Testing data quality with cuallee

There are many data quality and validation tools out there for data workflows in Python. One of the most popular options is Great Expectations. There is another popular solution called deequ. cuallee was born to improve deequ by making the API more user-friendly and cost-efficient. It is a very lightweight library that helps you conduct data quality checks quickly in your workflows. cuallee’s API is clean and easy to understand, so you can get started with data quality testing using cuallee without facing many technical hurdles.

In this recipe, we’ll cover how we can add data quality tests to your data transformation process in Polars.

Getting ready

Install cuallee in your terminal with the following code:

pip install cuallee

How to do it...

Here’s how to conduct data quality checks with cuallee.

1. cuallee only takes a DataFrame as its input, so let’s convert our LazyFrame to a DataFrame:

   df = lf.collect...