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24K Labs CSV Profile
x402
24K Labs CSV Profile is a paid API for AI agents from api.24klabs.ai, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-14).
Computes column-level statistics for a CSV file including inferred type, null count, distinct count, min/max/mean for numeric columns, and sample values.
Price / call$0.005
Success—
StatusUnknown
How this service works
Column-level statistics for a CSV: inferred type, count, null count, distinct count, min/max/mean for numeric columns, and sample values. One-call data audit.
Output
Returns per-column statistics including inferred data type (string, integer, float, boolean, date, etc.), total count, null count, distinct count, and for numeric columns: min, max, and mean values, plus a list of sample values drawn from the column.
Example use cases
Pre-pipeline data quality check
“Before I load this CSV into our database, can you profile each column — I want to know the inferred types, how many nulls there are, distinct counts, and the min/max for any numeric fields?”
Exploratory analysis of uploaded dataset
“I just got this CSV from a vendor and have no idea what's in it. Can you run a column-level audit on it and show me the type, null rate, distinct values, and some sample values for each column?”
Detecting anomalies before ML training
“I need to audit my training CSV before feeding it into a model — can you check each column for inferred type, null counts, distinct counts, and the min/max/mean of numeric columns so I can spot any obvious data issues?”
Technical details
Endpoint
POSThttps://api.24klabs.ai/api/v1/csv-profileWhen to prefer
Use this endpoint when you need a fast, one-call summary of a CSV file's structure and data quality — especially before ETL pipelines, ML training, or data validation workflows. It's ideal when you want column-level profiling without standing up a full data warehouse or pandas environment. Prefer this over manual inspection or generic code execution when you need structured, machine-readable statistics quickly.
Side effects
- Malformed or unparseable CSV returns an error
- Empty CSV or CSV with no rows returns minimal stats
- Very large CSV may time out or exceed payload limits
- Columns with all-null values may have limited statistics
- Ambiguous types (e.g. mixed numeric/string) may result in 'string' inference
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| csv | string |
Output
Schema · Response
{
"type": "json",
"example": {
"rows": 3,
"columns": [
{
"name": "price",
"type": "number"
}
]
}
}Tags
x402
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