{"uid":"cap_dFpqj2jx0ALjqYpUa7cEo","slug":"time-series-completeness-exact-row-deduplication-date-by-category-pivot-7df34c27","name":"Time Series Completeness — Exact Row Deduplication + Date by Category Pivot","description":"Time series completeness — Exact row deduplication + Date by category pivot: Exact row deduplication + 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-16-052?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_fanpRoUeOKxW791JaNnSY","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":"Deduplicates exact rows and generates a date-by-category pivot table from time series completeness records containing observed and expected data points.","exampleAgentPrompt":"Can you deduplicate and pivot this time series completeness data? I have up to 50 rows with observed_points and expected_points per dataset_group and date — run both the exact row deduplication and the date-by-category pivot on the observed_points field.","exampleUseCases":[{"title":"Audit pipeline completeness by group","prompt":"I have a batch of 120 completeness records across dataset groups like 'sensor-a' and 'sensor-b' with dates and observed vs expected point counts — can you deduplicate the exact rows and give me a pivot of observed_points by date and category so I can see gaps at a glance?"},{"title":"Clean duplicate ingestion logs","prompt":"My ingestion log has 80 rows of completeness data and I think some records got duplicated during the ETL — run the exact row deduplication report and also pivot the expected_points by date and dataset_group so I can verify coverage."},{"title":"Cross-dataset completeness comparison","prompt":"I've got completeness records from two datasets merged into one table, about 150 rows with observed_points and expected_points per group and date — deduplicate the exact rows and build a date-by-category pivot on observed_points so I can compare completeness across groups over time."}],"resultDescription":"Returns two analytical reports: (1) an exact row deduplication report identifying and removing duplicate records from the supplied rows, and (2) a date-by-category pivot table showing the selected metric (observed_points or expected_points) broken down by date and dataset_group. Up to 200 rows are processed per call.","failureModes":["Input exceeds 200 rows — request rejected with validation error","Missing required fields (row_id, date, dataset_group, observed_points, expected_points) cause row-level errors","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Null values for observed_points or expected_points may affect pivot aggregation","Field enum value other than 'observed_points' or 'expected_points' causes validation error"],"whenToPreferThis":"Choose this endpoint when you need both exact row deduplication and a date-by-category pivot on time series completeness data in a single low-cost call. It is ideal for data quality workflows where you need to clean duplicates and summarize observed vs expected point counts across groups and dates, without requiring live lookups, predictions, or external data sources. Best suited for datasets of up to 200 rows processed offline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:50.616Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-exact-row-deduplication-date-by-category-pivot-7df34c27"}