{"uid":"cap_1kyq7LKtIJTpxlqcqbMiH","slug":"numeric-precision-comparisons-distribution-missing-value-fill-analytics-7f6af31d","name":"Numeric Precision Comparisons — Distribution & Missing-Value Fill Analytics","description":"Numeric precision comparisons — Numeric distribution + Explicit missing-value fill: Numeric distribution + 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-20-085?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_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_3kLoiq0qrnLFpocOZu7JO","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 for supplied source_value/transformed_value pairs, delivering two independent analytical reports for up to 200 rows.","exampleAgentPrompt":"I have up to 200 rows of source and transformed numeric values with some nulls — can you run a numeric distribution analysis and fill the missing values in source_value using median imputation? Here's my data with row IDs, dataset groups, and dates.","exampleUseCases":[{"title":"ETL pipeline data quality check","prompt":"I just ran an ETL transformation on my sales figures and I need to check whether the distribution of the transformed_value column looks reasonable compared to source_value — can you analyze the numeric distribution and fill any missing transformed values using mean imputation? I have about 150 rows with row IDs, dates, and dataset groups."},{"title":"Impute missing sensor readings","prompt":"I have a dataset of 80 sensor readings with some null values in source_value — please fill the missing entries using median and give me a distribution report so I can see the spread of valid readings across my dataset groups."},{"title":"Validate data transformation accuracy","prompt":"After applying a scaling transformation to my financial data, I want to compare the numeric distribution of source values versus transformed values and fill any gaps with zeros — I have 120 rows organized by date and group, can you run both reports?"}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution report covering spread, central tendency, and range statistics for the selected field across the supplied rows; and (2) a missing-value fill report showing which null entries were imputed and their replacement values using the chosen method (zero, mean, or median). Both reports operate entirely on the supplied data with no external lookups.","failureModes":["Rows array empty or missing — minimum 1 row required","More than 200 rows submitted — exceeds maxItems limit","Invalid date format — must match YYYY-MM-DD pattern","row_id missing or empty — required field with minLength 1","Numeric values outside allowed range (±1,000,000,000,000) — validation error","Invalid method enum value — must be zero, mean, or median","Invalid field enum value — must be source_value or transformed_value","Payment not included or insufficient — requires 0.01 USDC via x402"],"whenToPreferThis":"Choose this endpoint when you need fast, local statistical analysis of numeric comparison pairs (source vs. transformed values) without any external data access, prediction, or professional assessment. It is ideal for data quality workflows where you need both a distribution summary and missing-value imputation in a single call at very low cost. Prefer this over heavier analytics platforms when your dataset is under 200 rows and you only need descriptive statistics plus null filling.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:06.504Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-distribution-missing-value-fill-analytics-7f6af31d"}