{"uid":"cap_3mapOSdFCexKLvQ9aq9rL","slug":"numeric-precision-comparisons-two-measure-correlation-missing-value-230da269","name":"Numeric Precision Comparisons — Two-Measure Correlation + Missing-Value Fill","description":"Numeric precision comparisons — Two-measure correlation + Explicit missing-value fill: Two-measure correlation + 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-20-184?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_QDnZlJh83FXOlLWQGkgBH","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 correlation between source and transformed numeric values and fills missing values using a chosen imputation method, across up to 200 rows of supplied data.","exampleAgentPrompt":"I have up to 200 rows of numeric data with source_value and transformed_value columns — some values are missing. Can you run a correlation analysis between those two measures and fill any nulls in source_value using the median, grouped by dataset_group and date?","exampleUseCases":[{"title":"ETL pipeline data quality check","prompt":"I just ran a transformation on our sales figures and want to see how closely the transformed values track the originals. Here are 150 rows with source_value and transformed_value — run a correlation report and fill any nulls in transformed_value using the mean."},{"title":"Sensor reading imputation before modeling","prompt":"I have sensor readings with some missing source_value entries. Can you fill those gaps using the median and also tell me how correlated source_value and transformed_value are across these 80 rows before I pass them to my ML model?"},{"title":"Financial data reconciliation audit","prompt":"We have two numeric columns — raw ledger amounts and adjusted amounts — across 200 records. Some adjusted values are null. Please fill the missing adjusted values with zero and compute the correlation between the two columns so I can flag any reconciliation issues."}],"resultDescription":"Returns two analytical reports: (1) a correlation report showing the statistical relationship between source_value and transformed_value across the provided rows, and (2) a missing-value fill report showing which entries were imputed and their replacement values, using the selected method (zero, mean, or median) on the specified field.","failureModes":["Fewer than 1 row or more than 200 rows submitted — rejected by schema","Invalid date format (not YYYY-MM-DD) causes validation error","source_value or transformed_value outside ±1,000,000,000,000 range causes rejection","Invalid imputation method (not zero/mean/median) causes enum validation error","All values in the target field are null — mean/median imputation may be undefined","row_id missing or empty string — required field validation failure"],"whenToPreferThis":"Choose this endpoint when you have a batch of up to 200 rows with paired numeric columns (source and transformed) and need both a correlation analysis and a missing-value imputation report in a single call. It is ideal for data quality checks after ETL transformations, pre-modeling data preparation, or reconciliation audits where null handling and linear relationship strength must be assessed together without external data lookups.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:51.095Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-two-measure-correlation-missing-value-230da269"}