Update app.py to utilize session state for trimming dictionary max values: enhance state management by ensuring consistent access to ownership type thresholds during portfolio updates.

Update trimming dictionary initialization in app.py: ensure consistent handling of maximum values for ownership types during portfolio updates, improving state management and preventing data conflicts.

Remove trimming_dict_maxes from session state before reinitializing in app.py: ensure clean state management during portfolio updates and prevent potential data conflicts.

Refactor trimming dictionary handling in app.py: initialize trimming dictionary max values in session state for improved state management and ensure accurate threshold inputs during portfolio updates.

Update trimming dictionary max values in app.py: adjust ownership type thresholds to improve filtering accuracy and maintain consistency in portfolio management.

Add trimming dictionary max values in app.py: define maximum thresholds for ownership types to enhance filtering options and maintain consistency in portfolio management.

Reintroduce trimming dictionary in app.py: move the calculation of maximum values for ownership types back into the submission logic, ensuring accurate threshold inputs during portfolio updates and maintaining consistency in the trimming process.

Enhance trimming functionality in app.py: introduce a trimming dictionary to streamline the retrieval of maximum values for performance and ownership types, improving the accuracy of threshold inputs in the portfolio management process.

Refactor filter options in app.py: streamline the organization of macro and micro filter inputs into expandable sections, enhance player locking and removal functionalities, and ensure consistent handling of trimming parameters for improved user experience during portfolio management.

Refactor filter options in app.py: reorganize macro and micro filter inputs into expandable sections for improved user experience, and ensure consistent handling of player locking and removal functionalities during portfolio management.

Update input fields in app.py: add unique keys to selectboxes and number inputs for sorting and trimming variables, enhancing state management and user experience in the portfolio trimming process.

Refactor trimming options in app.py: enhance user interface by organizing input fields into columns for better clarity and usability, and update threshold inputs to allow for minimum and maximum values, improving the portfolio trimming process.

Add trim slack parameter to portfolio trimming functionality in app.py and trim_portfolio.py, allowing for more flexible threshold adjustments during portfolio management.

Update app.py and trim_portfolio.py to change threshold parameters from integers to floats, enhancing precision in portfolio trimming functionality and ensuring consistent data handling.

Enhance portfolio trimming functionality in app.py and trim_portfolio.py: add performance and own thresholds for trimming criteria, improving data filtering capabilities and allowing for more precise portfolio management.

Refactor salary processing in app.py: update salary conversion to drop NaN values before converting to integers, ensuring cleaner data handling and enhancing the robustness of projections.

Refactor salary processing in app.py: streamline salary conversion by removing unwanted characters before converting to integers, enhancing clarity and ensuring consistent data handling during projections file loading.

Enhance salary processing in app.py: add error handling during salary conversion to integers, ensuring robustness in data processing while maintaining clarity in projections file loading.

Refactor salary processing in app.py: improve clarity by separating character replacements for salary values into distinct operations, ensuring consistent formatting during projections file loading.

Refactor salary processing in app.py: move salary character replacement to app.py for improved clarity and maintainability, ensuring consistent handling of salary values during projections file loading.

Update app.py and load_file.py: add regex import in app.py for future enhancements and replace print with write in load_file.py for improved output handling.

Refactor file type checks in load_file.py and load_ss_file.py: streamline file extension validation using endswith method for improved readability. Update app.py to handle salary conversion more robustly by removing unnecessary exception handling.

Refactor salary data processing in app.py: streamline salary conversion by consolidating character removal into a single regex operation, enhancing data integrity and consistency during projections file handling.

Enhance salary data processing in app.py: add a try-except block to convert salary values to integers after removing unwanted characters, improving data integrity during projections file handling.

Refactor file type checks in loading functions: update conditions in load_dk_fd_file.py, load_file.py, and load_ss_file.py to use substring checks for file extensions, improving readability and maintainability. Remove debug print statements from load_dk_fd_file.py and app.py to clean up the code.

Add debug print statement for export portfolio in app.py: include a print statement to output the export portfolio for debugging purposes during file uploads, aiding in troubleshooting and data verification.

Update score cutoff in portfolio matching logic in app.py: increase the score cutoff from 85 to 90 to enhance the accuracy of portfolio name matches.

Update salary input limits in app.py: increase the maximum acceptable salary from 60,000 to 100,000 to accommodate a wider range of salary options for users. Adjust conditional checks in predict_dupes.py to use 'elif' for improved clarity in type_var evaluations.

Improve error handling in projections_df processing in app.py: add try-except blocks for ownership percentage conversion and salary formatting to maintain data integrity during potential conversion failures.

Update projections_df processing in app.py: add ownership percentage conversion and ensure salary formatting for improved data integrity and analysis accuracy.

Add support for DraftKings/Fanduel file uploads in app.py: implement a new upload option for processing lineups, integrating a dedicated loading function for enhanced compatibility with different data sources.

Comment out duplicate removal in app.py: temporarily disable the line that drops duplicate entries based on 'Name' in the session state CSV file, preserving original data for further analysis.

Refactor showdown mapping logic in app.py: consolidate NFL and non-NFL mapping into a single dictionary assignment, improving code readability and reducing redundancy in player data handling.

Update excluded columns in app.py: add 'Weighted Own' and 'Geomean' to the list of excluded columns in projections, enhancing data filtering for improved analysis.

Enhance match output in app.py: update the display of matched portfolio names to include matching scores, improving user feedback and providing more detailed insights into the matching process.

Add match confirmation output in app.py: include a statement to display matched portfolio names, enhancing user feedback and improving traceability of the matching process.

Add unmatched names warning in app.py: implement logic to identify and display portfolio names without matches in projections, enhancing user feedback and improving data validation during the matching process.

Add portfolio name extraction and matching in app.py: implement a new function to retrieve unique player names from the portfolio, enhance name matching logic, and update session state with matched names for improved data consistency and analysis.

Remove redundant update of projections_df in app.py: eliminate unnecessary reassignment of projections_df with name mismatches, streamlining the session state management and improving code efficiency.

Enhance name matching process in app.py: streamline the handling of player names by implementing a more efficient matching algorithm, updating session state management, and improving debug output for better traceability of matches.

Refactor name standardization and mapping logic in app.py: introduce functions for creating site mappings and standardizing player names, improving code organization and efficiency in handling name variations across dataframes.

Refactor name matching logic in app.py: streamline the process of creating the name ID mapping and matching player names, improving efficiency and clarity in data handling.

Refactor debug output in app.py: adjust the placement of the export dictionary display statement to ensure it appears after applying matches, improving clarity in data processing verification.

Add debug output in app.py: include a statement to display the export dictionary after applying matches, aiding in the verification of data processing.

Refactor filter form keys in app.py: rename filter form keys to 'macro_filter_form' and 'micro_filter_form' for improved clarity and organization in the filtering options.

Refactor filter options in app.py: rename 'Filter Options' to 'Macro Filter Options' and streamline the filter form by consolidating stack selection logic, enhancing user experience and clarity in filtering functionalities.

Enhance portfolio calculations in app.py: implement sport-specific logic for CS2 to differentiate salary, median, and ownership calculations for Captain and FLEX positions, improving accuracy in player statistics and overall portfolio analysis.

Update sport selection in app.py: add 'CS2' to the sport selection dropdown, expanding user options for game type selection.

Update portfolio duplication prediction in app.py: modify predict_dupes call to include sport_var, enhancing the accuracy of duplication metrics based on sport-specific logic.

Refactor portfolio duplication prediction in predict_dupes.py: enhance the function to accommodate sport-specific logic for CS2, improving ownership calculations and ensuring accurate duplication metrics based on player ownership and salary. Update app.py to pass sport_var to predict_dupes for better context in calculations.