James McCool commited on
Commit
0b97fc2
·
1 Parent(s): 3597da4

Update salary calculation logic in app.py

Browse files

- Refactored the salary calculation to utilize 'salary_dict' instead of 'salary_map', ensuring accurate data retrieval for player salaries.
- This change enhances the consistency of data processing across different sports, particularly for GOLF, by standardizing the method of accessing salary information.

Files changed (1) hide show
  1. app.py +2 -2
app.py CHANGED
@@ -181,7 +181,7 @@ with tab2:
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  ).most_common(1)[0][1] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[st.session_state['player_columns']]) else '',
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  axis=1
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  )
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- working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
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  working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
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  working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
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  working_df['sorted'] = working_df[st.session_state['player_columns']].apply(
@@ -223,7 +223,7 @@ with tab2:
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  axis=1
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  )
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  if sport_select == 'GOLF':
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- working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['map_dict']['salary_map'].get(player, 0) for player in row), axis=1)
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  working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
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  else:
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  # Modified salary calculation with 1.5x multiplier for first player
 
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  ).most_common(1)[0][1] if any(st.session_state['map_dict']['team_map'].get(player, '') for player in row[st.session_state['player_columns']]) else '',
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  axis=1
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  )
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+ working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['salary_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
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  working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
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  working_df['actual_own'] = working_df.apply(lambda row: sum(st.session_state['ownership_dict'].get(player, 0) for player in row[st.session_state['player_columns']]), axis=1)
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  working_df['sorted'] = working_df[st.session_state['player_columns']].apply(
 
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  axis=1
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  )
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  if sport_select == 'GOLF':
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+ working_df['salary'] = working_df.apply(lambda row: sum(st.session_state['salary_dict'].get(player, 0) for player in row), axis=1)
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  working_df['actual_fpts'] = working_df.apply(lambda row: sum(st.session_state['actual_dict'].get(player, 0) for player in row), axis=1)
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  else:
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  # Modified salary calculation with 1.5x multiplier for first player