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.

Refactor salary data processing in load_file.py: remove try-except block for salary conversion, ensuring consistent handling of salary values while maintaining data integrity during file uploads.

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.

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

Add debug print statements in load_dk_fd_file.py: include additional print statements for clean_name and lineups.name to assist in debugging and data verification during file processing.

Refactor lineups file handling in load_dk_fd_file.py: clean up line name by removing numeric suffixes using regex, and streamline CSV file loading by removing UTF-8 encoding specification for improved compatibility.

Enhance CSV file loading in load_dk_fd_file.py: update the read_csv function to include UTF-8 encoding for better compatibility with file objects provided by Streamlit.

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 statements in load_dk_fd_file.py: include print statements for name_dict and lineups_df to assist in debugging and data verification during file processing.

Add debug print statements in load_dk_fd_file.py: include print statements for lineups_df and export_df to assist in debugging and data verification during file processing.

Enhance error handling in lineups DataFrame processing in load_dk_fd_file.py: add a try-except block around the column drop operation to prevent failures when attempting to remove specified columns, ensuring smoother data processing.

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.

Remove unnecessary columns from lineups DataFrame in load_dk_fd_file.py: drop 'Entry ID', 'Contest Name', 'Contest ID', and 'Entry Fee' to streamline data processing and enhance analysis efficiency.

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.

Update weighted ownership calculation in predict_dupes.py: adjust the multiplication factor for ownership values and change the return value to a larger scale, enhancing the accuracy of percentage representation in ownership metrics.

Refactor calculate_weighted_ownership function in predict_dupes.py: remove debug print statements, adjust ownership value calculations to handle percentages, and ensure the return value is in percentage form, improving clarity and accuracy of ownership metrics.

Add debug statements in calculate_weighted_ownership function: include print statements to trace input values and intermediate calculations, aiding in the debugging process for ownership metrics.

Add weighted ownership calculation in predict_dupes.py: implement a new function to calculate weighted ownership values, enhancing the accuracy of ownership metrics in portfolio analysis.

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.

Update ownership nerf logic in predict_dupes.py: implement sport-specific adjustments for CS2, allowing for differentiated ownership ratio calculations based on sport type, enhancing accuracy in duplication predictions.

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.

Enhance ownership calculations in predict_dupes.py: add support for an additional FLEX5 ownership column, improving the accuracy of duplication predictions for CS2 portfolios and ensuring comprehensive analysis of player ownership metrics.

Refactor ownership calculations in predict_dupes.py: remove redundant ownership columns for CS2, streamlining the duplication prediction logic and enhancing performance in 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.

Remove export dictionary display in app.py: eliminate the output of the export dictionary contents to streamline the user interface and focus on essential information during portfolio processing.

Enhance export file processing in app.py: implement mapping for non-excluded columns in the export file using the export dictionary, ensuring improved data accuracy during portfolio downloads.

Enhance export file processing in app.py: add mapping for non-excluded columns in the export file using the export dictionary, improving data accuracy during portfolio downloads.