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.

Update size selection logic in app.py: conditionally include size options based on sport type, ensuring appropriate functionality for NFL, MLB, and NHL while defaulting to an empty selection for others.

Update excluded columns in app.py: replace 'Dupes' with 'Size' in the list of excluded columns to reflect recent changes in player size selection functionality.

Add size selection functionality in app.py: introduce options to include player sizes in lineups, enhancing user control over player configurations and improving data representation.

Update team selection options in app.py: switch from using 'working_frame' to 'projections_df' for team inclusion and removal, ensuring accurate data representation in player configurations.

Add team selection functionality in app.py: introduce options to include and remove teams from lineups for enhanced user control over player configurations.

Enhance stack selection functionality in app.py: update stack inclusion and removal options for improved user control over lineup configurations.

Refactor exposure calculation in app.py: remove percentage formatting from 'Exposure' field to ensure consistent raw value representation across player statistics.

Refactor exposure calculation in app.py: update 'Exposure' field to store raw percentage values instead of formatted strings for improved data handling and consistency in player statistics.

Refactor player statistics display in app.py: update 'Lineup Count' to show raw counts and add 'Exposure' percentage for enhanced clarity in player usage analysis.

Update player and stack statistics display in app.py: modify 'Lineup Count' to include percentage of total lineups for improved clarity and insight into player usage.

Fix condition for stack statistics display in app.py: update header check to use 'columns' instead of 'headers' for accurate data retrieval.

Enhance stack summary in app.py: modify sorting to include dropping duplicates for improved data accuracy and clarity in displayed statistics.

Enhance tab layout for player and stack statistics in app.py: replace column layout with tabs for improved organization and user navigation, and adjust stack statistics condition for better data handling.

Refactor player and stack statistics display in app.py: streamline player summary calculations and add stack summary section for enhanced data visualization and user experience.

Adjust column layout in portfolio section of app.py: change from 3 to 2 columns for improved UI in the projections display.

Refactor contest size and strength input in app.py: replace individual input fields with a form layout for better organization and user experience, and ensure portfolio reset functionality is maintained upon submission.

Merge branch 'main' of https://huggingface.co/spaces/Multichem-PD/DFS_Portfolio_Manager

Update sport selection options in app.py: add 'WNBA' to the list of available sports in the dropdown menu, enhancing user choice and application functionality.

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Update app.py

Improve error handling for CSV file names in app.py: add a try-except block to ensure robust retrieval of names from session state, enhancing application stability and user experience.

Enhance user interface in app.py: add a fourth column for game type selection, reorganize input fields for site, sport, contest size, and field strength, and improve layout for better user interaction and clarity.

Add user interface enhancements in app.py: introduce new selection options for site, sport, game type, contest size, and field strength, along with a clear data reset button, improving user experience and interaction within the application.

Implement conditional team stacking logic in app.py: add a check to ensure 'Stack' column is created only if it doesn't exist, enhancing the portfolio's team analysis functionality and preventing redundant calculations.

Add team stacking logic in app.py: implement a new feature to determine the most common team for players in the portfolio, enhancing team analysis capabilities and improving user insights into player distributions.

Update score cutoff values in app.py: increase the score_cutoff for name matching from 85 to 87 to enhance matching accuracy for portfolio and projections, ensuring better alignment with user expectations.