Adjust team filter UI in Handbuilder tab of app.py to spread toggle switches across fewer columns, improving layout and user experience.

Add team filter UI in Handbuilder tab of app.py to allow users to toggle team selections, enhancing player selection flexibility and improving user experience.

Enhance "Clear Data" button functionality in Handbuilder tab of app.py by introducing a session state variable to control the key, ensuring a proper reset of the player selection editor on each rerun, thereby improving user interactivity and experience.

Refactor "Clear Data" button functionality in Handbuilder tab of app.py to ensure proper data reset on rerun, enhancing user interactivity and maintaining a clean state for player selections.

Update "Clear Data" button functionality in Handbuilder tab of app.py to trigger a rerun, improving data refresh behavior and enhancing user interactivity.

Refactor player selection logic in Handbuilder tab of app.py by removing the "Clear All Selections" button and simplifying the state management. Introduce a "Clear Data" button to reset selections, enhancing user experience and interactivity.

Implement "Clear All Selections" button in Handbuilder tab of app.py to reset player selections, enhancing user interactivity. Update session state management to preserve user selections across reruns, improving overall functionality.

Enhance Handbuilder tab in app.py by increasing the height of the player selection display for better visibility and adding a "Clear Data" button to reset player selections, improving user experience and interactivity.

Update player selection display in Handbuilder tab of app.py to remove unnecessary columns from the DataFrame, enhancing clarity and maintaining the overall functionality of the lineup presentation.

Refactor selected players DataFrame in Handbuilder tab of app.py to simplify the structure by removing unnecessary columns, enhancing clarity in player selection display.

Refactor lineup display in Handbuilder tab of app.py to use a separate DataFrame for selected players, enhancing clarity and maintaining functionality in the user interface.

Update player selection DataFrame in Handbuilder tab of app.py to include 'Order' and 'Hand' columns, and adjust lineup display settings for improved user experience and clarity.

Refactor player selection logic in Handbuilder tab of app.py to streamline DataFrame handling and improve user experience by ensuring fresh data on each rerun, while maintaining lineup display functionality.

Refactor player selection handling in Handbuilder tab of app.py to improve session state management and enhance user experience by separating lineup display and player selection editor.

Add Handbuilder tab in app.py to allow user selection of players for lineup creation, enhancing user interactivity and lineup management capabilities.

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Add Pitcher_Info integration in app.py to enhance player data processing by including LHH and RHH attributes, improving data analysis capabilities for hitters.

Enhance player ROO DataFrame styling in app.py by expanding the background gradient subset to include 'Small Field Own%', 'Large Field Own%', and 'Cash Own%' for improved visual representation and clarity.

Refactor background gradient styling in app.py for scoring percentages and player ROO DataFrames to enhance visual clarity by including 'Salary' and 'Own%' in the gradient subset.

Update scoring percentages DataFrame in app.py to include 'Order' in the background gradient styling for improved visual clarity and data representation.

Update 'Order' and 'Hand' column handling in app.py to replace NaN with 0 for 'Order' and default 'Hand' to 'Pitcher' for improved data consistency and clarity in player data processing for hitters.

Update 'Order' column handling in app.py to replace empty strings with NaN for improved data integrity, and ensure proper type conversion for consistency in player data processing.

Update 'Order' column type to integer and add 'Runs/$' calculation in scoring percentages DataFrame in app.py for improved data accuracy and analysis capabilities.

Refactor data handling in app.py to create a separate hold_frame for 'Order' and 'Hand' attributes, improving clarity and organization in player data processing for hitters.

Refactor Hitter_Info integration in app.py to separate LHP and RHP data, ensuring accurate mapping of 'Order' and 'Hand' attributes for hitters, enhancing data processing and analysis capabilities.

Add Hitter_Info integration in app.py to enhance player data processing by including 'Order' and 'Hand' attributes for hitters, improving data analysis capabilities.

Update scoring percentages DataFrame in app.py to include 'Opp_SP' for improved data analysis and clarity in player performance metrics.

Update Stack Priority options in app.py to use 'OF_Prio' and 'IF_Prio' for improved clarity in user selections, enhancing the data filtering experience.

Fix the order of options in Stack Priority isolation radio button in app.py to improve user experience and clarity in data filtering.

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.