{"uid":"cap_vXeuFr9yTl6gvfekyavcn","slug":"table-extraction-numeric-row-ordering-explicit-missing-value-fill-cc896ac6","name":"Table Extraction — Numeric Row Ordering + Explicit Missing-Value Fill","description":"Table extraction — Numeric row ordering + Explicit missing-value fill: Numeric row ordering + Explicit missing-value fill for supplied table extraction records (extracted_rows, expected_rows). 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-web-10-124?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","site_group","date","extracted_rows","expected_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"site_group":{"type":"string","default":"","maxLength":80},"expected_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"extracted_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","site_group":"group-a","expected_rows":1,"extracted_rows":10},{"date":"2026-10-02","row_id":"row-2","site_group":"group-b","expected_rows":2,"extracted_rows":20},{"date":"2026-10-03","row_id":"row-3","site_group":"group-a","expected_rows":3,"extracted_rows":15},{"date":"2026-10-04","row_id":"row-4","site_group":"group-b","expected_rows":4,"extracted_rows":30}],"maxItems":200,"minItems":1},"field":{"enum":["extracted_rows","expected_rows"],"type":"string","default":"extracted_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_lmIajumWiZFJjJnuaz3vl","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":"Sorts table extraction records numerically by row order and fills missing values in extracted_rows or expected_rows using zero, mean, or median imputation, producing 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a batch of 50 table extraction records across two site groups with some missing extracted_rows values — can you sort them numerically by row order, fill the nulls using median imputation, and give me the 2 analytical reports?","exampleUseCases":[{"title":"ETL pipeline row count audit","prompt":"I have 120 extraction log rows for site group 'warehouse-a' dated between 2025-01-01 and 2025-04-30, some with missing extracted_rows counts — sort them in numeric row order and fill the gaps using mean imputation so I can audit the pipeline."},{"title":"Expected vs extracted discrepancy cleanup","prompt":"My table extraction report has about 80 rows where expected_rows is sometimes null — fill those missing expected_rows values using zero and sort everything numerically so I can compare against what was actually extracted."},{"title":"Data quality report for scraping job","prompt":"Can you take these 40 daily scraping records across site groups 'retail' and 'logistics', sort them by row ID numerically, impute any missing extracted_rows with the median, and produce the two analytical reports on data completeness?"}],"resultDescription":"Returns 2 analytical reports covering the submitted records (up to 200 rows) after applying numeric row ordering and explicit missing-value fill (zero, mean, or median) to the specified field (extracted_rows or expected_rows). Reports summarize ordered row sequences and imputed values based on the chosen method, using local analytical engines with no external lookups.","failureModes":["Submitting more than 200 rows returns an error (maxItems exceeded)","Invalid date format (not YYYY-MM-DD) triggers schema validation failure","Missing required fields (row_id) causes request rejection","Values outside ±1,000,000,000,000 range are rejected","Invalid method enum value (not zero/mean/median) causes error","Invalid field enum value (not extracted_rows/expected_rows) causes error"],"whenToPreferThis":"Choose this endpoint when you need to programmatically sort table extraction audit records by numeric row order AND impute missing values in a single pass, producing two structured analytical reports. It is ideal for ETL auditing, scraping QA, and data pipeline validation workflows where you have up to 200 rows of extracted vs expected row count data with nulls. Prefer this over generic stats endpoints when you specifically need combined numeric ordering plus explicit missing-value fill with configurable imputation strategy.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-26T12:33:13.546Z","isFirstParty":false,"canonicalSlug":"table-extraction-numeric-row-ordering-explicit-missing-value-fill-cc896ac6"}