Update app.py to modify column names for DraftKings and FanDuel lineups, enhancing data structure for player positions and improving data retrieval in init_DK_lineups and init_FD_lineups functions.

app.py

@@ -23,8 +23,8 @@ game_format = {'Win Percentage': '{:.2%}','First Inning Lead Percentage': '{:.2%
 player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}',
                    '4x%': '{:.2%}'}
 
-dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']
-fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']
+dk_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 st.markdown("""
 <style>
@@ -88,69 +88,140 @@ def init_baselines():
 
 @st.cache_data(ttl = 60)
 def init_DK_lineups(type_var, slate_var):  
-        
-        if type_var == 'Regular':
-            if slate_var == 'Main':
-                collection = db['DK_MLB_seed_frame']
-                cursor = collection.find().limit(10000)
-            elif slate_var == 'Secondary':
-                collection = db['DK_MLB_Secondary_seed_frame']
-                cursor = collection.find().limit(10000)
-            elif slate_var == 'Auxiliary':
-                collection = db['DK_MLB_Turbo_seed_frame']
-                cursor = collection.find().limit(10000)
+    
+    if type_var == 'Regular':
+        if slate_var == 'Main':
+            collection = db['DK_MLB_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
+            collection = db['DK_MLB_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
+            # Map names
+            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+        elif slate_var == 'Secondary':
+            collection = db['DK_MLB_Secondary_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
             
+            collection = db['DK_MLB_Secondary_seed_frame']
+            cursor = collection.find().limit(10000)
+
             raw_display = pd.DataFrame(list(cursor))
-            raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Own']]
+            raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
+            # Map names
+            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+        elif slate_var == 'Auxiliary':
+            collection = db['DK_MLB_Turbo_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+            
+            collection = db['DK_MLB_Turbo_seed_frame']
+            cursor = collection.find().limit(10000)
 
-        elif type_var == 'Showdown':
-            if slate_var == 'Main':
-                collection = db2['DK_MLB_SD1_seed_frame']
-                cursor = collection.find().limit(10000)
-            elif slate_var == 'Secondary':
-                collection = db2['DK_MLB_SD2_seed_frame']
-                cursor = collection.find().limit(10000)
-            elif slate_var == 'Auxiliary':
-                collection = db2['DK_MLB_SD3_seed_frame']
-                cursor = collection.find().limit(10000)
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['SP1', 'SP2', 'C', '1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
+            # Map names
+            raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
+    elif type_var == 'Showdown':
+        if slate_var == 'Main':
+            collection = db2['DK_MLB_SD1_seed_frame']
+            cursor = collection.find().limit(10000)
 
             raw_display = pd.DataFrame(list(cursor))
             raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']]
-    
-        DK_seed = raw_display.to_numpy()
+        elif slate_var == 'Secondary':
+            collection = db2['DK_MLB_SD2_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']]
+        elif slate_var == 'Auxiliary':
+            collection = db2['DK_MLB_SD3_seed_frame']
+            cursor = collection.find().limit(10000)
+
+            raw_display = pd.DataFrame(list(cursor))
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Own']]
+
+    DK_seed = raw_display.to_numpy()
 
-        return DK_seed
+    return DK_seed
 
 @st.cache_data(ttl = 60)
 def init_FD_lineups(type_var,slate_var):  
         
         if type_var == 'Regular':
             if slate_var == 'Main':
+                collection = db['FD_MLB_name_map']
+                cursor = collection.find()
+                raw_data = pd.DataFrame(list(cursor))
+                names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
                 collection = db['FD_MLB_seed_frame']
                 cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Own']]
+                dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
+                # Map names
+                raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
             elif slate_var == 'Secondary':
+                collection = db['FD_MLB_Secondary_name_map']
+                cursor = collection.find()
+                raw_data = pd.DataFrame(list(cursor))
+                names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
                 collection = db['FD_MLB_Secondary_seed_frame']
                 cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Own']]
+                dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
+                # Map names
+                raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
             elif slate_var == 'Auxiliary':
+                collection = db['FD_MLB_Turbo_name_map']
+                cursor = collection.find()
+                raw_data = pd.DataFrame(list(cursor))
+                names_dict = dict(zip(raw_data['key'], raw_data['value']))
+
                 collection = db['FD_MLB_Turbo_seed_frame']
                 cursor = collection.find().limit(10000)
             
-            raw_display = pd.DataFrame(list(cursor))
-            raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Own']]
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Own']]
+                dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3']
+                # Map names
+                raw_display[dict_columns] = raw_display[dict_columns].apply(lambda x: x.map(names_dict))
 
         elif type_var == 'Showdown':
             if slate_var == 'Main':
                 collection = db2['FD_MLB_SD1_seed_frame']
                 cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
             elif slate_var == 'Secondary':
                 collection = db2['FD_MLB_SD2_seed_frame']
                 cursor = collection.find().limit(10000)
+
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
             elif slate_var == 'Auxiliary':
                 collection = db2['FD_MLB_SD3_seed_frame']
                 cursor = collection.find().limit(10000)
             
-            raw_display = pd.DataFrame(list(cursor))
-            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
+                raw_display = pd.DataFrame(list(cursor))
+                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'salary', 'proj', 'Own']]
     
         FD_seed = raw_display.to_numpy()