Optimize cleaning providers for faster type fixes

[LinklyLuck]

Summary

• refactor the type-based cleaning provider to use vectorized Polars expressions for numeric, date, and boolean normalization
• skip unnecessary work for empty or already-typed columns while keeping detailed change reports intact
• ensure the cleaning helpers expose clean returns with newline-terminated modules

Testing

• python -m compileall cleaning_providers/type_cleaner.py cleaning_providers/init.py

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https://chatgpt.com/codex/tasks/task_e_6902641b0434832995bfa7ad87b3d6f9

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Boolean cleaner leaves column as strings

The refactored _clean_boolean_column builds a with_columns expression that mixes Boolean literals with the original string values but never casts the result. In Polars, combining Boolean and string branches produces a UTF-8 column, so the cleaned dataframe still contains strings like "true"/"false" rather than True/False, despite the report claiming type fixes. Downstream code that expects an actual Boolean dtype will continue to see strings, meaning the type-cleaning stage no longer enforces boolean typing. Consider casting the final expression to pl.Boolean (or casting the original branch) so the column becomes a real boolean series.

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