Refactor FD lineup data retrieval in app.py to streamline logic for handling priority variables and slate types. This update consolidates the data fetching process, ensuring consistent DataFrame creation and enhancing overall code readability and maintainability.

app.py

@@ -278,104 +278,107 @@ def init_DK_lineups(type_var, slate_var, prio_var, prio_mix):
 
 @st.cache_data(ttl = 60)
 def init_FD_lineups(type_var,slate_var, prio_var, prio_mix):  
-        
-        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']
-                if prio_var == None:
-                    cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
-                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
-                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
-                else:
-                    cursor = collection.find().sort(prio_var, -1).limit(10000)
-                    raw_display = pd.DataFrame(list(cursor))
-                
-                raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-                dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-                # 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']
-                if prio_var == None:
-                    cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
-                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
-                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
-                else:
-                    cursor = collection.find().sort(prio_var, -1).limit(10000)
-                    raw_display = pd.DataFrame(list(cursor))
-                
-                raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-                dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-                # 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']
-                if prio_var == None:
-                    cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
-                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
-                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
-                else:
-                    cursor = collection.find().sort(prio_var, -1).limit(10000)
-                    raw_display = pd.DataFrame(list(cursor))
-                
-                raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-                dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
-                # 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 = db['FD_MLB_SD1_seed_frame']
-                if prio_var == None:
-                    cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
-                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
-                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
-                else:
-                    cursor = collection.find().sort(prio_var, -1).limit(10000)
-                    raw_display = pd.DataFrame(list(cursor))
-                
-                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-            elif slate_var == 'Secondary':
-                collection = db['FD_MLB_SD2_seed_frame']
-                if prio_var == None:
-                    cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
-                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
-                    cursor = pd.concat([cursor1, cursor2])
-                else:
-                    cursor = collection.find().sort(prio_var, -1).limit(10000)
-                    raw_display = pd.DataFrame(list(cursor))
-                
-                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-            elif slate_var == 'Auxiliary':
-                collection = db['FD_MLB_SD3_seed_frame']
-                if prio_var == None:
-                    cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
-                    cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
-                    raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
-                else:
-                    cursor = collection.find().sort(prio_var, -1).limit(10000)
-                    raw_display = pd.DataFrame(list(cursor))
-                
-                raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+
+    if prio_var == 'Mix':
+        prio_var = None
     
-        FD_seed = raw_display.to_numpy()
+    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']
+            if prio_var == None:
+                cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
+                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
+                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
+            else:
+                cursor = collection.find().sort(prio_var, -1).limit(10000)
+                raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
+            # 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']
+            if prio_var == None:
+                cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
+                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
+                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
+            else:
+                cursor = collection.find().sort(prio_var, -1).limit(10000)
+                raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
+            # 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']
+            if prio_var == None:
+                cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
+                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
+                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
+            else:
+                cursor = collection.find().sort(prio_var, -1).limit(10000)
+                raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display[['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['P', 'C_1B', '2B', '3B', 'SS', 'OF1', 'OF2', 'OF3', 'UTIL']
+            # 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 = db['FD_MLB_SD1_seed_frame']
+            if prio_var == None:
+                cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
+                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
+                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
+            else:
+                cursor = collection.find().sort(prio_var, -1).limit(10000)
+                raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        elif slate_var == 'Secondary':
+            collection = db['FD_MLB_SD2_seed_frame']
+            if prio_var == None:
+                cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
+                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
+                cursor = pd.concat([cursor1, cursor2])
+            else:
+                cursor = collection.find().sort(prio_var, -1).limit(10000)
+                raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        elif slate_var == 'Auxiliary':
+            collection = db['FD_MLB_SD3_seed_frame']
+            if prio_var == None:
+                cursor1 = collection.find().limit(math.ceil(10000 * (prio_mix / 100)))
+                cursor2 = collection.find().sort('Own', -1).limit(math.ceil(10000 * ((100 - prio_mix) / 100)))
+                raw_display = pd.concat([pd.DataFrame(list(cursor1)), pd.DataFrame(list(cursor2))])
+            else:
+                cursor = collection.find().sort(prio_var, -1).limit(10000)
+                raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+
+    FD_seed = raw_display.to_numpy()
 
-        return FD_seed
+    return FD_seed
 
 @st.cache_data
 def convert_df_to_csv(df):