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: 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.

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.

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.

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.

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.

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.

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 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.

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.

Fix typo in geometric mean calculation in predict_dupes: rename 'Goemean' to 'Geomean' for consistency and clarity in portfolio analysis.

Add geometric mean calculation in predict_dupes: introduce a new column 'Goemean' to compute the geometric mean of ownership percentages, enhancing portfolio analysis capabilities.

Enhance trimming options in app.py: add informational prompt for filtering before trimming and update portfolio copy logic to ensure correct data handling during download, improving user guidance and functionality.

Update 'Weighted Own' calculation in predict_dupes.py: adjust formula to account for the difference between maximum and minimum ownership values for improved accuracy in portfolio analysis.

Enhance portfolio reset functionality in app.py: implement comprehensive filtering options for salary, finish percentile, and player selections, and update predict_dupes function to calculate 'Weighted Own' for improved accuracy in portfolio management.

Enhance portfolio trimming logic in app.py: add comprehensive filtering options based on salary, finish percentile, and player selections, and update predict_dupes function to adjust 'Product Own' calculation for improved accuracy.

Remove unused import of gmean from app.py and predict_dupes.py for cleaner code and improved performance.

Update trimming options in app.py: replace 'Geomean' with 'Product Own' for portfolio calculations, and ensure trimming logic is executed only upon form submission for improved user experience.

Enhance portfolio filtering and trimming options in app.py: introduce expandable sections for filter and trimming options, update portfolio trimming logic to support geomean calculations, and improve export functionality with stack count adjustments.

Fix index access and row dropping logic in trim_portfolio function in global_func/trim_portfolio.py for improved accuracy and performance.

Refactor trim_portfolio function in global_func/trim_portfolio.py: improve index handling and streamline row dropping logic for better performance.

Refactor trim_portfolio function in global_func/trim_portfolio.py: optimize iteration logic for filtering portfolios based on ownership type.

Add 'Trim Portfolio' button in app.py and implement trim_portfolio function in new global_func/trim_portfolio.py

Add 'Product Own' calculation to portfolio in predict_dupes.py

Initial Commit