Enhance app.py: add support for secondary slate data retrieval and processing, update overall stats function to include new data, and improve data export functionality for both Draftkings and Fanduel

app.py

@@ -19,7 +19,9 @@ def init_conn():
 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']
 
 roo_format = {'Top_finish': '{:.2%}', 'Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '4x%': '{:.2%}', '5x%': '{:.2%}', '6x%': '{:.2%}', 'GPP%': '{:.2%}'}
 
@@ -101,6 +103,16 @@ def load_overall_stats():
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     fd_raw_sec = raw_display.sort_values(by='Median', ascending=False)
 
+    collection = db["Player_SD_Range_Of_Outcomes"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    raw_display = raw_display[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%',
+                               'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX', 'site', 'version', 'slate', 'timestamp', 'player_ID']]
+    raw_display = raw_display.loc[raw_display['Median'] > 0]
+    raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
+    sd_raw = raw_display.sort_values(by='Median', ascending=False)
+
     collection = db["Player_Range_Of_Outcomes"] 
     cursor = collection.find()
 
@@ -113,7 +125,7 @@ def load_overall_stats():
 
     timestamp = raw_display['timestamp'].values[0]
     
-    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp
+    return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp
 
 @st.cache_data(ttl = 60)
 def init_DK_lineups(slate_desig: str):  
@@ -233,8 +245,11 @@ def convert_df(array):
     array = pd.DataFrame(array, columns=column_names)
     return array.to_csv().encode('utf-8')
 
-dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
+dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
 salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
+id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
+salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
+id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
 
 try:
     dk_lineups = init_DK_lineups('Main Slate')
@@ -259,10 +274,18 @@ with tab1:
             with col2:
                 if st.button("Load/Reset Data", key='reset1'):
                     st.cache_data.clear()
-                    dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
+                    dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
+                    salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
                     id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
-                    dk_lineups = init_DK_lineups('Main Slate')
-                    fd_lineups = init_FD_lineups('Main Slate')
+                    salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
+                    id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
+
+                    try:
+                        dk_lineups = init_DK_lineups('Main Slate')
+                        fd_lineups = init_FD_lineups('Main Slate')
+                    except:
+                        dk_lineups = pd.DataFrame(columns=dk_columns)
+                        fd_lineups = pd.DataFrame(columns=fd_columns)
                     t_stamp = f"Last Update: " + str(timestamp) + f" CST"
                     for key in st.session_state.keys():
                         del st.session_state[key]
@@ -338,28 +361,33 @@ with tab2:
     with st.expander("Info and Filters"):
         if st.button("Load/Reset Data", key='reset2'):
             st.cache_data.clear()
-            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
-            dk_lineups = init_DK_lineups('Main Slate')
-            fd_lineups = init_FD_lineups('Main Slate')
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, timestamp = load_overall_stats()
+            salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
+            id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
+            salary_dict_sd = dict(zip(sd_raw.Player, sd_raw.Salary))
+            id_dict_sd = dict(zip(sd_raw.Player, sd_raw.player_ID))
+
+            try:
+                dk_lineups = init_DK_lineups('Main Slate')
+                fd_lineups = init_FD_lineups('Main Slate')
+            except:
+                dk_lineups = pd.DataFrame(columns=dk_columns)
+                fd_lineups = pd.DataFrame(columns=fd_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)
         with col1:
-            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Just the Main Slate'))
+            slate_var1 = st.radio("Which data are you loading?", ('Main Slate', 'Secondary'))
         with col2:
             site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel'))
         with col3:
-            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 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)
+        with col5:
             if site_var1 == 'Draftkings':
-                raw_baselines = dk_raw
-                ROO_slice = roo_raw[roo_raw['site'] == 'Draftkings']
-                id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
-                # Get the minimum and maximum ownership values from dk_lineups
-                min_own = np.min(dk_lineups[:,14])
-                max_own = np.max(dk_lineups[:,14])
                 column_names = dk_columns
                 
                 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
@@ -369,11 +397,6 @@ with tab2:
                         player_var2 = dk_raw.Player.values.tolist()
                         
             elif site_var1 == 'Fanduel':
-                raw_baselines = fd_raw
-                ROO_slice = roo_raw[roo_raw['site'] == 'Fanduel']
-                id_dict = dict(zip(ROO_slice.Player, ROO_slice.player_ID))
-                min_own = np.min(fd_lineups[:,15])
-                max_own = np.max(fd_lineups[:,15])
                 column_names = fd_columns
                 
                 player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1')
@@ -381,21 +404,21 @@ with tab2:
                         player_var2 = st.multiselect('Which players do you want?', options = fd_raw['Player'].unique())
                 elif player_var1 == 'Full Slate':
                         player_var2 = fd_raw.Player.values.tolist()
-        with col5:
-            if st.button("Prepare data export", key='data_export'):
-                data_export = st.session_state.working_seed.copy()
-                if site_var1 == 'Draftkings':
-                    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 site_var1 == 'Fanduel':
-                    for col_idx in range(9):
-                        data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
-                st.download_button(
-                    label="Export optimals set",
-                    data=convert_df(data_export),
-                    file_name='NBA_optimals_export.csv',
-                    mime='text/csv',
-            )
+        if st.button("Prepare data export", key='data_export'):
+            data_export = st.session_state.working_seed.copy()
+            if site_var1 == 'Draftkings':
+                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 site_var1 == 'Fanduel':
+                for col_idx in range(9):
+                    data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]])
+            st.download_button(
+                label="Export optimals set",
+                data=convert_df(data_export),
+                file_name='NBA_optimals_export.csv',
+                mime='text/csv',
+        )
+            
 
     if site_var1 == 'Draftkings':
         if 'working_seed' in st.session_state: