Remove unnecessary print statements from app.py and load_contest_file.py for cleaner code

- Eliminated print statements related to salary_dict and player information in both app.py and load_contest_file.py, enhancing code readability and reducing clutter in the output.
- Maintained existing functionality while streamlining the data handling process during contest file loading and player information retrieval.

app.py

@@ -146,8 +146,6 @@ with tab1:
             st.session_state['team_dict'] = dict(zip(st.session_state['team_df']['Player'], st.session_state['team_df']['Team']))
             st.session_state['pos_dict'] = dict(zip(st.session_state['pos_df']['Player'], st.session_state['pos_df']['Pos']))
 
-        st.write(st.session_state['salary_dict'])
-
 with tab2:
     excluded_cols = ['BaseName', 'EntryCount']
     if 'Contest' in st.session_state:

global_func/load_contest_file.py

@@ -48,8 +48,6 @@ def load_contest_file(upload, helper_var, helper = None, sport = None):
             
             print('Made it through helper')
 
-            print(df_helper[df_helper['Player'] == 'Luis Torrens'])
-
             contest_names = df.Player.unique()
             if helper is not None:
                 helper_names = helper_df.Player.unique()
@@ -84,8 +82,6 @@ def load_contest_file(upload, helper_var, helper = None, sport = None):
             
             df_helper['Player'] = df_helper['Player'].map(contest_match_dict)
             df_helper = df_helper.drop_duplicates(subset='Player', keep='first')
-
-            print(df_helper[df_helper['Player'] == 'Luis Torrens'])
             
             # Create separate dataframes for different player attributes
             if helper is not None:
@@ -112,8 +108,6 @@ def load_contest_file(upload, helper_var, helper = None, sport = None):
             elif sport == 'GOLF':
                 cleaned_df['Lineup'] = cleaned_df['Lineup'].replace([' P ', ' C ', '1B ', ' 2B ', ' 3B ', ' SS ', ' OF ', ' G ', 'G '], value=',', regex=True)
             print(sport)
-            print(cleaned_df.head(10))
-            st.table(cleaned_df.head(10))
             check_lineups = cleaned_df.copy()
             if sport == 'MLB':
                 cleaned_df[['Remove', '1B', '2B', '3B', 'C', 'OF1', 'OF2', 'OF3', 'P1', 'P2', 'SS']] = cleaned_df['Lineup'].str.split(',', expand=True)
@@ -121,7 +115,6 @@ def load_contest_file(upload, helper_var, helper = None, sport = None):
                 cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
             elif sport == 'GOLF':
                 cleaned_df[['Remove', 'Guy', 'Dude', 'Pooba', 'Bub', 'Chief', 'Buddy']] = cleaned_df['Lineup'].str.split(',', expand=True)
-            st.table(cleaned_df.head(10))
             cleaned_df = cleaned_df.drop(columns=['Lineup', 'Remove'])
             entry_counts = cleaned_df['BaseName'].value_counts()
             cleaned_df['EntryCount'] = cleaned_df['BaseName'].map(entry_counts)
@@ -134,15 +127,6 @@ def load_contest_file(upload, helper_var, helper = None, sport = None):
             st.table(cleaned_df.head(10))
             
             print('Made it through check_lineups')
-
-            st.table(df['BaseName'].dropna())
-            st.table(cleaned_df)
-            st.table(ownership_df)
-            st.table(fpts_df)
-            st.table(salary_df)
-            st.table(team_df)
-            st.table(pos_df)
-            st.table(check_lineups)
             
             # Get unique entry names
             entry_list = list(set(df['BaseName'].dropna()))