Add WNBA database support in app.py: modify connection initialization and update overall stats retrieval to handle both NBA and WNBA data, enhancing functionality for player statistics management.

app.py

@@ -13,10 +13,11 @@ def init_conn():
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
         db = client["NBA_DFS"]
+        wnba_db = client["WNBA_DFS"]
 
-        return db
+        return db, wnba_db
     
-db = 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']
@@ -58,8 +59,11 @@ st.markdown("""
 </style>""", unsafe_allow_html=True)
 
 @st.cache_data(ttl=60)
-def load_overall_stats():
-    collection = db["DK_Player_Stats"] 
+def load_overall_stats(league: str):
+    if league == 'NBA':
+        collection = db["DK_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["DK_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -70,7 +74,10 @@ def load_overall_stats():
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     dk_raw = raw_display.sort_values(by='Median', ascending=False)
     
-    collection = db["FD_Player_Stats"] 
+    if league == 'NBA':
+        collection = db["FD_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["FD_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -81,7 +88,10 @@ def load_overall_stats():
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     fd_raw = raw_display.sort_values(by='Median', ascending=False)
     
-    collection = db["Secondary_DK_Player_Stats"] 
+    if league == 'NBA':
+        collection = db["Secondary_DK_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Secondary_DK_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -92,7 +102,10 @@ def load_overall_stats():
     raw_display = raw_display.apply(pd.to_numeric, errors='ignore')
     dk_raw_sec = raw_display.sort_values(by='Median', ascending=False)
     
-    collection = db["Secondary_FD_Player_Stats"] 
+    if league == 'NBA':
+        collection = db["Secondary_FD_Player_Stats"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Secondary_FD_Player_Stats"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -103,7 +116,10 @@ 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"] 
+    if league == 'NBA':
+        collection = db["Player_SD_Range_Of_Outcomes"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Player_SD_Range_Of_Outcomes"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -119,7 +135,10 @@ def load_overall_stats():
 
     print(sd_raw.head(10))
 
-    collection = db["Player_Range_Of_Outcomes"] 
+    if league == 'NBA':
+        collection = db["Player_Range_Of_Outcomes"] 
+    elif league == 'WNBA':
+        collection = wnba_db["Player_Range_Of_Outcomes"] 
     cursor = collection.find()
 
     raw_display = pd.DataFrame(list(cursor))
@@ -251,7 +270,8 @@ 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, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats()
+league_var = 'NBA'
+dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
 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))
@@ -279,7 +299,8 @@ 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, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats()
+                    league_var = 'NBA'
+                    dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
                     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))
@@ -292,12 +313,14 @@ with tab1:
                     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:
             view_var2 = st.radio("View Type", ('Simple', 'Advanced'), key='view_var2')
         with col2:
-            slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
+            league_var = st.radio("What League to load:", ('NBA', 'WNBA'), key='league_var')
         with col3:
+            slate_type_var2 = st.radio("What slate type are you working with?", ('Regular', 'Showdown'), key='slate_type_var2')
+        with col4:
             site_var2 = st.radio("Site", ('Draftkings', 'Fanduel'), key='site_var2')
 
             # Process site selection
@@ -311,7 +334,7 @@ with tab1:
                     site_baselines = roo_raw[roo_raw['site'] == 'Fanduel']
                 elif slate_type_var2 == 'Showdown':
                     site_baselines = sd_raw[sd_raw['site'] == 'Fanduel']
-        with col4:
+        with col5:
             slate_split = st.radio("Slate Type", ('Main Slate', 'Secondary'), key='slate_split')
 
             if slate_split == 'Main Slate':
@@ -325,7 +348,7 @@ with tab1:
                 elif slate_type_var2 == 'Showdown':
                     raw_baselines = site_baselines[site_baselines['slate'] == 'Showdown #2']
         
-        with col5:
+        with col6:
             split_var2 = st.radio("Slate Range", ('Full Slate Run', 'Specific Games'), key='split_var2')
             if split_var2 == 'Specific Games':
                 team_var2 = st.multiselect('Select teams for ROO', options=raw_baselines['Team'].unique(), key='team_var2')
@@ -377,7 +400,8 @@ 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, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats()
+            league_var = 'NBA'
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, sd_raw, dk_sd_raw, fd_sd_raw, timestamp = load_overall_stats(league_var)
             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))