Optimize Memory Usage and Performance in Python Code

[Jlowlansky]

This pull request introduces several optimizations to reduce memory usage and improve performance in the Python codebase. The changes focus on enhancing efficiency while adhering to best practices. Key improvements include:

Memory Allocation Reductions:

• Replaced list operations with set operations for faster uniqueness checks.
• Switched from list comprehensions to generator expressions to minimize memory overhead.
• Optimized string parsing to reduce the creation of temporary objects.
• Improved dictionary operations using setdefault() for more efficient key handling.

Performance Optimizations:

• Enhanced string handling for better efficiency.
• Reduced the creation of temporary objects throughout the code.
• Optimized collection operations for improved runtime performance.

Best Practices:

• Improved memory cleanup to prevent unnecessary resource retention.
• Adopted more efficient data structures tailored to the use case.
• Minimized object copying to streamline operations.