3d","slug":"time-series-completeness-numeric-distribution-date-by-category-pivot-91ecd829","name":"Time Series Completeness — Numeric Distribution + Date by Category Pivot","description":"Time series completeness — Numeric distribution + Date by category pivot: Numeric distribution + Date by category pivot for supplied time series completeness records (observed_points, expected_points). 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-16-083?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","observed_points","expected_points"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"expected_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"observed_points":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_points":1,"observed_points":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_points":2,"observed_points":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_points":3,"observed_points":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_points":4,"observed_points":30}],"maxItems":200,"minItems":1},"field":{"enum":["observed_points","expected_points"],"type":"string","default":"observed_points"}}},"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_KiDzUX5_zYZ5HVusKq6tg","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":"Generates two local analytical reports (numeric distribution and date-by-category pivot) for time series completeness data (observed_points, expected_points) across up to 200 rows.","exampleAgentPrompt":"I have 50 rows of time series completeness records with columns row_id, dataset_group, date, observed_points, and expected_points — can you run a numeric distribution and a date-by-category pivot on the observed_points field and give me both reports?","exampleUseCases":[{"title":"Pipeline completeness audit by group","prompt":"Here are 120 rows of daily completeness records for our data pipelines, each with a dataset_group, date, observed_points, and expected_points. Can you generate a numeric distribution and a date-by-category pivot on observed_points so I can see where coverage is dropping?"},{"title":"Expected vs observed gap analysis","prompt":"I have time series completeness data for 4 groups over the last quarter — about 90 rows with date, dataset_group, observed_points, and expected_points. Run a numeric distribution and date-by-category pivot on the expected_points field so I can spot which groups consistently fall short."},{"title":"Multi-group data coverage summary","prompt":"Can you take these 200 completeness records covering dataset groups A, B, and C across 2025, and give me both a numeric distribution and a date-by-category pivot for observed_points? I want to see the spread and how coverage varies by group over time."}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution of the selected field (observed_points or expected_points) across all supplied rows, and (2) a date-by-category pivot table breaking down the selected field by date and dataset_group. Both reports are computed locally with no external lookups, covering up to 200 input rows.","failureModes":["Rows array exceeds 200 items — request rejected","Missing required fields (row_id, date, observed_points, expected_points) on any row — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","Null values for observed_points or expected_points may affect distribution accuracy","Invalid field enum value (not 'observed_points' or 'expected_points') — rejected"],"whenToPreferThis":"Choose this endpoint when you have pre-collected time series completeness records (observed vs expected points) and need two complementary analytical views — a numeric distribution and a date-by-category pivot — in a single low-cost call. Ideal for data engineers auditing pipeline completeness across multiple groups over time without needing live data fetching, prediction, or external enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:10.016Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-numeric-distribution-date-by-category-pivot-91ecd829"}