{"uid":"cap_WBGrZKZctiR6AABE_tGk9","slug":"transformation-throughput-analytics-date-by-category-pivot-with-8a91f880","name":"Transformation Throughput Analytics — Date-by-Category Pivot with Missing-Value Fill","description":"Transformation throughput — Date by category pivot + Explicit missing-value fill: Date by category pivot + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). 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-13-189?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"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},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"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_q8Wa1uW7wXDMmYcqsrAUl","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":"Pivots transformation throughput records (processed_rows, duration_seconds) by date and category, then fills missing values using zero, mean, or median strategies — returning two analytical reports for up to 200 rows.","exampleAgentPrompt":"Pivot my transformation throughput data by date and dataset group — I have about 80 rows with processed_rows and duration_seconds fields — and fill in any missing processed_rows values using the median method.","exampleUseCases":[{"title":"ETL pipeline throughput review","prompt":"I have 150 rows of daily ETL run logs with dataset_group, date, processed_rows, and duration_seconds. Can you pivot them by date and group, and fill any missing processed_rows values with the median so I can see where each group stands over time?"},{"title":"Missing data imputation for duration metrics","prompt":"My transformation logs have some null duration_seconds entries across different dataset groups. Pivot the records by date and category, and fill those gaps using the mean — I have around 60 rows total."},{"title":"Zero-fill throughput matrix for reporting","prompt":"I need a clean date-by-category matrix for my processed_rows data — about 200 rows across several dataset groups. Pivot everything by date and group, and zero-fill any missing cells so the table is complete for my report."}],"resultDescription":"Two analytical reports based on the supplied transformation throughput records: (1) a date-by-category pivot table for the chosen metric field (processed_rows or duration_seconds), and (2) a version of that pivot with missing values explicitly filled using the selected strategy (zero, mean, or median). All computation is local with no external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, date, dataset_group) in any row — validation error","Invalid date format (non-YYYY-MM-DD) — schema validation failure","Unsupported fill method or field enum value — rejected input","Empty rows array — minimum 1 item required","Numeric values outside allowed range (±1 trillion) — schema rejection"],"whenToPreferThis":"Choose this endpoint when you have structured transformation throughput records (processed_rows and duration_seconds) that need to be reshaped into a date-by-category pivot table with controlled missing-value handling. It is ideal for lightweight, self-contained analytical reporting on ETL or data pipeline metrics without requiring an external analytics platform. Prefer it when you need two reports per call (pivot + fill) on datasets of up to 200 rows at minimal cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:26.024Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-date-by-category-pivot-with-8a91f880"}