{"uid":"cap_6MxWjnd91lrZ8_rcQXQRG","slug":"data-reconciliation-numeric-distribution-explicit-missing-value-fill-2e4428cf","name":"Data Reconciliation – Numeric Distribution + Explicit Missing-Value Fill","description":"Data reconciliation — Numeric distribution + Explicit missing-value fill: Numeric distribution + Explicit missing-value fill for supplied data reconciliation records (source_total, target_total). 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-11-085?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_total","target_total"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"target_total":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_total":10,"target_total":1,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_total":20,"target_total":2,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_total":15,"target_total":3,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_total":30,"target_total":4,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["source_total","target_total"],"type":"string","default":"source_total"},"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_2jWL6JUmFr6Bdm3f4SRKn","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":"Analyzes up to 200 data reconciliation rows (source_total vs target_total) by producing a numeric distribution report and filling missing values in a chosen field using zero, mean, or median imputation.","exampleAgentPrompt":"Can you run a numeric distribution report and fill any missing source_total values using median imputation on my reconciliation dataset? I have rows with row_id, dataset_group, date, source_total, and target_total — up to 50 rows across group-a and group-b.","exampleUseCases":[{"title":"Finance team reconciliation data audit","prompt":"We have a reconciliation export with source_total and target_total for about 120 rows across multiple dataset groups — some source_total entries are null. Can you analyze the numeric distribution of source_total and fill the missing values using mean imputation?"},{"title":"Monthly report missing-value cleanup","prompt":"I'm preparing a monthly report and some of my target_total figures are missing. Run a distribution summary on my target_total field and fill the nulls with zero — I have about 80 reconciliation rows dated from 2025-01-01 to 2025-01-31."},{"title":"QA check on pipeline output data","prompt":"Our data pipeline produced 200 reconciliation rows and I need to verify the numeric distribution of source_total looks reasonable, and replace any missing source_total values using median. Can you run both reports at once?"}],"resultDescription":"Returns two analytical reports: (1) a numeric distribution summary of the selected field (source_total or target_total) across up to 200 reconciliation rows, and (2) a version of the dataset with missing values in the chosen field explicitly filled using the selected imputation method (zero, mean, or median). No external lookups or predictions are made — all computation is local.","failureModes":["More than 200 rows submitted — exceeds maxItems limit, request will be rejected","Missing required fields (row_id, dataset_group, date, source_total, target_total) on any row — schema validation failure","Date fields not matching YYYY-MM-DD pattern — schema validation error","Numeric values outside ±1,000,000,000,000 range — schema validation failure","Invalid method enum value (anything other than zero, mean, median) — rejected","Invalid field enum value (anything other than source_total, target_total) — rejected","Empty rows array — minItems constraint violation"],"whenToPreferThis":"Choose this endpoint when you need a combined numeric distribution analysis and missing-value imputation for a batch of up to 200 reconciliation records with source and target totals. It is ideal for data quality pipelines, finance reconciliation audits, and ETL validation steps where you want both descriptive statistics and cleaned data in a single call. It is not suitable when you need live data fetching, predictive modeling, or more than 200 rows processed at once.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:45:47.656Z","isFirstParty":false,"canonicalSlug":"data-reconciliation-numeric-distribution-explicit-missing-value-fill-2e4428cf"}