Enhance app.py to support both NBA and WNBA leagues: refactor lineup initialization functions to accept league as an argument, update column definitions for WNBA, and implement conditional logic for data retrieval and display, improving overall functionality and user experience.

app.py

@@ -19,10 +19,15 @@ def init_conn():
     
 db, wnba_db = init_conn()
 
-dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-dk_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
-fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', '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']
+dk_nba_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_nba_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_nba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+dk_wnba_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+dk_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_wnba_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_wnba_sd_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
 
@@ -173,23 +178,35 @@ def load_overall_stats(league: str):
     return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp
 
 @st.cache_data(ttl = 60)
-def init_DK_lineups(slate_desig: str):  
+def init_DK_lineups(slate_desig: str, league: str):  
         
         if slate_desig == 'Main Slate':
-            collection = db['DK_NBA_name_map']
+            if league == 'NBA':
+                collection = db['DK_NBA_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['DK_WNBA_name_map']
             cursor = collection.find()
             raw_data = pd.DataFrame(list(cursor))
             names_dict = dict(zip(raw_data['key'], raw_data['value']))
-        
-            collection = db["DK_NBA_seed_frame"] 
+
+            if league == 'NBA':
+                collection = db["DK_NBA_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Secondary':
-            collection = db['DK_NBA_Secondary_name_map']
+            if league == 'NBA':
+                collection = db['DK_NBA_Secondary_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['DK_WNBA_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_NBA_Secondary_seed_frame"] 
+
+            if league == 'NBA':
+                collection = db["DK_NBA_Secondary_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_Secondary_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Auxiliary':
             collection = db['DK_NBA_Auxiliary_name_map']
@@ -201,8 +218,13 @@ def init_DK_lineups(slate_desig: str):
             cursor = collection.find().limit(10000) 
 
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        if league == 'NBA':
+            raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
+        elif league == 'WNBA':
+            raw_display = raw_display[['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['G1', 'G2', 'F1', 'F2', 'F3', 'UTIL']
+
         for col in dict_columns:
             raw_display[col] = raw_display[col].map(names_dict)
         DK_seed = raw_display.to_numpy()
@@ -210,12 +232,18 @@ def init_DK_lineups(slate_desig: str):
         return DK_seed
 
 @st.cache_data(ttl = 60)
-def init_DK_SD_lineups(slate_desig: str):
+def init_DK_SD_lineups(slate_desig: str, league: str):
 
         if slate_desig == 'Main Slate':
-            collection = db["DK_NBA_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["DK_NBA_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_SD_seed_frame"] 
         elif slate_desig == 'Secondary':
-            collection = db["DK_NBA_Secondary_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["DK_NBA_Secondary_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["DK_WNBA_Secondary_SD_seed_frame"] 
         elif slate_desig == 'Auxiliary':
             collection = db["DK_NBA_Auxiliary_SD_seed_frame"] 
 
@@ -228,23 +256,35 @@ def init_DK_SD_lineups(slate_desig: str):
         return DK_seed
 
 @st.cache_data(ttl = 60)
-def init_FD_lineups(slate_desig: str):  
+def init_FD_lineups(slate_desig: str, league: str):  
         
         if slate_desig == 'Main Slate':
-            collection = db['FD_NBA_name_map']
+            if league == 'NBA':
+                collection = db['FD_NBA_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['FD_WNBA_name_map']
             cursor = collection.find()
             raw_data = pd.DataFrame(list(cursor))
             names_dict = dict(zip(raw_data['key'], raw_data['value']))
         
-            collection = db["FD_NBA_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Secondary':
-            collection = db['FD_NBA_Secondary_name_map']
+            if league == 'NBA':
+                collection = db['FD_NBA_Secondary_name_map']
+            elif league == 'WNBA':
+                collection = wnba_db['FD_WNBA_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_NBA_Secondary_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_Secondary_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_Secondary_seed_frame"] 
             cursor = collection.find().limit(10000)
         elif slate_desig == 'Auxiliary':
             collection = db['FD_NBA_Auxiliary_name_map']
@@ -256,8 +296,12 @@ def init_FD_lineups(slate_desig: str):
             cursor = collection.find().limit(10000) 
     
         raw_display = pd.DataFrame(list(cursor))
-        raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
-        dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        if league == 'NBA':
+            raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
+        elif league == 'WNBA':
+            raw_display = raw_display[['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+            dict_columns = ['G1', 'G2', 'G3', 'F1', 'F2', 'F3', 'F4']
         for col in dict_columns:
             raw_display[col] = raw_display[col].map(names_dict)
         FD_seed = raw_display.to_numpy()
@@ -265,12 +309,18 @@ def init_FD_lineups(slate_desig: str):
         return FD_seed
 
 @st.cache_data(ttl = 60)
-def init_FD_SD_lineups(slate_desig: str):
+def init_FD_SD_lineups(slate_desig: str, league: str):
 
         if slate_desig == 'Main Slate':
-            collection = db["FD_NBA_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_SD_seed_frame"] 
         elif slate_desig == 'Secondary':
-            collection = db["FD_NBA_Secondary_SD_seed_frame"] 
+            if league == 'NBA':
+                collection = db["FD_NBA_Secondary_SD_seed_frame"] 
+            elif league == 'WNBA':
+                collection = wnba_db["FD_WNBA_Secondary_SD_seed_frame"] 
         elif slate_desig == 'Auxiliary':
             collection = db["FD_NBA_Auxiliary_SD_seed_frame"] 
 
@@ -297,10 +347,16 @@ salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
 dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
 fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
 
-dk_lineups = pd.DataFrame(columns=dk_columns)
-dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
-fd_lineups = pd.DataFrame(columns=fd_columns)
-fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
+dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
+dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
+fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
+fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
+
+dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
+dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
+fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
+fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
+
 t_stamp = f"Last Update: " + str(timestamp) + f" CST"
 
 tab1, tab2 = st.tabs(['Range of Outcomes', 'Optimals'])
@@ -324,10 +380,15 @@ with tab1:
                     salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
                     dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
                     fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
-                    dk_lineups = pd.DataFrame(columns=dk_columns)
-                    dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
-                    fd_lineups = pd.DataFrame(columns=fd_columns)
-                    fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
+                    dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
+                    dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
+                    fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
+                    fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
+
+                    dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
+                    dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
+                    fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
+                    fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
                     t_stamp = f"Last Update: " + str(timestamp) + f" CST"
                     for key in st.session_state.keys():
                         del st.session_state[key]
@@ -425,31 +486,44 @@ with tab2:
             salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
             dk_id_dict_sd = dict(zip(dk_sd_raw.Player, dk_sd_raw.player_ID))
             fd_id_dict_sd = dict(zip(fd_sd_raw.Player, fd_sd_raw.player_ID))
-            dk_lineups = pd.DataFrame(columns=dk_columns)
-            dk_sd_lineups = pd.DataFrame(columns=dk_sd_columns)
-            fd_lineups = pd.DataFrame(columns=fd_columns)
-            fd_sd_lineups = pd.DataFrame(columns=fd_sd_columns)
+            dk_nba_lineups = pd.DataFrame(columns=dk_nba_columns)
+            dk_nba_sd_lineups = pd.DataFrame(columns=dk_nba_sd_columns)
+            fd_nba_lineups = pd.DataFrame(columns=fd_nba_columns)
+            fd_nba_sd_lineups = pd.DataFrame(columns=fd_nba_sd_columns)
+
+            dk_wnba_lineups = pd.DataFrame(columns=dk_wnba_columns)
+            dk_wnba_sd_lineups = pd.DataFrame(columns=dk_wnba_sd_columns)
+            fd_wnba_lineups = pd.DataFrame(columns=fd_wnba_columns)
+            fd_wnba_sd_lineups = pd.DataFrame(columns=fd_wnba_sd_columns)
             t_stamp = f"Last Update: " + str(timestamp) + f" CST"
             for key in st.session_state.keys():
                 del st.session_state[key]
     
-        col1, col2, col3, col4, col5 = st.columns(5)
+        col1, col2, col3, col4, col5, col6 = st.columns(6)
         with col1:
-            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
+            league_var = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var')
         with col2:
+            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
+        with col3:
             site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
             if 'working_seed' in st.session_state:
                 del st.session_state['working_seed']
-        with col3:
-            slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
         with col4:
-            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+            slate_type_var1 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'))
         with col5:
+            lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1)
+        with col6:
             if site_var1 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    column_names = dk_columns
-                elif slate_type_var1 == 'Showdown':
-                    column_names = dk_sd_columns
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = dk_nba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = dk_nba_sd_columns
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = dk_wnba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = dk_wnba_sd_columns
                 
                 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
                 if player_var1 == 'Specific Players':
@@ -458,10 +532,16 @@ with tab2:
                         player_var2 = dk_raw.Player.values.tolist()
                         
             elif site_var1 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    column_names = fd_columns
-                elif slate_type_var1 == 'Showdown':
-                    column_names = fd_sd_columns
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = fd_nba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = fd_nba_sd_columns
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        column_names = fd_wnba_columns
+                    elif slate_type_var1 == 'Showdown':
+                        column_names = fd_wnba_sd_columns
                 
                 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
                 if player_var1 == 'Specific Players':
@@ -472,24 +552,24 @@ with tab2:
     
             if site_var1 == 'Draftkings':
                 if slate_type_var1 == 'Regular':
-                    data_export = init_DK_lineups(slate_var1)
+                    data_export = init_DK_lineups(slate_var1, league_var)
                     data_export_names = data_export.copy()
                     for col_idx in range(8):
                         data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
                 elif slate_type_var1 == 'Showdown':
-                    data_export = init_DK_SD_lineups(slate_var1)
+                    data_export = init_DK_SD_lineups(slate_var1, league_var)
                     data_export_names = data_export.copy()
                     for col_idx in range(6):
                         data_export[:, col_idx] = np.array([dk_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
                 
             elif site_var1 == 'Fanduel':
                 if slate_type_var1 == 'Regular':
-                    data_export = init_FD_lineups(slate_var1)
+                    data_export = init_FD_lineups(slate_var1, league_var)
                     data_export_names = data_export.copy()
                     for col_idx in range(9):
                         data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
                 elif slate_type_var1 == 'Showdown':
-                    data_export = init_FD_SD_lineups(slate_var1)
+                    data_export = init_FD_SD_lineups(slate_var1, league_var)
                     data_export_names = data_export.copy()
                     for col_idx in range(6):
                         data_export[:, col_idx] = np.array([fd_id_dict_sd.get(player, player) for player in data_export[:, col_idx]])
@@ -518,17 +598,17 @@ with tab2:
 
         elif 'working_seed' not in st.session_state:
             if slate_type_var1 == 'Regular':
-                st.session_state.working_seed = init_DK_lineups(slate_var1)
+                st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
             elif slate_type_var1 == 'Showdown':
-                st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
+                st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
             st.session_state.working_seed = st.session_state.working_seed
             if player_var1 == 'Specific Players':
                 st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
             elif player_var1 == 'Full Slate':
                 if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = init_DK_lineups(slate_var1)
+                    st.session_state.working_seed = init_DK_lineups(slate_var1, league_var)
                 elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = init_DK_SD_lineups(slate_var1)
+                    st.session_state.working_seed = init_DK_SD_lineups(slate_var1, league_var)
             st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
         
     elif site_var1 == 'Fanduel':
@@ -542,17 +622,17 @@ with tab2:
         
         elif 'working_seed' not in st.session_state:
             if slate_type_var1 == 'Regular':
-                st.session_state.working_seed = init_FD_lineups(slate_var1)
+                st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
             elif slate_type_var1 == 'Showdown':
-                st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
+                st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
             st.session_state.working_seed = st.session_state.working_seed
             if player_var1 == 'Specific Players':
                 st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)]
             elif player_var1 == 'Full Slate':
                 if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = init_FD_lineups(slate_var1)
+                    st.session_state.working_seed = init_FD_lineups(slate_var1, league_var)
                 elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = init_FD_SD_lineups(slate_var1)
+                    st.session_state.working_seed = init_FD_SD_lineups(slate_var1, league_var)
             st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names)
 
     export_file = st.session_state.data_export_display.copy()
@@ -576,15 +656,27 @@ with tab2:
             for key in st.session_state.keys():
                 del st.session_state[key]
             if site_var1 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = dk_lineups.copy()
-                elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = dk_sd_lineups.copy()
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = dk_nba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = dk_nba_sd_lineups.copy()
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = dk_wnba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = dk_wnba_sd_lineups.copy()
             elif site_var1 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    st.session_state.working_seed = fd_lineups.copy()
-                elif slate_type_var1 == 'Showdown':
-                    st.session_state.working_seed = fd_sd_lineups.copy()
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = fd_nba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = fd_nba_sd_lineups.copy()
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        st.session_state.working_seed = fd_wnba_lineups.copy()
+                    elif slate_type_var1 == 'Showdown':
+                        st.session_state.working_seed = fd_wnba_sd_lineups.copy()
         if 'data_export_display' in st.session_state:
             st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True)
         st.download_button(
@@ -598,96 +690,188 @@ with tab2:
         if 'working_seed' in st.session_state:
             # Create a new dataframe with summary statistics
             if site_var1 == 'Draftkings':
-                if slate_type_var1 == 'Regular':
-                    summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,8]),
-                            np.mean(st.session_state.working_seed[:,8]),
-                            np.max(st.session_state.working_seed[:,8]),
-                            np.std(st.session_state.working_seed[:,8])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,14]),
-                            np.mean(st.session_state.working_seed[:,14]),
-                            np.max(st.session_state.working_seed[:,14]),
-                            np.std(st.session_state.working_seed[:,14])
-                        ]
-                    })
-                elif slate_type_var1 == 'Showdown':
-                    summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,6]),
-                            np.mean(st.session_state.working_seed[:,6]),
-                            np.max(st.session_state.working_seed[:,6]),
-                            np.std(st.session_state.working_seed[:,6])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,7]),
-                            np.mean(st.session_state.working_seed[:,7]),
-                            np.max(st.session_state.working_seed[:,7]),
-                            np.std(st.session_state.working_seed[:,7])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,12]),
-                            np.mean(st.session_state.working_seed[:,12]),
-                            np.max(st.session_state.working_seed[:,12]),
-                            np.std(st.session_state.working_seed[:,12])
-                        ]
-                    })
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,8]),
+                                np.mean(st.session_state.working_seed[:,8]),
+                                np.max(st.session_state.working_seed[:,8]),
+                                np.std(st.session_state.working_seed[:,8])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,9]),
+                                np.mean(st.session_state.working_seed[:,9]),
+                                np.max(st.session_state.working_seed[:,9]),
+                                np.std(st.session_state.working_seed[:,9])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,14]),
+                                np.mean(st.session_state.working_seed[:,14]),
+                                np.max(st.session_state.working_seed[:,14]),
+                                np.std(st.session_state.working_seed[:,14])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,8]),
+                                np.mean(st.session_state.working_seed[:,8]),
+                                np.max(st.session_state.working_seed[:,8]),
+                                np.std(st.session_state.working_seed[:,8])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,13]),
+                                np.mean(st.session_state.working_seed[:,13]),
+                                np.max(st.session_state.working_seed[:,13]),
+                                np.std(st.session_state.working_seed[:,13])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
                 
             elif site_var1 == 'Fanduel':
-                if slate_type_var1 == 'Regular':
-                    summary_df = pd.DataFrame({
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                            'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,9]),
+                                np.mean(st.session_state.working_seed[:,9]),
+                                np.max(st.session_state.working_seed[:,9]),
+                                np.std(st.session_state.working_seed[:,9])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,10]),
+                                np.mean(st.session_state.working_seed[:,10]),
+                                np.max(st.session_state.working_seed[:,10]),
+                                np.std(st.session_state.working_seed[:,10])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,15]),
+                                np.mean(st.session_state.working_seed[:,15]),
+                                np.max(st.session_state.working_seed[:,15]),
+                                np.std(st.session_state.working_seed[:,15])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
                         'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,9]),
-                            np.mean(st.session_state.working_seed[:,9]),
-                            np.max(st.session_state.working_seed[:,9]),
-                            np.std(st.session_state.working_seed[:,9])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,10]),
-                            np.mean(st.session_state.working_seed[:,10]),
-                            np.max(st.session_state.working_seed[:,10]),
-                            np.std(st.session_state.working_seed[:,10])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,15]),
-                            np.mean(st.session_state.working_seed[:,15]),
-                            np.max(st.session_state.working_seed[:,15]),
-                            np.std(st.session_state.working_seed[:,15])
-                        ]
-                    })
-                elif slate_type_var1 == 'Showdown':
-                    summary_df = pd.DataFrame({
-                    'Metric': ['Min', 'Average', 'Max', 'STDdev'],
-                        'Salary': [
-                            np.min(st.session_state.working_seed[:,6]),
-                            np.mean(st.session_state.working_seed[:,6]),
-                            np.max(st.session_state.working_seed[:,6]),
-                            np.std(st.session_state.working_seed[:,6])
-                        ],
-                        'Proj': [
-                            np.min(st.session_state.working_seed[:,7]),
-                            np.mean(st.session_state.working_seed[:,7]),
-                            np.max(st.session_state.working_seed[:,7]),
-                            np.std(st.session_state.working_seed[:,7])
-                        ],
-                        'Own': [
-                            np.min(st.session_state.working_seed[:,12]),
-                            np.mean(st.session_state.working_seed[:,12]),
-                            np.max(st.session_state.working_seed[:,12]),
-                            np.std(st.session_state.working_seed[:,12])
-                        ]
-                    })
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        summary_df = pd.DataFrame({
+                            'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,8]),
+                                np.mean(st.session_state.working_seed[:,8]),
+                                np.max(st.session_state.working_seed[:,8]),
+                                np.std(st.session_state.working_seed[:,8])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,9]),
+                                np.mean(st.session_state.working_seed[:,9]),
+                                np.max(st.session_state.working_seed[:,9]),
+                                np.std(st.session_state.working_seed[:,9])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,14]),
+                                np.mean(st.session_state.working_seed[:,14]),
+                                np.max(st.session_state.working_seed[:,14]),
+                                np.std(st.session_state.working_seed[:,14])
+                            ]
+                        })
+                    elif slate_type_var1 == 'Showdown':
+                        summary_df = pd.DataFrame({
+                        'Metric': ['Min', 'Average', 'Max', 'STDdev'],
+                            'Salary': [
+                                np.min(st.session_state.working_seed[:,6]),
+                                np.mean(st.session_state.working_seed[:,6]),
+                                np.max(st.session_state.working_seed[:,6]),
+                                np.std(st.session_state.working_seed[:,6])
+                            ],
+                            'Proj': [
+                                np.min(st.session_state.working_seed[:,7]),
+                                np.mean(st.session_state.working_seed[:,7]),
+                                np.max(st.session_state.working_seed[:,7]),
+                                np.std(st.session_state.working_seed[:,7])
+                            ],
+                            'Own': [
+                                np.min(st.session_state.working_seed[:,12]),
+                                np.mean(st.session_state.working_seed[:,12]),
+                                np.max(st.session_state.working_seed[:,12]),
+                                np.std(st.session_state.working_seed[:,12])
+                            ]
+                        })
 
             # Set the index of the summary dataframe as the "Metric" column
             summary_df = summary_df.set_index('Metric')
@@ -704,16 +888,29 @@ with tab2:
         tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"])
         with tab1:
             if 'data_export_display' in st.session_state:
-                if slate_type_var1 == 'Regular':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.data_export_display.iloc[:, :8]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.data_export_display.iloc[:, :9]
-                elif slate_type_var1 == 'Showdown':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.data_export_display.iloc[:, :5]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.data_export_display.iloc[:, :5]
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :8]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :9]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :7]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :8]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.data_export_display.iloc[:, :5]
+                
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.values.flatten().tolist()
@@ -744,16 +941,28 @@ with tab2:
                 )
         with tab2:
             if 'working_seed' in st.session_state:
-                if slate_type_var1 == 'Regular':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.working_seed[:, :8]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.working_seed[:, :9]
-                elif slate_type_var1 == 'Showdown':
-                    if site_var1 == 'Draftkings':
-                        player_columns = st.session_state.working_seed[:, :5]
-                    elif site_var1 == 'Fanduel':
-                        player_columns = st.session_state.working_seed[:, :5]
+                if league_var == 'NBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :8]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :9]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :5]
+                elif league_var == 'WNBA':
+                    if slate_type_var1 == 'Regular':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :7]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :8]
+                    elif slate_type_var1 == 'Showdown':
+                        if site_var1 == 'Draftkings':
+                            player_columns = st.session_state.working_seed[:, :5]
+                        elif site_var1 == 'Fanduel':
+                            player_columns = st.session_state.working_seed[:, :5]
                 
                 # Flatten the DataFrame and count unique values
                 value_counts = player_columns.flatten().tolist()