My new book, Test-Driven Data Analysis is now available from all good booksellers and all sellers of good books.
Get 20% at the publisher’s website with code 26SMA1 (until 30 June 2026).
Publisher site: https://www.routledge.com/Test-Driven-Data-Analysis/Radcliffe/p/book/9781032897158
TDDA library (Python) including command-line tools:
PyPI (python -m pip install -U tdda): https://pypi.org/project/tdda/
Source: https://github.com/tdda/tdda.git
Online docs: https://tdda.readthedocs.io/
Book resources: https://book.tdda.info
Blog: https://tdda.info
Test-Driven Data Analysis is a methodology and set of tools and a library for improving quality of data and analytical data processes and pipelines.
• Data Validation with constraints including inference of constraints from data (Parquet, Pandas, Polars, Postgres, MySQL, SQLite, and flat files (.csv etc).
• Reference Testing: extensions to Python unittest and pytest for semantic testing of complex outputs
• Visual diff for data frames serialised as parquet or as flat files (e.g. csv)
• tdda.serial: metadata for describing flat-file formats and tools for using and converting between tdda.serial, CSVW (CSV on the Web), and Frictionless.
• Automatic test generation for (non-GUI) programs in any language: Gentest writes tests so you don’t have to.™
• Unicode text utilities including glyph counting and extended kompatibility normalization (Normal Form TK), which extends normal forms TKC and TKD.
• Support for Pandas 3.0 (original, numpy_nullable, and pyarrow backends) and for Polars (most part of library; constraints to follow).
