Refactor stacking logic in app.py to streamline team assignment calculations for player lineups. This update removes legacy stacking code and optimizes the application of team data, enhancing performance and maintainability.

Refactor portfolio filtering in app.py to convert entries to string and remove invalid values before saving to compressed format. This update enhances data integrity and ensures only valid entries are retained.

Remove debug print statements from app.py that were used for validating the working frame structure and content. This cleanup enhances code readability and maintains focus on core functionality.

Add debug prints for working frame in app.py to assist with data validation. This change helps verify the structure and content of the working frame after applying salary cap filtering.

Reintroduce salary cap filtering for working frame in app.py to ensure only eligible players are loaded. This update enhances data integrity and aligns with previous optimizations for memory efficiency.

Update portfolio management in app.py to reintroduce salary cap filtering for working frame. This change ensures that only eligible players are loaded into the working frame, enhancing data integrity and memory efficiency.

Refactor portfolio storage in app.py to filter by salary cap before saving to compressed format. This change optimizes memory usage and ensures only eligible entries are retained.

Update score cutoff in chunk_name_matching function and add PowerShell scripts for instance management

Added QB force option for micro

Enhance freq_format in app.py to include 'Lineup Edge_Raw', improving data representation for portfolio management.

Update excluded_cols in app.py to include 'Lineup Edge_Raw', enhancing the data handling for portfolio management.

Refactor DataFrame optimization in app.py to enhance memory efficiency. Disable categorical conversion for specific columns to avoid issues with exposure_spread, while implementing smarter handling for numeric types. Introduce a new function to prepare DataFrames for exposure_spread, ensuring compatibility and improved performance during calculations.

Enhance DataFrame optimization and vectorized calculations in app.py. Refine category conversion logic to exclude specific player columns and improve memory efficiency. Introduce a safe mapping function to handle NaN values for salary, median, and ownership calculations, ensuring better performance across various scenarios.

Implement vectorized calculations for salary, median, and ownership in app.py to enhance performance and memory efficiency. Refactor reassess_edge and stratification_function to minimize DataFrame copies and improve memory management. Update filtering logic to use boolean masks for better efficiency.

Refactor portfolio storage in session state to use compressed format directly

Add memory optimization functions and implement chunked processing for name matching in app.py

Add error handling for position replacement in CSV loading process for Fanduel site. This ensures that any exceptions during the replacement of 'D' with 'DST' are gracefully handled without interrupting the flow.

Refactor session state management by removing unnecessary copy operations for origin and working portfolios.

Add smart memory management to optimize session state handling and include psutil in requirements

Refine position replacement logic in CSV loading process to apply only when site_var is 'Fanduel'.

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Added replacement for 'D' to 'DST' in Roster Position column of CSV template and included a print statement for verification.

finalized dupe math, maintaining on staging

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Remove table display in 'app.py' and adjust dupes calculation in 'predict_dupes.py' to include a new scaling factor for CPT_Own_percent_rank, enhancing prediction accuracy.

printing a check frame

removing Fanduel specific logic from Predict Dupes

checking a debug

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Refactor multiselect options in 'Manage Portfolio' section of 'app.py' to remove unnecessary sorting, improving performance and clarity in player and team selection.

extending display frame

Remove debug print statements from 'app.py' to streamline data loading process and enhance code clarity, while retaining essential data validation steps in 'load_ss_file.py'.

Remove redundant NaN logging from 'Data Load' section of 'app.py' and add initial data load debug statements in 'load_ss_file.py' to enhance data validation and transparency during file loading.

Add additional logging in 'Data Load' section of 'app.py' to display total rows, NaN counts per column, and samples of rows with NaN values, enhancing data validation during file loading.

Add debug print statements in 'Data Load' section of 'app.py' to log the count of missing values in the portfolio for improved data validation during file loading.

Add debug print statement in 'Data Load' section of 'app.py' to log the count of missing values in the portfolio, aiding in data validation during file loading.

Add comment in 'Manage Portfolio' section of 'app.py' regarding potential changes to Showdown position handling, indicating future considerations for CPT/FLEX options.

Update 'Manage Portfolio' section in 'app.py' to source specific positions from 'player_columns' instead of 'working_frame', improving data accuracy for user selections.

testing exposure management with specific column selections

Enhance player filtering logic in 'Manage Portfolio' section of 'app.py' by implementing conditional checks for both alpha and beta players, improving the accuracy of row selection based on user-defined criteria.

Refactor player presence logic in 'Manage Portfolio' section of 'app.py' to improve clarity by using direct row value checks instead of row values array, enhancing code readability.

Update 'Manage Portfolio' section in 'app.py' to include unique keys for CPT/FLEX selection boxes, enhancing state management and user experience.

Add CPT/FLEX selection options in 'Manage Portfolio' section of 'app.py' to enhance player filtering capabilities based on slot type.

Enhance min/max input constraints in 'Manage Portfolio' section of 'app.py' for better user guidance and validation.

More fine grained min/max filters in macro

Adjust column widths in 'Manage Portfolio' section of 'app.py' to improve layout and enhance user interaction with the reset portfolio button.

Update row selection logic in 'Manage Portfolio' section of 'app.py' to use 'display_frame' for adding selected rows to custom export, improving data handling and user experience.

Refactor column layout in 'Manage Portfolio' section of 'app.py' to enhance user interface by adjusting column definitions and improving button placements for better functionality.