In previous Radars, we've featured data validation and testing platforms like Great Expectations that can be used to validate assumptions and test the quality of incoming data used for training or classification. Sometimes, though, all you need is a simple code library to implement tests and quality checks directly in pipelines. pandera is a Python library for testing and validating data across a wide range of frame types such as pandas, Dask or PySpark. pandera can implement simple assertions about fields or hypothesis tests based on statistical models. The wide range of supported frame libraries means tests can be written once and then applied to a variety of underlying data formats. pandera can also be used to generate synthetic data to test ML models.
