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In today's world, where vast amounts of data are generated and collected daily, and where data heavily influence business, political, and societal decisions, it is crucial to evaluate the quality of the data used for analysis, decision-making, and reporting. This involves understanding how reliable and trustworthy the data are. To address this need, we have created `cuallee`, a Python package for assessing data quality. `cuallee` is designed to be dataframe-agnostic, offering an intuitive and user-friendly API for describing checks across the most popular dataframe implementations such as PySpark, Pandas, Snowpark, Polars, DuckDB and BigQuery. Currently, `cuallee` offers over 50 checks to help users evaluate the quality of their data.
In today's world, where vast amounts of data are generated and collected daily, and where data heavily influence business, political, and societal decisions, it is crucial to evaluate the quality of the data used for analysis, decision-making, and reporting. This involves understanding how reliable and trustworthy the data are. To address this need, we have created `cuallee`, a Python package for assessing data quality. `cuallee` is designed to be dataframe-agnostic, offering an intuitive and user-friendly API for describing checks across the most popular dataframe implementations such as PySpark, Pandas, Snowpark, Polars, DuckDB, and BigQuery. Currently, `cuallee` offers over 50 checks to help users evaluate the quality of their data.
