{"uid":"cap_2aLqBtNhLSWnQMkDhho6m","slug":"vextorium-csv-stats-profiler-437d97a7","name":"Vextorium CSV Stats Profiler","description":"Profile a CSV (delimiter auto-detected): rows, columns, type of each column, nulls, unique values, min, max, mean, median, std, sum, top values, duplicate rows and a sample. Understand a dataset before using it.","url":"https://api.vextorium.com/v1/csv-stats?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"required":["content"],"properties":{"content":{"type":"string","description":"CSV text (up to 5 MB)"},"delimiter":{"type":"string"}}},"type":{"type":"string","const":"http"},"method":{"enum":["POST"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":["object","null"],"properties":{"rows":{"type":["number","null"]},"sample":{"type":["array","null"],"items":{"type":["null","object"],"properties":{"city":{"type":["null","string"]},"month":{"type":["null","number"]},"sales":{"type":["null","number"]}}}},"columns":{"type":["number","null"]},"profile":{"type":["array","null"],"items":{"type":["null","object"],"properties":{"avg":{"type":["number","null"]},"max":{"type":["number","null"]},"min":{"type":["number","null"]},"sum":{"type":["number","null"]},"type":{"type":["null","string"]},"empty":{"type":["null","number"]},"column":{"type":["null","string"]},"median":{"type":["number","null"]},"unique":{"type":["null","number"]},"deviation":{"type":["number","null"]},"avg_length":{"type":["number","null"]},"most_frequent":{"type":["array","null"],"items":{"type":["null","object"],"properties":{"times":{"type":["null","number"]},"value":{"type":["null","number","string"]}}}}}}},"delimiter":{"type":["string","null"]},"duplicate_rows":{"type":["number","null"]}}}}}}},"responseSchema":{"type":"json","example":{"rows":3,"sample":[{"city":"Madrid","month":1,"sales":100},{"city":"Barcelona","month":1,"sales":80}],"columns":3,"profile":[{"type":"str","empty":0,"column":"city","unique":2,"avg_length":7,"most_frequent":[{"times":2,"value":"Madrid"},{"times":1,"value":"Barcelona"}]},{"avg":100,"max":120,"min":80,"sum":300,"type":"int64","empty":0,"column":"sales","median":100,"unique":3,"deviation":20,"most_frequent":[{"times":1,"value":100},{"times":1,"value":80}]}],"delimiter":",","duplicate_rows":0}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.004","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.004/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.004","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.004","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_el65dxX4u8AwqDiOF68S8","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.004","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Accepts CSV text and returns statistical profiling of each column: row/column counts, data types, min/max/avg/median/sum/stddev, unique values, most frequent values, empty counts, and duplicate row detection.","exampleAgentPrompt":"Can you profile this CSV data for me and give me column statistics — things like min, max, average, median, unique counts, and most frequent values? Here's the CSV text: city,month,sales\\nMadrid,1,100\\nBarcelona,1,80\\nMadrid,2,120","exampleUseCases":[{"title":"Pre-processing data quality check","prompt":"Before I run my pipeline on this CSV, can you analyze it and tell me how many rows and columns it has, whether there are any duplicate rows, and how many empty values are in each column? Here's the CSV content: [csv text]"},{"title":"Sales data summary by column","prompt":"I have a CSV with sales figures — can you compute the min, max, average, median, and sum for each numeric column, and tell me the most frequent values for text columns? The CSV is: product,region,revenue\\nWidgetA,North,500\\nWidgetB,South,300\\nWidgetA,North,500"},{"title":"Exploratory dataset profiling for unknown CSV","prompt":"I just received a CSV file and I'm not sure what's in it — can you figure out the delimiter, detect the column types, and give me a full statistical profile of each column including unique counts and top frequent values? Here's the raw CSV text."}],"resultDescription":"A JSON object containing: total row count, column count, detected delimiter, a sample of the first rows, a duplicate row count, and a per-column profile array. Each column profile includes: column name, inferred data type (str, int64, etc.), count of empty values, count of unique values, average string length (for text), and for numeric columns: min, max, sum, average, median, and standard deviation. Most frequent values with occurrence counts are also included for each column.","failureModes":["CSV text exceeds 5 MB limit — request rejected","Malformed or unparseable CSV content — parsing error returned","Missing required 'content' field in body — validation error","Ambiguous delimiter not correctly detected — incorrect column parsing","All-empty columns or fully blank CSV — partial or empty profile returned","Network timeout for very large CSVs near the size limit"],"whenToPreferThis":"Choose this endpoint when you need rapid, per-column statistical profiling of CSV data without setting up a local data processing environment. It is ideal for data quality checks before ETL pipelines, exploratory data analysis on unknown datasets, or when you need a quick summary of numeric distributions and categorical frequencies in tabular data. It handles delimiter auto-detection and type inference, saving preprocessing steps. Prefer this over general-purpose code execution endpoints when you want a structured JSON profile without writing custom analysis code.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-11T18:49:26.665Z","isFirstParty":false,"canonicalSlug":"vextorium-csv-stats-profiler-437d97a7"}