# Test-Driven Data Analysis (TDDA)

> Python library for constraint discovery and verification, reference
> testing, and regular expression inference.

## Summary

TDDA provides four primary tools:

1. **tdda.constraints**: Discover data constraints from existing datasets
   and verify new data against them.
2. **tdda.referencetest**: Semantic regression testing for text, DataFrames
   (Pandas/Polars), and binary files.
3. **tdda.serial**: Metadata-guided reading and writing of CSV and flat files,
   with companion `.serial` metadata files that record format details
   (types, nulls, dates, separators) for accurate round-tripping.
4. **tdda.rexpy**: Automated regular expression inference from data samples.

## Documentation

- [TDDA Documentation](https://tdda.readthedocs.io)
- [CLI / Man Pages](https://github.com/tdda/tdda/blob/master/doc/source/cli.md) (also as [HTML](https://tdda.readthedocs.io/en/latest/cli.html))
- [Source Code (GitHub)](https://github.com/tdda/tdda)
- [Package (PyPI)](https://pypi.org/project/tdda/)

## Book

- [Test-Driven Data Analysis](https://www.routledge.com/Test-Driven-Data-Analysis/Radcliffe/p/book/9781032897158) (Radcliffe, CRC Press, 2026)
- [Book resources](https://book.tdda.info)

## Guides for LLMs and Coding Agents

- [Reference Testing with TDDA: A Guide for LLMs and Coding Bots](https://tdda.info/md/076-reference-testing-with-tdda-a-guide-for-llms-and-coding-bots.md)
- [CSV Metadata and tdda.serial: A Guide for LLMs and Coding Agents](https://tdda.info/md/077-csv-metadata-and-tdda-serial-a-guide-for-llms.md)
- [Data Validation with tdda Constraints: A Guide for LLMs and Coding Agents](https://tdda.info/md/078-data-validation-with-tdda-constraints-a-guide-for-llms.md)