Add Stack Priority filtering option in app.py to enhance user control over scoring percentages display. Users can now isolate data based on specific Stack Priority selections, improving data analysis capabilities.

Remove 'Avg_Salary' from the background gradient in scoring percentages display in app.py, streamlining the visual presentation for the Simple view while maintaining clarity for the Advanced view.

Enhance scoring percentages display in app.py by adding a second background gradient for 'Avg_Salary' and 'Own%' columns, improving visual differentiation and data clarity in both Simple and Advanced views.

Update column names in scoring percentages DataFrame in app.py to include 'Avg_Salary' and 'Stack_Prio', enhancing data clarity and consistency in player information presentation.

Reorder columns in player display within app.py to enhance data presentation, improving readability and consistency in the displayed player information.

Standardize column names in app.py from 'Site' and 'Slate' to 'site' and 'slate' for consistency in player data filtering, improving code readability and maintainability.

Add slate-specific filtering in app.py to refine player data based on selected slate type (Main, Secondary, Auxiliary), improving lineup generation accuracy.

Fix salary column reference in app.py to ensure accurate filtering of data export display based on minimum and maximum salary values, enhancing data integrity for lineup generation.

Refactor app.py to enhance user input handling for slate types and player selection, improving layout with columns and ensuring accurate lineup generation based on selected parameters.

Refactor app.py to consistently set index for scoring percentages and player ROO displays, enhancing data presentation and ensuring uniformity across different view types.

Refactor app.py to set index for scoring percentages and player ROO displays, improving data presentation and consistency across views.

Refactor scoring percentage calculations in app.py to drop unnecessary columns for DraftKings and FanDuel, enhancing data clarity and consistency across slate types.

Refactor scoring percentage calculations in app.py to consistently drop unnecessary columns for all slate types in DraftKings and FanDuel, improving data clarity and reducing redundancy.

Remove unnecessary column drops for DraftKings and FanDuel in app.py, streamlining scoring percentage calculations and improving data clarity.

Update app.py to handle empty values in scoring percentages for all slate types in DraftKings and FanDuel, ensuring accurate data representation and improving overall data integrity.

Enhance scoring percentage calculations in app.py by implementing slate-specific logic for DraftKings and FanDuel, ensuring accurate ranking and representation of top scores across different slate types.

Refactor game format keys in app.py to simplify naming conventions for scoring percentages, enhancing clarity and consistency in data representation.

Update scoring percentage columns in app.py to include specific slate types for DraftKings and FanDuel, ensuring accurate data representation and enhancing user experience.

Refactor scoring percentage calculations in app.py to differentiate between DraftKings and FanDuel slate types, updating column names and improving data filtering logic for enhanced accuracy and user experience.

Update app.py to include additional slate options ('Secondary Slate' and 'Turbo Slate') in the data loading selection, improving user flexibility and enhancing the overall experience.

Update scoring percentages and filtering logic in app.py to accommodate multiple slate types for DraftKings and FanDuel, enhancing data accuracy and user experience.

Update player column selection in app.py to adjust indices for 'Showdown' slate types in DraftKings and FanDuel, ensuring accurate data representation and improving user experience.

Refactor database connection in app.py to remove unused database reference and streamline data retrieval for DraftKings and FanDuel lineups, enhancing code clarity and maintainability.

Refactor player column selection in app.py to dynamically adjust based on slate type for DraftKings and FanDuel, ensuring accurate data representation and enhancing user experience.

Refactor ownership value calculations in app.py to use updated column indices for DraftKings and FanDuel, and enhance summary statistics display based on slate type, improving data accuracy and user experience.

Update app.py to include new column mappings for DraftKings and FanDuel showdown formats, enhancing data display and ensuring accurate representation of team and player metrics in lineups.

Enhance team ownership calculations in app.py by filtering for 'main_slate' in DraftKings and FanDuel data, updating ownership metrics, and refining scoring percentages with new calculations for 'DK LevX' and 'FD LevX'.

Add functionality to export player data in both ID and name formats in app.py, enhancing user options for data downloads and improving overall export capabilities.

Update data export in app.py to convert working seed into a DataFrame with specified column names, enhancing data structure for export functionality.

Fix player ID mapping in app.py by swapping keys and values in the dictionaries for DraftKings and FanDuel, ensuring accurate player identification in data processing.

Refactor data export functionality in app.py by replacing hardcoded column indices with named column mappings for DraftKings and FanDuel, improving code readability and maintainability.

Add player ID mappings for DraftKings and FanDuel in app.py to enhance data export functionality, ensuring accurate player identification in exported files.

Remove duplicate player entries in app.py by dropping duplicates based on the 'Player' column, enhancing data integrity for player analysis.

Update position group selection in app.py by including 'UTIL' in the dict_columns for player data mapping, ensuring comprehensive data representation across all slate types.

Update position group selection variable in app.py for improved clarity and consistency, changing 'pos_var2' to 'group_var2' in user interface elements and related logic for player data filtering.

Enhance user interface in app.py by updating team and position selection prompts for clarity, allowing users to select multiple teams and positions, thereby improving data filtering capabilities in player analysis.

Enhance team ownership calculations in app.py by replacing 'CWS' with 'CHW' for both DraftKings and FanDuel data, and improve scoring percentages by sorting and dropping the 'Slate' column, refining player metrics analysis.

Update 'Top Score' handling in scoring percentages calculation in app.py to replace empty strings with NaN before casting to float, improving data integrity in player metrics analysis.

Cast 'Top Score' to float in scoring percentages calculation in app.py, ensuring consistency and accuracy in player metrics analysis.

Refactor DK and FD LevX calculations in app.py to ensure rank calculations are explicitly cast to float, improving accuracy in scoring percentage metrics.

Add 'Top Score' to scoring percentages in app.py and calculate DK and FD LevX metrics based on this new column, enhancing player analysis capabilities.

Update scoring percentages in app.py to include 'Slate' column for filtering data based on selected slate type, enhancing user experience and data relevance in the display.

Update ownership column names in app.py for clarity by renaming 'Own%' to 'DK Own%' and 'FD Own%' after aggregating team ownership data, ensuring consistency in scoring percentages for DraftKings and FanDuel.

Refactor team ownership calculations in app.py to separate DraftKings and FanDuel data processing, enhancing clarity and accuracy in scoring percentages while removing unnecessary columns.

Add team ownership calculations to scoring percentages in app.py by filtering out pitchers and aggregating ownership data, enhancing player metrics for analysis.

Refactor scoring percentage calculations in app.py by removing division by 100 for '8+ runs' and 'Win Percentage', improving data accuracy and simplifying the conversion process.

Update app.py to include additional columns 'Team', 'Team_count', 'Secondary', and 'Secondary_count' in player data display, enhancing the detail and usability of player metrics for both DraftKings and FanDuel lineups.

Enhance player ROO data handling in app.py by introducing separate dataframes for DraftKings and FanDuel, improving clarity and organization in the data processing flow while maintaining existing functionality.

Update column name for player ownership in app.py by renaming 'Own' to 'Own%', enhancing clarity in data presentation for player ROO display.

Refactor player ROO display logic in app.py by eliminating try-except blocks and directly dropping unnecessary columns based on slate type, improving code clarity and maintaining functionality.