{"uid":"cap_cGEW7Qq680CFwopmnsQQU","slug":"duplicate-observations-string-normalisation-category-frequencies-0bda7bfe","name":"Duplicate Observations — String Normalisation + Category Frequencies","description":"Duplicate observations — String normalisation + Category frequencies: String normalisation + Category frequencies for supplied duplicate observations records (duplicate_rows, total_rows). 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-04-024?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","duplicate_rows","total_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"total_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"duplicate_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","total_rows":1,"dataset_group":"group-a","duplicate_rows":10},{"date":"2026-10-02","row_id":"row-2","total_rows":2,"dataset_group":"group-b","duplicate_rows":20},{"date":"2026-10-03","row_id":"row-3","total_rows":3,"dataset_group":"group-a","duplicate_rows":15},{"date":"2026-10-04","row_id":"row-4","total_rows":4,"dataset_group":"group-b","duplicate_rows":30}],"maxItems":200,"minItems":1}}},"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_XvU93kIx57Emr4FXixMDP","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 duplicate observation records to produce two reports: string normalisation of text fields and category frequency distributions for dataset groups.","exampleAgentPrompt":"I have 4 duplicate observation records — can you run string normalization and category frequency analysis on them? Rows are: row-1 on 2026-10-01 in group-a with 10 duplicates out of 1 total, row-2 on 2026-10-02 in group-b with 20 duplicates out of 2 total, row-3 on 2026-10-03 in group-a with 15 duplicates out of 3 total, and row-4 on 2026-10-04 in group-b with 30 duplicates out of 4 total.","exampleUseCases":[{"title":"Data quality audit by group","prompt":"I have a batch of 50 duplicate observation records across groups 'sales', 'marketing', and 'ops' with dates and row counts — can you normalize the group name strings and give me a frequency breakdown of how many duplicates fall into each category?"},{"title":"ETL pipeline duplicate summary","prompt":"After our nightly ETL run I collected duplicate row counts for each dataset group — here are 120 records with row IDs, dates, duplicate_rows, and total_rows. Please normalize the text fields and compute category frequencies so I can see which groups are producing the most duplicates."},{"title":"Research dataset deduplication report","prompt":"I'm cleaning a research dataset and have logged duplicate row observations for 80 entries across several dataset groups. Can you run the string normalisation and category frequency reports on these records so I can understand the distribution of duplicates by group?"}],"resultDescription":"Two analytical reports for the supplied records: (1) a string normalisation report that standardizes text fields such as dataset_group names, and (2) a category frequency report showing the distribution and counts of duplicate observations across dataset groups, along with duplicate rate metrics derived from duplicate_rows and total_rows fields.","failureModes":["Fewer than 1 row submitted — minItems constraint violated","More than 200 rows submitted — maxItems constraint exceeded","Invalid date format (not YYYY-MM-DD) causes schema validation failure","Missing required fields (row_id, dataset_group, date, duplicate_rows, total_rows) causes rejection","Non-numeric values for duplicate_rows or total_rows where numbers are expected","Values outside allowed numeric range (±1,000,000,000,000) rejected","Payment not included or insufficient — x402 payment required at $0.01 USDC per call"],"whenToPreferThis":"Choose this endpoint when you have structured duplicate observation data (with row IDs, dates, group labels, duplicate counts, and total counts) and need both text normalization of categorical fields and frequency distribution analysis in a single call. It is ideal for data quality workflows, ETL monitoring, and research deduplication audits where batch processing of up to 200 records is needed without any external lookups or live data fetching.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T07:19:32.517Z","isFirstParty":false,"canonicalSlug":"duplicate-observations-string-normalisation-category-frequencies-0bda7bfe"}