{"uid":"cap_egVyM8Sm-n1So8HuX-_K4","slug":"numeric-precision-comparisons-category-frequencies-iqr-outlier-flags-d1db2f2b","name":"Numeric Precision Comparisons — Category Frequencies + IQR Outlier Flags","description":"Numeric precision comparisons — Category frequencies + IQR outlier flags: Category frequencies + IQR outlier flags 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-20-107?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"}}},"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_cqWkbCuFb-i10-VzVhIwO","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 rows of source/transformed numeric pairs, producing category frequency distributions and IQR-based outlier flags as two independent analytical reports.","exampleAgentPrompt":"I have 150 rows of source and transformed numeric values across two groups — can you run category frequency and IQR outlier detection on the source_value field for me? Here's the data with row IDs, dataset groups, and dates.","exampleUseCases":[{"title":"ETL pipeline data quality check","prompt":"I just ran a data transformation pipeline and want to verify the output. Can you analyze these 80 rows — each has a row_id, dataset_group, date, source_value, and transformed_value — and give me both a category frequency breakdown and IQR outlier flags on the transformed_value field so I can spot any bad records?"},{"title":"Sales data anomaly detection","prompt":"I have daily sales figures from two regional groups across 120 records. Can you run IQR outlier detection and category frequency analysis on the source_value field to tell me which entries look unusual and how the values are distributed across the groups?"},{"title":"Scientific measurement comparison","prompt":"We have 200 paired measurements — original readings and calibrated values — grouped by instrument type. Please analyze the source_value field for outliers using IQR and give me a frequency breakdown by dataset_group so I can see where calibration might be off."}],"resultDescription":"Returns two independent analytical reports: (1) category frequencies showing value distribution broken down by dataset_group, and (2) IQR-based outlier flags identifying which rows contain extreme values in the selected field (source_value or transformed_value). All computation is performed locally with no external lookups.","failureModes":["Empty or missing rows array returns an error","More than 200 rows submitted exceeds the limit and is rejected","Invalid date format (not YYYY-MM-DD) fails schema validation","Numeric values outside ±1,000,000,000,000 are rejected","Missing required fields (row_id, dataset_group, date) cause validation errors","Invalid field enum value (not source_value or transformed_value) is rejected"],"whenToPreferThis":"Choose this endpoint when you need both frequency distribution analysis and IQR-based outlier detection on numeric source/transformed value pairs in a single call, for datasets up to 200 rows. Ideal for data quality validation after ETL transforms, where you want to inspect both the spread of values by category and identify statistical outliers without any external API dependency or live lookup.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:46.056Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-category-frequencies-iqr-outlier-flags-d1db2f2b"}