{"uid":"cap_KpKM1nqc6tzp52p7BfKhf","slug":"feature-availability-string-normalisation-and-missing-value-fill-21eb0bf3","name":"Feature Availability String Normalisation and Missing-Value Fill","description":"Feature availability — String normalisation + Explicit missing-value fill: String normalisation + Explicit missing-value fill for supplied feature availability records (populated_features, expected_features). 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-10-037?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","populated_features","expected_features"],"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},"expected_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"populated_features":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","expected_features":1,"populated_features":10},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","expected_features":2,"populated_features":20},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","expected_features":3,"populated_features":15},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","expected_features":4,"populated_features":30}],"maxItems":200,"minItems":1},"field":{"enum":["populated_features","expected_features"],"type":"string","default":"populated_features"},"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_SbHgAM8BS9Sr1lPrcy7LU","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 representations and fills missing values in feature availability records (populated_features, expected_features) for up to 200 rows, producing 2 analytical reports.","exampleAgentPrompt":"I have a set of feature availability records across two groups — 'group-a' and 'group-b' — with dates from 2026-10-01 to 2026-10-04 and some missing values in populated_features. Can you normalise the strings and fill those missing values using median imputation, and give me back 2 analytical reports?","exampleUseCases":[{"title":"Impute missing feature counts before model training","prompt":"I have up to 150 rows of feature availability data with row IDs, dataset groups, and dates, but the populated_features column has some nulls. Can you fill those missing values using the mean method and normalise the string fields so my data is clean before I train?"},{"title":"Median-fill expected features for data quality audit","prompt":"My expected_features column has several missing entries across my dataset rows. Can you apply median imputation to fill those gaps and normalise any string formatting issues, giving me back two reports summarising what was done?"},{"title":"Zero-fill populated features for completeness dashboard","prompt":"I'm building a feature completeness dashboard and need to zero-fill any missing populated_features values across about 80 rows spanning groups 'prod' and 'staging' with dates in October 2026. Can you also normalise the string values and return two analytical reports?"}],"resultDescription":"Returns 2 analytical reports covering (1) string-normalised and (2) missing-value-filled feature availability records for the submitted rows, processed locally with no external lookups or predictions.","failureModes":["More than 200 rows submitted — request rejected (maxItems exceeded)","Missing required fields (row_id, date, dataset_group, populated_features, expected_features) on any row — validation error","Date field does not match YYYY-MM-DD pattern — schema validation failure","populated_features or expected_features values outside allowed numeric range — rejection","Invalid fill method or field enum value — schema validation error","Payment not included or insufficient USDC — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, local string normalisation and missing-value imputation specifically for feature availability records (populated_features / expected_features) without requiring a live data connection, external prediction, or professional assessment. Ideal for preprocessing pipelines handling up to 200 rows that need two co-produced analytical reports in a single low-cost call ($0.01 USDC).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-03T06:39:31.516Z","isFirstParty":false,"canonicalSlug":"feature-availability-string-normalisation-and-missing-value-fill-21eb0bf3"}