Which item is in the data quality checklist?

While Boolean columns can be an important part of a dataset, the number of these columns is not typically a primary focus in a data quality checklist. Boolean data types mainly represent true/false conditions, and their quantity does not directly influence the overall quality of the dataset in the same way that categories do.

Similar to Boolean columns, the number of date columns may be relevant for specific analyses but is not generally included in a standard data quality checklist. The presence of date columns is more about the data’s temporal aspects rather than its overall quality or categorization.

Though numerical columns are important for quantitative analysis, the mere count of these columns does not address the broader aspects of data quality that a checklist aims to evaluate. The focus should be on how well the numerical data supports the analysis rather than just the number of columns present.