Refactor init_baselines function to accept type_var parameter for improved data handling in Streamlit app; enhance lineup initialization by adding type selection for Regular and Showdown data, and implement duplicate removal in lineup dataframes for better accuracy.

src/streamlit_app.py

@@ -139,7 +139,7 @@ def init_handbuilder_data(site_var):
         return load_display.dropna(subset=['Median'])
 
 @st.cache_resource(ttl=60)
-def init_baselines():
+def init_baselines(type_var: str):
 
     collection = db["Player_Baselines"] 
     cursor = collection.find()
@@ -150,71 +150,84 @@ def init_baselines():
     raw_display['name'] = raw_display['name'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['name'])
     player_stats = raw_display[raw_display['Position'] != 'K']
 
-    collection = db["DK_NFL_ROO"] 
-    cursor = collection.find()
+    if type_var == 'Regular':
+        collection = db["DK_NFL_ROO"] 
+        cursor = collection.find()
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
-    raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
-    load_display = raw_display[raw_display['Position'] != 'K']
-    dk_roo_raw = load_display.dropna(subset=['Median'])
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
+        load_display = raw_display[raw_display['Position'] != 'K']
+        dk_roo_raw = load_display.dropna(subset=['Median'])
 
-    dk_id_map = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
+        dk_id_map = dict(zip(dk_roo_raw['Player'], dk_roo_raw['player_id']))
 
-    collection = db["FD_NFL_ROO"] 
-    cursor = collection.find()
+        collection = db["FD_NFL_ROO"] 
+        cursor = collection.find()
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
-    raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
-    load_display = raw_display[raw_display['Position'] != 'K']
-    fd_roo_raw = load_display.dropna(subset=['Median'])
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
+        load_display = raw_display[raw_display['Position'] != 'K']
+        fd_roo_raw = load_display.dropna(subset=['Median'])
 
-    fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
+        fd_id_map = dict(zip(fd_roo_raw['Player'], fd_roo_raw['player_id']))
 
-    collection = db["DK_SD_NFL_ROO"] 
-    cursor = collection.find()
+        collection = db["DK_DFS_Stacks"] 
+        cursor = collection.find()
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
-    raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
-    # load_display = raw_display[raw_display['Position'] != 'K']
-    dk_sd_roo_raw = raw_display.dropna(subset=['Median'])
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate']]
+        dk_stacks_raw = raw_display.copy()
 
-    dk_sd_id_map = dict(zip(dk_sd_roo_raw['Player'], dk_sd_roo_raw['player_id']))
+        collection = db["FD_DFS_Stacks"] 
+        cursor = collection.find()
 
-    collection = db["FD_SD_NFL_ROO"] 
-    cursor = collection.find()
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate']]
+        fd_stacks_raw = raw_display.copy()
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
-    raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
-                               'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
-    raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
-    # load_display = raw_display[raw_display['Position'] != 'K']
-    fd_sd_roo_raw = raw_display.dropna(subset=['Median'])
+        dk_sd_roo_raw = None
+        fd_sd_roo_raw = None
+        dk_sd_id_map = None
+        fd_sd_id_map = None
+    elif type_var == 'Showdown':
+        collection = db["DK_SD_NFL_ROO"] 
+        cursor = collection.find()
 
-    fd_sd_id_map = dict(zip(fd_sd_roo_raw['Player'], fd_sd_roo_raw['player_id']))
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
+        # load_display = raw_display[raw_display['Position'] != 'K']
+        dk_sd_roo_raw = raw_display.dropna(subset=['Median'])
 
-    collection = db["DK_DFS_Stacks"] 
-    cursor = collection.find()
+        dk_sd_id_map = dict(zip(dk_sd_roo_raw['Player'], dk_sd_roo_raw['player_id']))
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate']]
-    dk_stacks_raw = raw_display.copy()
+        collection = db["FD_SD_NFL_ROO"] 
+        cursor = collection.find()
 
-    collection = db["FD_DFS_Stacks"] 
-    cursor = collection.find()
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display.rename(columns={'player_ID': 'player_id'})
+        raw_display = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                                'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+        raw_display['Player'] = raw_display['Player'].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display['Player'])
+        # load_display = raw_display[raw_display['Position'] != 'K']
+        fd_sd_roo_raw = raw_display.dropna(subset=['Median'])
 
-    raw_display = pd.DataFrame(list(cursor))
-    raw_display = raw_display[['Team', 'QB', 'WR1_TE', 'WR2_TE', 'Total', 'Salary', 'Floor', 'Median', 'exFPTS', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '60+%', '2x%', '3x%', '4x%', 'Own', 'LevX', 'slate']]
-    fd_stacks_raw = raw_display.copy()
+        fd_sd_id_map = dict(zip(fd_sd_roo_raw['Player'], fd_sd_roo_raw['player_id']))
+
+        dk_roo_raw = None
+        fd_roo_raw = None
+        dk_id_map = None
+        fd_id_map = None
+        dk_stacks_raw = None
+        fd_stacks_raw = None
 
     return player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map
 
@@ -259,6 +272,8 @@ def init_DK_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
             else:
                 cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                 raw_display = pd.DataFrame(list(cursor))
+            
+            raw_display = raw_display.drop_duplicates(subset=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
 
             raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
@@ -299,6 +314,8 @@ def init_DK_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
                 cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                 raw_display = pd.DataFrame(list(cursor))
 
+            raw_display = raw_display.drop_duplicates(subset=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
+
             raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
             # Map names
@@ -338,6 +355,8 @@ def init_DK_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
                 cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                 raw_display = pd.DataFrame(list(cursor))
 
+            raw_display = raw_display.drop_duplicates(subset=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
+
             raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
             # Map names
@@ -371,6 +390,8 @@ def init_DK_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
             cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
+        raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
+
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         for column in ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']:
             raw_display[column] = raw_display[column].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display[column])
@@ -422,6 +443,8 @@ def init_FD_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
                 cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                 raw_display = pd.DataFrame(list(cursor))
 
+            raw_display = raw_display.drop_duplicates(subset=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
+
             raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
             # Map names
@@ -461,6 +484,8 @@ def init_FD_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
                 cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                 raw_display = pd.DataFrame(list(cursor))
 
+            raw_display = raw_display.drop_duplicates(subset=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
+
             raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
             # Map names
@@ -500,6 +525,8 @@ def init_FD_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
                 cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
                 raw_display = pd.DataFrame(list(cursor))
 
+            raw_display = raw_display.drop_duplicates(subset=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST'])
+
             raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
             dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
             # Map names
@@ -534,6 +561,8 @@ def init_FD_lineups(type_var, slate_var, prio_var, prio_mix, db_translation, lin
             cursor = collection.find().sort(prio_var, -1).limit(lineup_num)
             raw_display = pd.DataFrame(list(cursor))
 
+        raw_display = raw_display.drop_duplicates(subset=['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5'])
+
         raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         for column in ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']:
             raw_display[column] = raw_display[column].map(dict(zip(wrong_team_names, right_name_teams)), na_action='ignore').fillna(raw_display[column])
@@ -564,24 +593,26 @@ def convert_hb_df(array, column_names):
 slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
 slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
 
-app_load_reset_column, app_view_site_column = st.columns([1, 9])
+app_load_reset_column, app_view_site_column, = st.columns([1, 9])
 with app_load_reset_column:
     if st.button("Load/Reset Data", key='reset_data_button'):
         st.cache_data.clear()
         slate_names_dk, slate_name_lookup_dk = define_dk_showdown_slates()
         slate_names_fd, slate_name_lookup_fd = define_fd_showdown_slates()
-        player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines()
+        player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines('Regular')
         dk_lineups = init_DK_lineups('Regular', 'Main Slate', 'proj', 50, dk_showdown_db_translation, 25000, [])
         fd_lineups = init_FD_lineups('Regular', 'Main Slate', 'proj', 50, fd_showdown_db_translation, 25000, [])
         for key in st.session_state.keys():
             del st.session_state[key]
 with app_view_site_column:
     with st.container():
-        app_view_column, app_site_column = st.columns([3, 3])
+        app_view_column, app_site_column, app_type_column = st.columns([3, 3, 3])
         with app_view_column:
             view_var = st.selectbox("Select view", ["Simple", "Advanced"], key='view_selectbox')
         with app_site_column:
             site_var = st.selectbox("What site do you want to view?", ('Draftkings', 'Fanduel'), key='site_selectbox')
+        with app_type_column:
+            type_var = st.selectbox("What type of data do you want to view?", ('Regular', 'Showdown'), key='type_selectbox')
 
 selected_tab = st.segmented_control(
     "Select Tab",
@@ -1020,8 +1051,11 @@ if selected_tab == 'Handbuilder':
 if selected_tab == 'Stacks ROO':
 
     print(st.session_state)
-    player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines()
-    t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+    player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines(type_var)
+    if type_var == 'Regular':
+        t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+    elif type_var == 'Showdown':
+        t_stamp = f"Last Update: " + str(dk_sd_roo_raw['timestamp'][0]) + f" CST"
     with st.expander("Info and Filters"):
         st.info(t_stamp)
         with st.container():
@@ -1059,26 +1093,32 @@ if selected_tab == 'Stacks ROO':
 if selected_tab == 'Player ROO':
 
     print(st.session_state)
-    player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines()
-    t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+    try:
+        player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines(type_var)
+    except:
+        st.error("No data found for this type of data, change the data type to either Regular or Showdown (located top right)")
+        st.stop()
+    if type_var == 'Regular':
+        t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+    elif type_var == 'Showdown':
+        t_stamp = f"Last Update: " + str(dk_sd_roo_raw['timestamp'][0]) + f" CST"
     with st.expander("Info and Filters"):
         st.info(t_stamp)
-        slate_type_var2 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var2_radio')
-        if slate_type_var2 == 'Regular':
+        if type_var == 'Regular':
             slate_var2 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary Slate', 'Late Slate', 'Thurs-Mon Slate'), key='slate_var2_radio')
-        elif slate_type_var2 == 'Showdown':
+        elif type_var == 'Showdown':
             slate_var2 = st.radio("Which slate are you loading?", (slate_names_dk if site_var == 'Draftkings' else slate_names_fd), key='slate_var2_radio')
         if site_var == 'Draftkings':
-            if slate_type_var2 == 'Regular':
+            if type_var == 'Regular':
                 raw_baselines = dk_roo_raw[dk_roo_raw['slate'] == str(slate_var2)]
-            elif slate_type_var2 == 'Showdown':
+            elif type_var == 'Showdown':
                 raw_baselines = dk_sd_roo_raw[dk_sd_roo_raw['slate'] == str(slate_name_lookup_dk[slate_var2])]
             
             raw_baselines = raw_baselines.iloc[:,:-2]
         elif site_var == 'Fanduel':
-            if slate_type_var2 == 'Regular':
+            if type_var == 'Regular':
                 raw_baselines = fd_roo_raw[fd_roo_raw['slate'] == str(slate_var2)]
-            elif slate_type_var2 == 'Showdown':
+            elif type_var == 'Showdown':
                 raw_baselines = fd_sd_roo_raw[fd_sd_roo_raw['slate'] == str(slate_name_lookup_fd[slate_var2])]
             raw_baselines = raw_baselines.iloc[:,:-2]
 
@@ -1118,11 +1158,11 @@ if selected_tab == 'Player ROO':
     final_Proj = final_Proj[final_Proj['Salary'] >= sal_var2[0]]
     final_Proj = final_Proj[final_Proj['Salary'] <= sal_var2[1]]
 
-    if slate_type_var2 == 'Regular':
+    if type_var == 'Regular':
         pm_export = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own']]
         pm_export['captain ownership'] = pm_export['Own'] / 6
         pm_export = pm_export.rename(columns={'Own': 'ownership', 'Median': 'median', 'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary'})
-    elif slate_type_var2 == 'Showdown':
+    elif type_var == 'Showdown':
         pm_export = final_Proj[['Player', 'Position', 'Team', 'Salary', 'Median', 'Own', 'CPT_Own']]
         pm_export['Salary'] = pm_export['Salary'] / 1.5
         pm_export = pm_export.rename(columns={'Own': 'ownership', 'Median': 'median', 'Player': 'player_names', 'Position': 'position', 'Team': 'team', 'Salary': 'salary', 'CPT_Own': 'captain ownership'})
@@ -1162,8 +1202,11 @@ if selected_tab == 'Player ROO':
     st.stop()
 
 if selected_tab == 'Optimals':
-    player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines()
-    t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+    player_stats, dk_stacks_raw, fd_stacks_raw, dk_roo_raw, fd_roo_raw, dk_sd_roo_raw, fd_sd_roo_raw, dk_id_map, fd_id_map, dk_sd_id_map, fd_sd_id_map = init_baselines(type_var)
+    if type_var == 'Regular':
+        t_stamp = f"Last Update: " + str(dk_roo_raw['timestamp'][0]) + f" CST"
+    elif type_var == 'Showdown':
+        t_stamp = f"Last Update: " + str(dk_sd_roo_raw['timestamp'][0]) + f" CST"
     st.header("Optimals")
     with st.expander("Info and Filters"):
         st.info("These filters will display various optimals in the table below to pick from. If you want to export the entire set of 10,000 optimals, hit the 'Prepare full data export' button. If you would like to apply the filters here to the 10,000 optimals before you export, use the 'Prepare full data export (Filter)' button.")
@@ -1175,36 +1218,35 @@ if selected_tab == 'Optimals':
             lineup_num = st.number_input("How many lineups do you want to work with?", min_value=1000, max_value=50000, value=25000, step=100, key='lineup_download_var_input')
 
         with optimals_site_col:
-            slate_type_var3 = st.radio("Which slate type are you loading?", ('Regular', 'Showdown'), key='slate_type_var3_radio')
-            if slate_type_var3 == 'Regular':
+            if type_var == 'Regular':
                 if site_var == 'Draftkings':
                     raw_baselines = dk_roo_raw
                 elif site_var == 'Fanduel':
                     raw_baselines = fd_roo_raw
-            elif slate_type_var3 == 'Showdown':
+            elif type_var == 'Showdown':
                 if site_var == 'Draftkings':
                     raw_baselines = dk_sd_roo_raw
                 elif site_var == 'Fanduel':
                     raw_baselines = fd_sd_roo_raw
             if site_var == 'Draftkings':
-                slate_var3 = st.radio("Which slate data are you loading?", (slate_names_dk if slate_type_var3 == 'Showdown' else ['Main', 'Secondary', 'Auxiliary']), key='slate_var3_radio')
+                slate_var3 = st.radio("Which slate data are you loading?", (slate_names_dk if type_var == 'Showdown' else ['Main', 'Secondary', 'Auxiliary']), key='slate_var3_radio')
             elif site_var == 'Fanduel':
-                slate_var3 = st.radio("Which slate data are you loading?", (slate_names_fd if slate_type_var3 == 'Showdown' else ['Main', 'Secondary', 'Auxiliary']), key='slate_var3_radio')
+                slate_var3 = st.radio("Which slate data are you loading?", (slate_names_fd if type_var == 'Showdown' else ['Main', 'Secondary', 'Auxiliary']), key='slate_var3_radio')
             
         with optimals_macro_col:
             lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1, key='lineup_num_var_input')
             player_var2 = st.multiselect('Query for lineups including:', options = raw_baselines['Player'].unique(), key='player_var2_multiselect', default=[])
         
-        if slate_type_var3 == 'Regular':
+        if type_var == 'Regular':
             if site_var == 'Draftkings':
-                dk_lineups = init_DK_lineups(slate_type_var3, slate_var3, prio_var, prio_mix, dk_showdown_db_translation, lineup_num, player_var2)
+                dk_lineups = init_DK_lineups(type_var, slate_var3, prio_var, prio_mix, dk_showdown_db_translation, lineup_num, player_var2)
             elif site_var == 'Fanduel':
-                fd_lineups = init_FD_lineups(slate_type_var3, slate_var3, prio_var, prio_mix, fd_showdown_db_translation, lineup_num, player_var2)
-        elif slate_type_var3 == 'Showdown':
+                fd_lineups = init_FD_lineups(type_var, slate_var3, prio_var, prio_mix, fd_showdown_db_translation, lineup_num, player_var2)
+        elif type_var == 'Showdown':
             if site_var == 'Draftkings':
-                dk_lineups = init_DK_lineups(slate_type_var3, slate_name_lookup_dk[slate_var3], prio_var, prio_mix, dk_showdown_db_translation, lineup_num, player_var2)
+                dk_lineups = init_DK_lineups(type_var, slate_name_lookup_dk[slate_var3], prio_var, prio_mix, dk_showdown_db_translation, lineup_num, player_var2)
             elif site_var == 'Fanduel':
-                fd_lineups = init_FD_lineups(slate_type_var3, slate_name_lookup_fd[slate_var3], prio_var, prio_mix, fd_showdown_db_translation, lineup_num, player_var2)
+                fd_lineups = init_FD_lineups(type_var, slate_name_lookup_fd[slate_var3], prio_var, prio_mix, fd_showdown_db_translation, lineup_num, player_var2)
 
         with optimals_salary_col:
             if site_var == 'Draftkings':
@@ -1223,23 +1265,21 @@ if selected_tab == 'Optimals':
         
         
         if site_var == 'Draftkings':
-            raw_baselines = dk_roo_raw
-            if slate_type_var3 == 'Regular':
+            if type_var == 'Regular':
                 ROO_slice = raw_baselines
                 player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
                 column_names = dk_columns
-            elif slate_type_var3 == 'Showdown':
+            elif type_var == 'Showdown':
                 player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                 column_names = dk_sd_columns
             
                     
         elif site_var == 'Fanduel':
-            raw_baselines = fd_roo_raw
-            if slate_type_var3 == 'Regular':
+            if type_var == 'Regular':
                 ROO_slice = raw_baselines
                 player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary']))
                 column_names = fd_columns
-            elif slate_type_var3 == 'Showdown':
+            elif type_var == 'Showdown':
                 player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary']))
                 column_names = fd_sd_columns
 
@@ -1249,20 +1289,20 @@ if selected_tab == 'Optimals':
                 name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 if site_var == 'Draftkings':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(dk_id_map)
-                    elif slate_type_var3 == 'Showdown':
+                    elif type_var == 'Showdown':
                         map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(dk_sd_id_map)
                 elif site_var == 'Fanduel':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_id_map)
-                    elif slate_type_var3 == 'Showdown':
+                    elif type_var == 'Showdown':
                         map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
                         for col_idx in map_columns:
                             data_export[col_idx] = data_export[col_idx].map(fd_sd_id_map)
@@ -1284,14 +1324,14 @@ if selected_tab == 'Optimals':
                     )
                 with pm_opt_col:
                     if site_var == 'Draftkings':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
                         label="Portfolio Manager Export (IDs)",
@@ -1302,14 +1342,14 @@ if selected_tab == 'Optimals':
                     )
                     
                     if site_var == 'Draftkings':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
                         label="Portfolio Manager Export (Names)",
@@ -1323,19 +1363,25 @@ if selected_tab == 'Optimals':
                 name_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 data_export = pd.DataFrame(st.session_state.working_seed.copy(), columns=column_names)
                 if site_var == 'Draftkings':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-                    elif slate_type_var3 == 'Showdown':
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(dk_id_map)
+                    elif type_var == 'Showdown':
                         map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-                    for col_idx in map_columns:
-                        data_export[col_idx] = data_export[col_idx].map(dk_id_map)
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(dk_sd_id_map)
+                    
                 elif site_var == 'Fanduel':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-                    elif slate_type_var3 == 'Showdown':
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(fd_id_map)
+                    elif type_var == 'Showdown':
                         map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-                    for col_idx in map_columns:
-                        data_export[col_idx] = data_export[col_idx].map(fd_id_map)
+                        for col_idx in map_columns:
+                            data_export[col_idx] = data_export[col_idx].map(fd_sd_id_map)
+                    
                 data_export = data_export[data_export['salary'] >= salary_min_var]
                 data_export = data_export[data_export['salary'] <= salary_max_var]
                 data_export = data_export[data_export['Team_count'] >= min_stacks_var]
@@ -1364,14 +1410,14 @@ if selected_tab == 'Optimals':
                     )
                 with pm_opt_col:
                     if site_var == 'Draftkings':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             data_export = data_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             data_export = data_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
                         label="Portfolio Manager Export (IDs)",
@@ -1382,14 +1428,14 @@ if selected_tab == 'Optimals':
                     )
                     
                     if site_var == 'Draftkings':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     elif site_var == 'Fanduel':
-                        if slate_type_var3 == 'Regular':
+                        if type_var == 'Regular':
                             name_export = name_export.set_index('QB').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
-                        elif slate_type_var3 == 'Showdown':
+                        elif type_var == 'Showdown':
                             name_export = name_export.set_index('CPT').drop(columns=['salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own'], axis=1)
                     st.download_button(
                         label="Portfolio Manager Export (Names)",
@@ -1439,19 +1485,23 @@ if selected_tab == 'Optimals':
     export_file = st.session_state.data_export_display.copy()
     name_export = st.session_state.data_export_display.copy()
     if site_var == 'Draftkings':
-        if slate_type_var3 == 'Regular':
+        if type_var == 'Regular':
             map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-        elif slate_type_var3 == 'Showdown':
+            for col_idx in map_columns:
+                export_file[col_idx] = export_file[col_idx].map(dk_id_map)
+        elif type_var == 'Showdown':
             map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-        for col_idx in map_columns:
-            export_file[col_idx] = export_file[col_idx].map(dk_id_map)
+            for col_idx in map_columns:
+                export_file[col_idx] = export_file[col_idx].map(dk_sd_id_map)
     elif site_var == 'Fanduel':
-        if slate_type_var3 == 'Regular':
+        if type_var == 'Regular':
             map_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
-        elif slate_type_var3 == 'Showdown':
+            for col_idx in map_columns:
+                export_file[col_idx] = export_file[col_idx].map(fd_id_map)
+        elif type_var == 'Showdown':
             map_columns = ['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5']
-        for col_idx in map_columns:
-            export_file[col_idx] = export_file[col_idx].map(fd_id_map)
+            for col_idx in map_columns:
+                export_file[col_idx] = export_file[col_idx].map(fd_sd_id_map)
             
     with st.container():
         if st.button("Reset Optimals", key='reset_optimals_button'):
@@ -1479,7 +1529,7 @@ if selected_tab == 'Optimals':
         )
     
     with st.container():
-        if slate_type_var3 == 'Regular':
+        if type_var == 'Regular':
             if 'working_seed' in st.session_state:
                 # Create a new dataframe with summary statistics
                 if site_var == 'Draftkings':
@@ -1526,7 +1576,7 @@ if selected_tab == 'Optimals':
                             np.std(st.session_state.working_seed[:,15])
                         ]
                     })
-        elif slate_type_var3 == 'Showdown':
+        elif type_var == 'Showdown':
             if 'working_seed' in st.session_state:
                 # Create a new dataframe with summary statistics
                 if site_var == 'Draftkings':
@@ -1590,14 +1640,14 @@ if selected_tab == 'Optimals':
         with display_freq_tab:
             if 'data_export_display' in st.session_state:
                 if site_var == 'Draftkings':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         player_columns = st.session_state.data_export_display.iloc[:, :9]
-                    elif slate_type_var3 == 'Showdown':
+                    elif type_var == 'Showdown':
                         player_columns = st.session_state.data_export_display.iloc[:, :6]
                 elif site_var == 'Fanduel':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         player_columns = st.session_state.data_export_display.iloc[:, :9]
-                    elif slate_type_var3 == 'Showdown':
+                    elif type_var == 'Showdown':
                         player_columns = st.session_state.data_export_display.iloc[:, :6]
                 
                 # Flatten the DataFrame and count unique values
@@ -1633,14 +1683,14 @@ if selected_tab == 'Optimals':
         with seed_frame_freq_tab:
             if 'working_seed' in st.session_state:
                 if site_var == 'Draftkings':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         player_columns = st.session_state.working_seed[:, :9]
-                    elif slate_type_var3 == 'Showdown':
+                    elif type_var == 'Showdown':
                         player_columns = st.session_state.working_seed[:, :6]
                 elif site_var == 'Fanduel':
-                    if slate_type_var3 == 'Regular':
+                    if type_var == 'Regular':
                         player_columns = st.session_state.working_seed[:, :9]
-                    elif slate_type_var3 == 'Showdown':
+                    elif type_var == 'Showdown':
                         player_columns = st.session_state.working_seed[:, :6]
                 
                 # Flatten the DataFrame and count unique values