Enhance portfolio concatenation in distribute_preset function by increasing the limit to the top 10 entries and adding a step to remove duplicates based on 'median', 'salary', and 'Own' for improved data relevance and accuracy.

Limit concatenation in distribute_preset function to the top 5 entries of the working portfolio to further enhance performance and focus on the most relevant data.

Limit concatenation in distribute_preset function to the top 10 entries of the working portfolio to enhance performance and maintain focus on the most relevant data.

Update preset label in app.py for clarity and consistency

Update debug print statement in distribute_preset function to display only the top 10 entries of the working portfolio for improved clarity during execution.

Add debug print statement in distribute_preset function to output working portfolio for better traceability during execution.

Refactor filtering logic in distribute_preset function to improve handling of 'Finish_percentile' thresholds. Introduced a variable for finish threshold to enhance code readability and maintain accurate portfolio selection across all finishing ranges.

Fix variable reference in distribute_preset function to correctly use 'Weighted Own' for filtering logic, ensuring accurate portfolio selection based on ownership thresholds.

Refactor distribute_preset function to improve filtering logic for 'Finish_percentile' by handling edge cases for the first and last finishing ranges, ensuring accurate portfolio selection and maintaining sorting by 'median' in descending order.

Update sorting logic in distribute_preset function to sort 'median' in descending order for improved portfolio selection accuracy

Add distribute_preset function and update app.py for preset selection

Refactor large_field_preset function to streamline player removal logic by eliminating unnecessary iterations and simplifying the exposure tracking process. Ensure sufficient lineups are generated before finalizing selections, enhancing overall efficiency and accuracy.

Enhance large_field_preset function to improve player exposure tracking by implementing a mechanism to remove high-exposure players across iterations, ensuring lineup integrity and preventing excessive ownership. Adjust logic to check for sufficient lineups before finalizing selections.

Refactor large_field_preset function to streamline the portfolio selection process by removing unnecessary iterations and simplifying the logic for team-based filtering, enhancing efficiency and lineup accuracy.

Enhance small_field_preset and large_field_preset functions to accept an additional parameter for excluded columns, improving filtering capabilities and lineup accuracy. Update app.py to reflect these changes in preset selections.

Refactor large_field_preset function to adjust filtering criteria, replacing 'Finish_percentile' with 'Own' for team-based selection, enhancing accuracy in lineup targeting and improving the logic for dropping rows based on ownership thresholds.

Refactor large_field_preset function to simplify ranking logic by removing median and finish percentile rankings, and adjust filtering criteria to focus on 'Finish_percentile' for team-based selection, enhancing accuracy in lineup targeting.

Remove filtering of working_portfolio by 'Lineup Edge' in large_field_preset function to allow for broader selection of portfolios, enhancing lineup options.

Update ranking logic in large_field_preset function to sort 'median' in descending order and 'Finish_percentile' in ascending order, enhancing accuracy in team-based filtering and lineup targeting.

Refactor large_field_preset function to improve team-based filtering by introducing median and finish percentile rankings for enhanced sorting accuracy. Clean up temporary columns after processing to maintain DataFrame integrity.

Refactor large_field_preset and small_field_preset functions to use 'Stack' instead of 'Team' for team-based filtering, ensuring consistency in portfolio adjustments and improving accuracy in lineup targeting.

Refactor large_field_preset and small_field_preset functions to enhance lineup targeting: increase slack_var range, implement team-based filtering, and consolidate results into a single DataFrame for improved accuracy in portfolio adjustments.

Refactor large_field_preset function to improve sorting logic: change the final return statement to sort by 'Finish_percentile' instead of 'Own', ensuring more accurate lineup targeting based on performance metrics.

Add large field preset functionality in app.py and implement large_field_preset function for improved lineup targeting. Update small_field_preset function to sort by 'Own' for consistency in portfolio adjustments.

Enhance app functionality by adding calculations for the highest owned teams and pitchers based on projections, improving user insights into team ownership trends. Refactor small_field_preset function to ensure consistent DataFrame structure by sorting working portfolio by median before returning results.

Refactor small_field_preset function to ensure consistent DataFrame structure by resetting the index after filtering and sorting the working portfolio by Median before returning results, improving accuracy in lineup selection.

Update small_field_preset function to reset index after filtering working portfolio by Finish_percentile, ensuring consistent DataFrame structure for subsequent calculations.

Enhance preset selection in app.py: add a dropdown for choosing presets and update lineup target input label, improving user experience and interaction. Update small_field_preset function to filter working portfolio based on Finish_percentile, ensuring more accurate portfolio adjustments.

Refactor slack_var handling in small_field_preset function: change iteration from float to integer and adjust calculation for curr_own_type_max to improve accuracy in portfolio adjustments.

Fix range definition in small_field_preset function: change slack_var initialization from integer to float for accurate iteration, ensuring proper functionality in portfolio adjustments.

Add small field preset functionality in app.py: integrate new preset options for lineup targeting, enhancing user experience and providing better control over portfolio management.

Remove unnamed columns from uploaded DataFrame in load_file.py: enhance data cleanliness by dropping columns with 'Unnamed' in their names before further processing.

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.