Add debug output for Draftkings raw data in app.py: include display of dk_raw DataFrame to enhance visibility during data processing and troubleshooting.

Update data loading in app.py to switch from NBA to WNBA for overall statistics, ensuring accurate data retrieval for player salary and ID mappings during the export process.

Update column renaming in load_overall_stats function in app.py: change 'DK_ID' and 'FD_ID' to 'player_ID' for improved clarity and consistency in data handling for WNBA league exports.

Refactor salary and ID dictionary handling in app.py: update variable names for clarity and ensure consistent mapping for Draftkings and Fanduel exports, enhancing data accuracy during player data processing.

Refactor lineup initialization in app.py: streamline the handling of 'working_seed' for Draftkings and Fanduel by consolidating logic for regular and showdown slate types, enhancing data export display and improving overall application performance.

Remove 'working_seed' from session state in app.py to streamline application performance and enhance user experience.

Enhance data export display in app.py: add output of column names and working seed to the user interface, improving visibility of data during the export process.

Refactor data export logic in app.py: streamline the export process by consolidating column drop operations for Portfolio Manager exports and enhancing the mapping of player position columns. This update improves data clarity and ensures accurate filtering based on salary ranges.

Refactor data export logic in app.py: streamline the export process by removing redundant mapping for player position columns and ensuring accurate filtering of entries based on salary range. Update Portfolio Manager exports to reflect these changes, enhancing data clarity and user experience.

Refactor data export logic in app.py: remove index settings for player position columns in Draftkings and Fanduel exports, simplifying the export process while ensuring accurate data representation for NBA and WNBA leagues.

Enhance data export logic in app.py: introduce mapping for player position columns in Draftkings and Fanduel exports, ensuring accurate data representation for NBA and WNBA leagues. This update improves the handling of player data during export processes.

Refactor data export logic in app.py: remove unnecessary index settings for WNBA exports, simplifying the data export process while maintaining accurate data representation for league-specific handling.

Add character normalization for data exports in app.py: implement a function to convert accented characters to ASCII equivalents, ensuring cleaner data output. Update CSV export functions to apply normalization, enhancing data consistency and accessibility.

Update data export logic in app.py for league-specific handling: modify index settings for Draftkings and Fanduel exports based on league type (NBA or WNBA), ensuring accurate data representation and enhancing the export functionality for different sports.

Remove unused session state variable in app.py: eliminate the 'working_seed' from session state to streamline the application and improve performance.

Add new data export functionality in app.py: introduce a separate function for exporting data in Portfolio Manager format, enhance the full data export process with additional filtering options, and streamline the download buttons for both optimals and filtered exports, improving user experience and data accessibility.

Fix salary column reference in data export display: update the column name from 'Salary' to 'salary' to ensure proper filtering of entries within the specified salary range, enhancing data accuracy in the export functionality.

Filter data export by salary range in app.py: update the data export display to include only entries within the specified minimum and maximum salary limits, enhancing user control over the exported data.

Enhance app.py with salary input fields: add minimum and maximum salary inputs for Draftkings and Fanduel, improving user control over data filtering and enhancing the overall functionality of the app.

Refactor display logic in app.py: streamline the conditional rendering of the project display by removing unnecessary container usage, enhancing code clarity and maintaining functionality for filtering based on position selection.

Refactor data export logic in app.py: update export data sources to use raw_baselines instead of display_proj, ensuring accurate player data retrieval for both Regular and Portfolio Manager views, and correct column renaming for improved clarity.

Enhance app.py with new download functionality: add export options for both Regular and Portfolio Manager views, allowing users to download player data in CSV format, improving data accessibility and user experience.

Refactor app.py layout for improved user experience: reorganize UI components into a container for better structure, enhance data loading functionality with a reset button, and streamline league and site selection processes, ensuring a more intuitive interface for managing NBA and WNBA lineups.

Refactor app.py layout and logic for improved user interaction: reorganize UI components for league and site selection, enhance data loading functionality, and streamline slate type handling, ensuring a more intuitive experience for users managing NBA and WNBA lineups.

Refactor summary statistics calculations in app.py: streamline the computation of minimum, average, maximum, and standard deviation for salary, project, and own metrics by correcting column indices, enhancing data accuracy and clarity in the displayed results.

Refactor league variable in app.py: rename 'league_var' to 'league_var2' for clarity and consistency in league selection logic, ensuring proper functionality across data retrieval and display for both NBA and WNBA leagues.

Enhance app.py to support both NBA and WNBA leagues: refactor lineup initialization functions to accept league as an argument, update column definitions for WNBA, and implement conditional logic for data retrieval and display, improving overall functionality and user experience.

Update 'Median' column handling in app.py: replace empty strings with zero before converting to float, ensuring robust data processing and preventing type-related issues during overall stats retrieval.

Convert 'Median' column to float type in app.py: ensure consistent data handling and prevent type-related issues during overall stats retrieval.

Improve player ID handling in app.py: add error handling for player_ID column selection to ensure consistent data frame structure, renaming player_id to player_ID when necessary, enhancing data integrity during processing.

Update player ID processing in app.py: convert player_ID column to string type in Fanduel data frame to ensure consistent data handling and prevent type-related issues during processing.

Enhance overall stats retrieval in app.py: implement conditional logic for NBA and WNBA leagues to customize data frame structure and column renaming, improving data handling and clarity for player statistics.

Refactor league variable usage in app.py: replace hardcoded 'NBA' string with direct argument in load_overall_stats function calls, improving code clarity and maintainability.

Add WNBA database support in app.py: modify connection initialization and update overall stats retrieval to handle both NBA and WNBA data, enhancing functionality for player statistics management.

Update player ID processing in app.py: modify Fanduel data frame to split player IDs at the last hyphen, ensuring accurate mapping and enhancing data integrity during export.

Refine player ID processing in app.py: update Fanduel data frame to split player IDs at the last hyphen, ensuring accurate mapping and improving data integrity during export.

Refine player ID processing in app.py: update Fanduel data frame to remove the last four characters from player IDs, ensuring accurate mapping and improving data integrity during export.

Update player ID mapping in app.py: modify Fanduel data frame to remove '-mvp' suffix from player IDs and ensure accurate mapping for Showdown slate types, enhancing data integrity during export.

Enhance overall stats retrieval in app.py: add separate data frames for Draftkings and Fanduel to improve player ID mapping and streamline data handling during lineup processing.

Refine lineup data retrieval in app.py: update database queries to filter teams with less than 6 players for both Draftkings and Fanduel, enhancing data accuracy and lineup generation.

Enhance data export functionality in app.py: add separate download buttons for optimal lineups by player names and IDs, improving user experience and data accessibility.

Update data export column handling in app.py: increase the number of columns processed for Showdown slate types in both Draftkings and Fanduel, ensuring accurate player ID mapping and enhancing data export functionality.

Implement conditional data export logic in app.py: enhance the data export functionality by incorporating slate type (Regular or Showdown) for both Draftkings and Fanduel, ensuring accurate player ID mapping and improving data handling during export.

Remove 'working_seed' from session state in app.py: implement logic to delete 'working_seed' when switching between sites, ensuring accurate data handling and preventing stale data issues during lineup processing.

Refactor working seed initialization in app.py: update logic for setting working seed based on slate type (Regular or Showdown) for both Draftkings and Fanduel, ensuring accurate lineup data handling and improving code clarity.

Enhance summary statistics generation in app.py: implement conditional logic for slate type (Regular or Showdown) to accurately compute metrics for Draftkings and Fanduel, improving data representation and user experience.

Refactor lineup initialization in app.py: remove try-except blocks for lineup creation, ensuring consistent DataFrame initialization for both Draftkings and Fanduel across Regular and Showdown slate types, enhancing code clarity and reliability.

Update salary input fields in app.py: adjust minimum salary values for both lowest and highest salary inputs to enhance user experience and ensure valid input range.

Update slate selection logic in app.py: enhance raw baseline retrieval by incorporating slate type (Regular or Showdown) for both Main and Secondary slates, improving data accuracy based on user input.

Add debug output in load_overall_stats function of app.py: print top 10 sorted player statistics for improved visibility during data processing.