{"uid":"cap_YuEoTgDNKS_YE9wpzbEwh","slug":"null-coverage-string-normalisation-and-missing-value-fill-1c08feb1","name":"Null Coverage String Normalisation and Missing-Value Fill","description":"Null coverage — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied null coverage records (missing_values, total_values). 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-03-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","missing_values","total_values"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"missing_values":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_values":1,"dataset_group":"group-a","missing_values":10},{"date":"2026-10-02","row_id":"row-2","total_values":2,"dataset_group":"group-b","missing_values":20},{"date":"2026-10-03","row_id":"row-3","total_values":3,"dataset_group":"group-a","missing_values":15},{"date":"2026-10-04","row_id":"row-4","total_values":4,"dataset_group":"group-b","missing_values":30}],"maxItems":200,"minItems":1},"field":{"enum":["missing_values","total_values"],"type":"string","default":"missing_values"},"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_bidDrPkNfctpOI_2RG678","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":"Normalises string fields and fills explicit missing values (zero, mean, or median) in null-coverage records containing missing_values and total_values columns, returning 2 analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of null coverage records with missing_values and total_values columns grouped by dataset_group — can you normalise the strings and fill the missing_values field using median imputation, then give me two analytical reports?","exampleUseCases":[{"title":"Median imputation for data quality audit","prompt":"I have 150 rows of dataset quality records with row_id, date, dataset_group, missing_values, and total_values — some missing_values entries are null. Can you fill those nulls using median imputation and give me a null coverage report?"},{"title":"Zero-fill missing coverage for pipeline validation","prompt":"My data pipeline produces null coverage records across two groups and some total_values entries are missing. Fill the total_values column with zeros for all 80 rows and return two analytical reports so I can validate completeness."},{"title":"Mean imputation across grouped dataset records","prompt":"I have daily null coverage records across group-a and group-b for October 2026 — about 40 rows — and some missing_values fields are null. Use mean imputation on the missing_values field and give me the two summary reports."}],"resultDescription":"Two analytical reports covering the supplied null coverage rows (up to 200): one covering string normalisation applied to fields like dataset_group and date, and one covering explicit missing-value fill results for the chosen field (missing_values or total_values) using the selected method (zero, mean, or median), including imputed values and coverage statistics.","failureModes":["Row count exceeds 200 items — request rejected","Required fields (row_id, date, missing_values, total_values) absent from one or more rows","Date strings not matching YYYY-MM-DD pattern — validation error","Numeric values outside allowed range (±1 trillion) — rejected","Invalid method enum value (not zero/mean/median) — error","Invalid field enum value (not missing_values/total_values) — error","Empty rows array — minimum 1 item required"],"whenToPreferThis":"Choose this endpoint when you have a bounded set of up to 200 null coverage records (with missing_values and total_values columns) and need both string normalisation and explicit missing-value imputation (zero, mean, or median) with two structured analytical reports, all processed locally without any external data lookup or live prediction. Prefer it over general-purpose data tools when you specifically need dual-report output on null coverage metrics per dataset group.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T06:55:11.347Z","isFirstParty":false,"canonicalSlug":"null-coverage-string-normalisation-and-missing-value-fill-1c08feb1"}