{"uid":"cap_s0e3mOGMI0KKW-LsHj66C","slug":"numeric-precision-comparisons-string-normalisation-equal-width-c58d3ab3","name":"Numeric Precision Comparisons — String Normalisation + Equal-Width Histogram","description":"Numeric precision comparisons — String normalisation + Equal-width histogram: String normalisation + Equal-width histogram 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-20-031?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"bins":{"type":"integer","default":5,"maximum":30,"minimum":1},"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_fyBcrNJMzCRcLeSYyP9fB","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 numeric precision comparison data (source vs transformed values) by producing a string normalization report and an equal-width histogram, locally, for up to 200 rows.","exampleAgentPrompt":"Can you run a string normalization and equal-width histogram analysis on these source vs transformed value pairs from group-a and group-b — use 10 bins and analyze the source_value field? I have about 50 rows dated between 2025-01-01 and 2025-03-31.","exampleUseCases":[{"title":"ETL pipeline numeric drift audit","prompt":"I've got 150 rows from my ETL pipeline with original and post-transform values for two dataset groups — can you build an equal-width histogram with 8 bins on the transformed_value field and also give me a string normalization report so I can see where precision is shifting?"},{"title":"Data quality check on financial calculations","prompt":"We processed 80 financial records and I need to compare source amounts versus calculated outputs — run a histogram with 5 bins on source_value and normalize the values so I can spot any precision issues across the dataset groups."},{"title":"QA validation of ML feature engineering","prompt":"Our feature engineering step changes raw scores into scaled values — I have 200 rows with row IDs, dates, and both raw and scaled numbers. Can you produce a string normalization report and a 15-bin equal-width histogram on the transformed_value to help QA the output?"}],"resultDescription":"Returns two independent analytical reports: (1) a string normalization report and (2) an equal-width histogram with the specified number of bins, both computed locally from the supplied rows of source_value and transformed_value pairs. No external lookups or predictions are made.","failureModes":["More than 200 rows submitted — request rejected, maxItems limit exceeded","Invalid date format not matching YYYY-MM-DD pattern — validation error","Bins value outside 1–30 range — rejected with schema error","Missing required fields (row_id, dataset_group, date, source_value, transformed_value) — validation failure","All values null for the selected field — histogram may be empty or error","Payment not completed — 402 response, endpoint not executed"],"whenToPreferThis":"Choose this endpoint when you need offline/local statistical analysis of numeric precision differences between a source and a transformed column, particularly when you want both a normalization summary and a histogram in a single low-cost call. Ideal for ETL auditing, data quality checks, and ML feature validation pipelines where no external data enrichment or prediction is needed.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:25.459Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-string-normalisation-equal-width-c58d3ab3"}