Refactor threshold logic in distribute_preset function to improve clarity and accuracy in filtering. Introduced a lower threshold variable for better readability and consistency in handling 'Finish_percentile' across all finishing ranges.

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

Remove limit on concatenation in distribute_preset function to include all entries of the working portfolio, enhancing data completeness while maintaining sorting by 'Finish_percentile'.

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

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

Update preset option in app.py to correct wording for 'Distributed' selection

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.

Add info message in Presets section of app.py to indicate ongoing testing status, enhancing user awareness of feature readiness.

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.

Update preset options in app.py: modify the small field preset selection to include more descriptive labels, enhancing clarity for users when choosing lineup configurations.

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.

Comment out calculations for highest owned teams and pitchers in app.py to streamline functionality and focus on essential features, while maintaining the option for future use.

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.

Implement form submission for small field preset in app.py: add a submit button to apply lineup target changes, enhancing user interaction and ensuring updates to the working frame are only processed upon user confirmation.

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.

Refine custom export row addition in app.py: update logic to use head for selecting rows, ensuring accurate concatenation of selected rows from the merge DataFrame and improving data integrity in the export process.

Refine row selection for custom export in app.py: replace selectbox with number input for selecting rows, enhancing user experience and ensuring accurate row addition from the merge DataFrame.

Update custom export selection in app.py: replace multiselect with selectbox for row selection, allowing users to select rows from the top of the DataFrame, and adjust concatenation logic to include selected rows more intuitively.

Refine custom export logic in app.py: update the condition to check for 'export_merge' in session state, enhancing error handling and ensuring smoother user interactions when selecting rows for custom export.

Enhance custom export functionality in app.py: add a check for the presence of 'export_merge' before allowing row selection, improving robustness and preventing errors during user interaction.

Update column layout for export options in app.py: adjust button column widths to improve UI spacing and enhance user experience when downloading portfolios.

Refactor custom export options in app.py: reorganize layout to separate buttons for adding all and selected rows to the custom export, improving user interface clarity and functionality.

Add custom export functionality in app.py: introduce options to add all or selected rows from the merge DataFrame to the custom export, enhancing user control over data selection and improving export flexibility.

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

Update stack inclusion logic in app.py: modify condition to check for sport types (NFL, MLB, NHL) instead of 'Stack' column, enhancing stack selection functionality for specific sports.