correct handling of categorical in and outliers and typing correction

[JHogenboom]

Resolve categorical inliers handling and consequential changes in typing hints

[Copilot]

Pull Request Overview

This pull request corrects handling of categorical data type detection and resolves typing inconsistencies throughout the codebase. The changes focus on improving robustness when working with pandas categorical data and ensuring proper type handling.

• Replaces hasattr(dtype, "categories") checks with explicit isinstance(dtype, pd.CategoricalDtype) checks
• Fixes categorical data processing by removing automatic category constraint application
• Corrects numpy array handling for ExtensionArrays and improves type safety

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

File	Description
src/vantage6_strongaya_general/privacy_measures.py	Updates categorical dtype detection and fixes typing for datetime bins
src/vantage6_strongaya_general/miscellaneous.py	Corrects string dtype and removes problematic categorical inliers handling
src/vantage6_strongaya_general/general_statistics.py	Adds numpy array conversion for ExtensionArrays and improves variable handling

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[Copilot]

The logic for categorical inliers/outliers filtering is incorrect. For categorical data, you should filter based on the values themselves, not the index. This should be column_values.isin(inliers) and ~column_values.isin(inliers) respectively.

Suggested change

	inliers_series = column_values[column_values.index.isin(inliers)]
	outliers_series = column_values[~column_values.index.isin(inliers)]
	inliers_series = column_values[column_values.isin(inliers)]
	outliers_series = column_values[~column_values.isin(inliers)]

[JHogenboom]

This is incorrect, we should use the index as value counts are passed to this function