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.

Fix calculation of current ownership type maximum in trim_portfolio.py to use the correct row's own type value, improving accuracy in portfolio trimming logic.

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.

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.