fix: include bool dtype in categorical columns for TabularDescriptionTables (ZD #609)

[hunner]

Pull Request Description

What and why?

TabularDescriptionTables.py classifies columns by pandas dtype via select_dtypes(). The three helpers cover int/float/uint8 (numerical), object/category (categorical), and datetime, but bool is absent absent. A column with dtype=bool falls through every check and is never included.

How to test

Supply a bool dataframe to the dataset.

What needs special review?

None

Dependencies, breaking changes, and deployment notes

None

Release notes

• Fix: include bool data fields in categorical table results

Checklist

• What and why
• Screenshots or videos (Frontend)
• How to test
• What needs special review
• Dependencies, breaking changes, and deployment notes
• Labels applied
• PR linked to Shortcut
• Unit tests added (Backend)
• Tested locally
• Documentation updated (if required)
• Environment variable additions/changes documented (if required)

[github-actions]

PR Summary

This pull request extends the functionality of the TabularDescriptionTables module by modifying the way categorical columns are identified. Previously, only columns with data types 'object' and 'category' were included. The change introduces boolean (bool) columns as part of the categorical data types, ensuring better representation of datasets that include flag-like or binary data.

Additionally, a unit test has been added in the tests suite to verify that boolean columns (such as 'flag' and 'active' in the example) are appropriately identified and appear in the categorical table. This ensures that function behavior aligns with the intended inclusion of boolean types in categorical analyses.

Test Suggestions

• Test with various input DataFrames that include combinations of boolean, numerical, and categorical columns to verify accurate identification.
• Include tests where DataFrames consist solely of boolean columns to validate that the categorical table is populated correctly.
• Introduce tests for edge cases such as DataFrames with null values in boolean columns to detect potential unexpected behavior.

[juanmleng]

Awesome. Thanks @hunner!