{"uid":"cap_rV4HpbYn6tKmBteo0IJxs","slug":"data-pipeline-runs-explicit-missing-value-fill-2dd9802a","name":"Data Pipeline Runs — Explicit Missing-Value Fill","description":"Data pipeline runs - Explicit missing-value fill: Explicit missing-value fill for Data pipeline runs. Up to 200 supplied typed records. Local descriptive analytics; no enrichment or forecast. One of 25 new profiles across 20 shared operations in this industry. 0.01 USDC includes one bounded explicit missing-value fill report.","url":"https://market.datapackvibe.com/x402/assistant-ar-analytics-pipeline-runs-fill-missing?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","pipeline","date","processed_rows","rejected_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"pipeline":{"type":"string","default":"","maxLength":80},"rejected_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","pipeline":"group-a","rejected_rows":1,"processed_rows":10},{"date":"2026-10-02","row_id":"row-2","pipeline":"group-b","rejected_rows":2,"processed_rows":20},{"date":"2026-10-03","row_id":"row-3","pipeline":"group-a","rejected_rows":3,"processed_rows":15},{"date":"2026-10-04","row_id":"row-4","pipeline":"group-b","rejected_rows":4,"processed_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","rejected_rows"],"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_mHqw6hmjNf0wew4kw-9ZL","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":"Fills missing numeric values (processed_rows or rejected_rows) in up to 200 data pipeline run records using zero, mean, or median imputation.","exampleAgentPrompt":"I have up to 200 data pipeline run records with some missing processed_rows values — can you fill those nulls using the median method across pipelines like group-a and group-b with dates like 2026-10-01?","exampleUseCases":[{"title":"ETL Monitoring Gap Repair","prompt":"My daily ETL pipeline logs have some null processed_rows entries for last week — can you fill in those missing values using the median so I can run clean analytics on the full dataset?"},{"title":"Rejected-Row Imputation for QA Report","prompt":"I'm building a QA report on our pipeline runs but rejected_rows is missing for a few records. Can you fill those gaps with the mean value across the dataset so nothing is left blank?"},{"title":"Zero-Fill for Dashboard Baseline","prompt":"For our pipeline run dashboard, I need any missing processed_rows counts replaced with zero so incomplete runs show up as 0 rather than null — can you apply zero-fill to these 150 records?"}],"resultDescription":"A bounded missing-value fill report containing the input pipeline run records with null numeric values (processed_rows or rejected_rows) replaced using the chosen method (zero, mean, or median). The output includes the corrected records and descriptive analytics computed locally without external enrichment or forecasting.","failureModes":["Empty or malformed rows array (minItems:1 not met)","rows array exceeds 200 items (maxItems:200 exceeded)","Invalid date format (must match YYYY-MM-DD pattern)","row_id missing or exceeds 80 characters","Invalid field enum value (must be processed_rows or rejected_rows)","Invalid method enum value (must be zero, mean, or median)","Numeric values outside allowed range (-1e12 to 1e12)","Payment failure or insufficient USDC balance"],"whenToPreferThis":"Use this endpoint when you have up to 200 pipeline run records with null numeric fields and need a fast, local imputation (zero, mean, or median) without any external data enrichment or forecasting. Prefer it over general-purpose data cleaning tools when your data is specifically structured as pipeline run records (processed_rows, rejected_rows per pipeline per date) and you need a bounded, auditable fill report at low cost ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-18T12:56:40.691Z","isFirstParty":false,"canonicalSlug":"data-pipeline-runs-explicit-missing-value-fill-2dd9802a"}