synthdata-compute

You can aggregate and transform CSV or JSON datasets by loading input tables into a map and executing a custom Python compute script to generate derived tables like monthly rollups. Results are written to an output file in CSV, JSON, or xlsx formats.

Yes, you can use pandas and numpy to compute composite scores and percentile ranks from xlsx files. The compute script operates on pre-loaded tables from input formats such as xlsx, csv, or json to derive scored tables for downstream analytics.

The best way to generate segment summaries is to provide your input datasets and a Python computation script that operates on the pre-loaded tables map. The script executes your aggregation logic and writes the derived segment summaries to your chosen output format.

Yes, you need to write a minimal Python compute script to derive transformed tables from existing data. The script operates on pre-loaded tables from your input files, allowing you to apply custom aggregation, scoring, or transformation logic before writing the output.

Yes, you can append derived analytics tables to an existing output file using the optional append mode. After executing your Python compute script on the input datasets, the results are written to xlsx, json, or csv formats with append support.

Yes, the approach supports validating results and inspecting schemas before writing output. After executing your Python computation script on the pre-loaded tables, you can validate the derived tables and inspect their schemas before generating the final output file.