{"uid":"cap_zBMy17ptROPiKt1hHHdrU","slug":"feature-availability-analytics-numeric-distribution-missing-value-fill-929d6323","name":"Feature Availability Analytics: Numeric Distribution + Missing Value Fill","description":"Feature availability — Numeric distribution + Explicit missing-value fill: Numeric distribution + 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-10-085?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_GP0_wmGoDN9hOlFZZqSHP","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":"Computes numeric distribution statistics and fills missing values (zero, mean, or median) for feature availability records across up to 200 rows, producing two independent analytical reports.","exampleAgentPrompt":"Can you analyze the numeric distribution of my populated_features data and fill in any missing values using median imputation? I have about 50 rows across two dataset groups — here's the data with row_id, date, dataset_group, populated_features, and expected_features fields.","exampleUseCases":[{"title":"Feature coverage audit across dataset groups","prompt":"I have 120 rows of feature availability records with row_id, date, dataset_group, populated_features, and expected_features columns — can you give me the numeric distribution of populated_features and fill any nulls using the mean?"},{"title":"Missing value imputation for ML pipeline prep","prompt":"Before I train my model, I need to fill in the missing expected_features values in my dataset using zero-fill. I have up to 200 rows with row_id, date, dataset_group, and feature counts — can you run the missing value fill and distribution report on expected_features?"},{"title":"Daily feature availability trend QA","prompt":"I'm doing a data quality check on 80 rows of daily feature availability logs — can you compute the numeric distribution of populated_features and replace any null entries with the median value? Each row has a date, row_id, dataset_group, and both populated and expected feature counts."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution summary (e.g. min, max, mean, median, percentiles) for the selected field across submitted rows, and (2) a missing-value fill report showing the original and imputed values using the chosen method (zero, mean, or median) — all computed locally without external lookups.","failureModes":["Input array is empty or exceeds 200 rows — returns validation error","Invalid date format (not YYYY-MM-DD) — rejected by schema pattern validation","Null or missing row_id — rejected as required field","Invalid method enum value (not zero/mean/median) — schema rejection","Invalid field enum (not populated_features/expected_features) — schema rejection","Payment failure via x402 protocol — no report returned"],"whenToPreferThis":"Choose this endpoint when you need both a numeric distribution summary and missing-value imputation in a single call for feature availability data (populated_features / expected_features). It is best suited for small-to-medium datasets (up to 200 rows) where you want offline, deterministic statistical analysis without live data lookups or external ML inference. Prefer it over general-purpose analytics APIs when cost predictability matters ($0.01 USDC flat for both reports).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:41.635Z","isFirstParty":false,"canonicalSlug":"feature-availability-analytics-numeric-distribution-missing-value-fill-929d6323"}