Update Draftkings column definitions in app.py to ensure consistent inclusion of 'FLEX' across relevant data structures and queries.

app.py

@@ -27,9 +27,9 @@ db = init_conn()
 player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                    '4x%': '{:.2%}'}
 
-dk_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-dk_hb_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G']
+dk_hb_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']
 fd_hb_columns = ['C1', 'C2', 'W1', 'W2', 'D1', 'D2', 'FLEX1', 'FLEX2', 'G']
 dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
@@ -97,7 +97,7 @@ def init_DK_lineups(prio_var, prio_mix, lineup_num, player_var2):
     collection = db['DK_NHL_seed_frame']
     if prio_var == None:
         if player_var2 != []:
-            player_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G']
+            player_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']
             query_conditions = []
             
             for player in player_var2:
@@ -122,9 +122,9 @@ def init_DK_lineups(prio_var, prio_mix, lineup_num, player_var2):
         cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
         raw_display = pd.DataFrame(list(cursor))
     
-    raw_display = raw_display.drop_duplicates(subset=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G'])
+    raw_display = raw_display.drop_duplicates(subset=['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX'])
 
-    raw_display = raw_display[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    raw_display = raw_display[['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
 
     DK_seed = raw_display.to_numpy()
 
@@ -454,7 +454,7 @@ with tab4:
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 if site_var == 'Draftkings':
                     if type_var == 'Regular':
-                        map_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G']
+                        map_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(dk_id_map)
                     elif type_var == 'Showdown':
@@ -528,7 +528,7 @@ with tab4:
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 if site_var == 'Draftkings':
                     if type_var == 'Regular':
-                        map_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G']
+                        map_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(dk_id_map)
                     elif type_var == 'Showdown':
@@ -650,7 +650,7 @@ with tab4:
     name_export = st.session_state.data_export_display.copy()
     if site_var == 'Draftkings':
         if type_var == 'Regular':
-            map_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'FLEX', 'G']
+            map_columns = ['C1', 'C2', 'W1', 'W2', 'W3', 'D1', 'D2', 'G', 'FLEX']
             for col_idx in map_columns:
                 export_file[col_idx] = export_file[col_idx].map(dk_id_map)
         elif type_var == 'Showdown':