{"uid":"cap_b3N5EVjgOKCM52BgGomzc","slug":"time-series-completeness-string-normalisation-date-by-category-pivot-a4afe64c","name":"Time Series Completeness — String Normalisation + Date by Category Pivot","description":"Time series completeness — String normalisation + Date by category pivot: String normalisation + 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-16-035?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_UO8aFUiWXRZ0EcUsfMZje","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":"Runs two local analytical reports on time series completeness data: string normalisation and a date-by-category pivot, for up to 200 rows of observed/expected point records.","exampleAgentPrompt":"I have up to 200 rows of time series completeness data with columns row_id, dataset_group, date, observed_points, and expected_points — can you run the string normalisation and date-by-category pivot reports on the observed_points field for me?","exampleUseCases":[{"title":"Completeness audit across sensor groups","prompt":"I've got daily completeness records for three sensor dataset groups from October 2026, each with observed_points and expected_points. Can you normalise the group name strings and pivot the observed_points values by date and category so I can see where gaps are?"},{"title":"Pipeline monitoring data cleanup","prompt":"Our data pipeline produces completeness logs with dataset_group labels that have inconsistent casing and spacing. Can you run string normalisation on these rows and also give me a date-by-category pivot of the expected_points so I can spot missing data by day?"},{"title":"Weekly reporting on data ingestion completeness","prompt":"Here are 150 rows of our weekly ingestion completeness records with dates, dataset groups, and observed vs expected point counts — please run the two standard reports: string normalisation and a date-by-category pivot on observed_points."}],"resultDescription":"Returns two analytical reports computed locally on the supplied rows: (1) a string normalisation report cleaning the dataset_group and related string fields, and (2) a date-by-category pivot table showing the selected numeric field (observed_points or expected_points) broken down by date and dataset_group category. No external lookups or predictions are included.","failureModes":["Fewer than 1 or more than 200 rows supplied — validation error","Missing required fields (row_id, dataset_group, date, observed_points, expected_points) on any row — schema rejection","Invalid date format (must be YYYY-MM-DD) — pattern mismatch error","Numeric values outside allowed range (-1e12 to 1e12) — validation failure","Invalid field enum value (must be 'observed_points' or 'expected_points') — rejection"],"whenToPreferThis":"Choose this endpoint when you have structured time series completeness records (with observed and expected point counts per group per date) and need both string normalisation and a date-by-category pivot in a single low-cost call. Ideal for data engineering pipelines, completeness audits, and reporting workflows where you want two bundled local analytical transformations without any live data lookup or external enrichment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:41:51.937Z","isFirstParty":false,"canonicalSlug":"time-series-completeness-string-normalisation-date-by-category-pivot-a4afe64c"}