{"uid":"cap_EYUJQJhhEI_7KexTnqYSo","slug":"feature-availability-duplicate-key-report-explicit-missing-value-fill-7e5e792b","name":"Feature Availability Duplicate Key Report + Explicit Missing-Value Fill","description":"Feature availability — Duplicate key report + Explicit missing-value fill: Duplicate key report + 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-10-070?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_n4mHP4HS_hbdDeEaFQTiu","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":"Runs two local analytical reports on feature availability records: a duplicate key audit and an explicit missing-value fill using zero, mean, or median imputation, for up to 200 rows.","exampleAgentPrompt":"Can you run a duplicate key report and fill in any missing populated_features values using median imputation on my feature availability records — I have about 150 rows with row_id, dataset_group, date, populated_features, and expected_features fields?","exampleUseCases":[{"title":"Audit ML training data for duplicate keys","prompt":"I have 180 rows of feature availability data with row IDs, dataset groups, dates, populated_features, and expected_features — can you check them for any duplicate row keys and give me a report?"},{"title":"Impute missing feature counts before model training","prompt":"Some of my expected_features values are null across about 120 feature availability records — can you fill those missing values using the mean and give me back the completed dataset?"},{"title":"Dual data quality check before pipeline handoff","prompt":"Before I hand off my feature availability dataset to the next stage, can you run both a duplicate key audit and fill any missing populated_features with zero — there are around 90 rows covering multiple dataset groups?"}],"resultDescription":"Returns two independent reports: (1) a duplicate key report identifying any rows sharing the same key fields, and (2) an explicit missing-value fill report where null values in the selected field (populated_features or expected_features) are replaced using the chosen method (zero, mean, or median), all computed locally with no external lookups.","failureModes":["Fewer than 1 row provided — minimum 1 row required","More than 200 rows submitted — exceeds the maxItems limit","Invalid date format (must be YYYY-MM-DD pattern)","row_id missing or empty string — required field","Numeric values outside the allowed range (-1e12 to 1e12)","Invalid enum value for field or method parameters","Malformed JSON input body"],"whenToPreferThis":"Choose this endpoint when you need a fast, low-cost ($0.01 USDC) dual-report data quality check on feature availability records — specifically combining duplicate key detection with missing-value imputation in a single call. Ideal for data pipeline preprocessing steps where you want to catch duplicate row keys and fill nulls before model training or downstream analytics, without requiring live external lookups or professional assessment.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:32.516Z","isFirstParty":false,"canonicalSlug":"feature-availability-duplicate-key-report-explicit-missing-value-fill-7e5e792b"}