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Enhanced Artemis Table Conversion Script for Readability, Performance, and Scalability #142

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Instead, use pathlib to replace manual file operations for cleaner and more intuitive path management.
Included comments and docstrings to enhance code readability.
The logic has been described in more self-explaining variable names.
Better Performance:

It uses str.contains() for filtering; the logic is simpler and runtime is faster.
It avoids repeated calculations by caching filtered data.
Error Handling and Robustness

Adding.dropna() to deal with missing or invalid data elegantly.
Streamlined the logic in handling subsections to ensure smooth transitions and avoid possible mistakes.

Code Scalability
Modularized key operations to make them reusable and scalable for bigger projects.

Why These Changes Are Needed:
This will make it easier to maintain code better for later changes.
Better runtime performance for large data-sets.
Minimize bugs that might be generated from wrong data or incorrect logical errors.

Testing and Validation:
The script was tested on sample datasets for which everything was assumed to work out fine. The output should match the expected format, thus staying compatible with downstream processes. Request for Review Please review the changes and let me know if further adjustments are needed. Much appreciated feedback to ensure it meets all standards and requirements.

This optimized code makes it more readable and efficient by using pathlib for file operations, caching filtered data to avoid redundant computations, and simplifying the filtering logic with.str.contains()

Missing data issues are handled using.dropna(), and the rich library is retained to provide clarity in debugging. These changes make the code scalable, error-resistant, and Pythonic best practices.
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