{"uid":"cap_dw24XK6sLwZE6fr1kVEIA","slug":"numeric-precision-comparisons-daily-sum-series-secondary-measure-750cb0b3","name":"Numeric Precision Comparisons — Daily Sum Series + Secondary-Measure Weighted Mean","description":"Numeric precision comparisons — Daily sum series + Secondary-measure weighted mean: Daily sum series + Secondary-measure weighted mean 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-20-142?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_VQWuUq9O-F5lziD8FwScl","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 a daily sum time series and a secondary-measure weighted mean for up to 200 rows of numeric precision comparison data (source_value vs transformed_value).","exampleAgentPrompt":"Can you run the daily sum series and weighted mean analysis on my numeric precision comparison data? I have 4 rows across group-a and group-b from 2026-10-01 to 2026-10-04, with source_values of 10, 20, 15, 30 and transformed_values of 1, 2, 3, 4 — use source_value as the primary field.","exampleUseCases":[{"title":"ETL pipeline accuracy validation","prompt":"I ran a data transformation pipeline and want to check if the numeric values changed meaningfully. Can you take my before-and-after rows — up to 80 records with source_value and transformed_value per date — and give me both a daily sum series and a weighted mean so I can spot where precision was lost?"},{"title":"Daily revenue aggregation comparison","prompt":"I have daily revenue figures from two systems, source and transformed, across two dataset groups from 2025-01-01 to 2025-03-31 — about 90 rows total. Can you compute the daily sum series and the secondary-measure weighted mean using source_value as the field so I can see how they diverge day by day?"},{"title":"Data quality audit across groups","prompt":"We have numeric precision comparison records for group-a and group-b — 150 rows dated across Q4 2025 — and I need both a daily sum report and a weighted mean for transformed_value to audit whether our normalization step is introducing bias."}],"resultDescription":"Returns two analytical reports for the supplied rows: (1) a daily sum series aggregating the chosen field (source_value or transformed_value) by date, and (2) a secondary-measure weighted mean using the complementary numeric column as weights. Results are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — request rejected","More than 200 rows supplied — exceeds limit","date field does not match YYYY-MM-DD pattern — validation error","source_value or transformed_value outside ±1,000,000,000,000 — rejected","Missing required fields (row_id, dataset_group, date) — schema validation error","Payment not completed or insufficient USDC — request not processed"],"whenToPreferThis":"Choose this endpoint when you need to compare source and transformed numeric values across dates using both a daily sum aggregation and a weighted mean in a single paid call, with no need for external data or live lookups. Ideal for ETL validation, data quality audits, and precision-loss analysis on small-to-medium datasets (≤200 rows). Prefer alternatives if you need rolling means, outlier detection, deduplication, or more than 200 rows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:03.399Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-daily-sum-series-secondary-measure-750cb0b3"}