{"uid":"cap_Qh6zQUEc58DzCgm2X6uYJ","slug":"transformation-throughput-analytics-numeric-interval-filter-explicit-c0c1cf86","name":"Transformation Throughput Analytics — Numeric Interval Filter + Explicit Missing-Value Fill","description":"Transformation throughput — Numeric interval filter + Explicit missing-value fill: Numeric interval filter + 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-13-135?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"},"maximum":{"type":"number","default":100,"maximum":1000000000000,"minimum":-1000000000000},"minimum":{"type":"number","default":0,"maximum":1000000000000,"minimum":-1000000000000}}},"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_yX-1xQwTLG0_vCHCxFUva","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":"Filters transformation throughput records by a numeric interval and fills missing values in processed_rows or duration_seconds fields using zero, mean, or median imputation, returning up to 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have 150 transformation throughput records with some missing processed_rows values — can you filter them to keep only rows where processed_rows is between 0 and 100, and fill any missing processed_rows values using the median? I need 2 reports back.","exampleUseCases":[{"title":"Clean ETL pipeline row counts","prompt":"I've got my ETL job logs for the past month — around 80 records — and some processed_rows values are null. Can you filter to only keep rows where processed_rows is between 10 and 500,000, and fill the nulls with the mean? Give me the 2 analytical reports."},{"title":"Impute missing job durations for reporting","prompt":"My transformation throughput dataset has 120 rows and a bunch of null duration_seconds entries. Please filter out anything outside the 0 to 3600 second range and fill missing duration_seconds with zero so I can get clean reports."},{"title":"Validate data group throughput ranges","prompt":"I have 60 pipeline records split across two dataset groups with some missing processed_rows. Filter them to the interval 5 to 50000 and impute nulls using median — I need the 2 summary reports to check if both groups look healthy."}],"resultDescription":"Returns up to 2 analytical reports covering the supplied transformation throughput rows after applying the specified numeric interval filter (keeping rows where the target field falls within [minimum, maximum]) and filling any null values in the target field using the chosen method (zero, mean, or median). Reports are generated locally without external lookups or predictions, covering up to 200 input rows per call.","failureModes":["Input array exceeds 200 rows — request will be rejected","Invalid date format (not YYYY-MM-DD) in any row — validation error","minimum greater than maximum — invalid interval configuration","Unknown fill method value outside zero/mean/median enum — schema error","Missing required fields (row_id, date, processed_rows, duration_seconds) in any row — validation failure","Empty rows array — rejected with minItems violation"],"whenToPreferThis":"Choose this endpoint when you have transformation throughput records (with processed_rows and duration_seconds metrics) and need to simultaneously apply a numeric range filter AND handle missing values in a single analytical pass, receiving up to 2 structured reports. It is ideal for data engineering teams cleaning ETL job metrics without needing external data sources, predictions, or live lookups. Prefer it over general-purpose data tools when your dataset fits within 200 rows and you want a cost-effective ($0.01 USDC) local computation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:37:53.837Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-numeric-interval-filter-explicit-c0c1cf86"}