{"uid":"cap_NuAS92k5xYIdYz9XbbbUl","slug":"numeric-precision-comparisons-adjacent-row-changes-explicit-missing-81129801","name":"Numeric Precision Comparisons: Adjacent Row Changes + Explicit Missing-Value Fill","description":"Numeric precision comparisons — Adjacent row changes + Explicit missing-value fill: Adjacent row changes + Explicit missing-value fill for supplied numeric precision comparisons records (source_value, transformed_value). 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-20-162?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"},"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_AUvJN8PIDhiKZRIQ2IS-V","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 adjacent row changes and fills missing values (zero/mean/median) for up to 200 rows of source_value/transformed_value numeric data, returning 2 analytical reports.","exampleAgentPrompt":"I have a dataset of up to 50 rows with source_value and transformed_value columns — can you compute the adjacent row changes and fill any missing source_value entries using median imputation?","exampleUseCases":[{"title":"ETL pipeline quality check","prompt":"I'm running an ETL job and need to validate my transformed_value column — can you compute row-over-row deltas and fill any nulls with the mean for these 80 rows grouped by 'pipeline-A'?"},{"title":"Financial data gap filling","prompt":"I have daily financial records from 2025-01-01 to 2025-04-30 with some missing source_value entries — fill the gaps using zero and also show me the adjacent row changes across all 120 rows."},{"title":"Sensor reading comparison audit","prompt":"Can you analyze these 150 sensor readings with both source and transformed values, compute adjacent row changes, and replace any null transformed_value readings with the median?"}],"resultDescription":"Two analytical reports: (1) adjacent row change report showing delta between each consecutive row for the specified field, and (2) a missing-value fill report with nulls replaced using the chosen method (zero, mean, or median). Covers up to 200 rows per call.","failureModes":["More than 200 rows submitted — request rejected or truncated","Invalid date format (not YYYY-MM-DD) causes row-level validation error","Values outside ±1,000,000,000,000 range cause schema rejection","Missing required fields (row_id, dataset_group, date) cause validation failure","Invalid fill method enum value causes request rejection"],"whenToPreferThis":"Choose this endpoint when you need both adjacent row delta computation and missing-value imputation in a single call on a small-to-medium dataset (≤200 rows) without any external lookups or live data. Ideal for batch data quality workflows, ETL validation, or offline numeric analysis where you supply the full dataset and want two complementary reports cheaply ($0.01 USDC per call).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:26.610Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-adjacent-row-changes-explicit-missing-81129801"}