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Trixie Lasagne Tabular Data Pipeline
x402
Trixie Lasagne Tabular Data Pipeline is a paid API for AI agents from trixielasagne-api.purplewater-b02e2643.eastus.azurecontainerapps.io, paid per call via x402, $0.01/call, status unknown (last checked 2026-09-16).
Runs deterministic tabular data transformations including select, rename, filter, sort, limit, group, aggregate, deduplicate, join, pivot, unpivot, and calculated fields over caller-supplied data
Price / call$0.01
Success—
StatusUnknown
How this service works
Deterministic tabular data pipeline: select/rename/filter/sort/limit/group/aggregate/dedupe/join/pivot/unpivot/calculated_field
Output
A transformed tabular dataset returned as structured data reflecting the applied operations (filters, aggregations, joins, pivots, etc.), with columns and rows matching the requested output shape
Example use cases
Monthly sales report by region
“I have a table of order transactions with columns order_id, region, product, amount, and date. Filter to last month's rows, group by region, sum the amount, and sort descending so I can see which regions sold the most.”
Joining customer and order tables
“I've got two datasets — one with customer records keyed on customer_id, and another with orders keyed on customer_id. Join them on that key, then select just the customer name, email, order date, and order total columns.”
Pivot survey responses wide to column format
“I have a long-format survey results table with columns respondent_id, question, and answer. Pivot it so each question becomes its own column and each row is one respondent.”
Technical details
Endpoint
POSThttps://trixielasagne-api.purplewater-b02e2643.eastus.azurecontainerapps.io/data/processWhen to prefer
Choose this endpoint when you need deterministic, reproducible tabular data transformations without spinning up a database or data warehouse. It is especially useful for chaining multiple operations (filter + group + aggregate + sort) in a single API call, or when you need to reshape data (pivot/unpivot) or merge datasets (join) programmatically inside an agent workflow. Prefer it over statistical analysis siblings when the goal is data shaping rather than descriptive statistics or technical indicators.
Side effects
- Missing or malformed 'input' body causes a 400 validation error
- Referencing a column name that doesn't exist in the dataset returns an error indicating unknown field
- Invalid operation type or unsupported combination of operations may return a 422 unprocessable entity
- Join on keys not present in both datasets fails with a key mismatch error
- Payload too large for the service limits may result in a timeout or 413 error
- Malformed JSON body returns a parse error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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