Update data loading in app.py to switch from NBA to WNBA for overall statistics, ensuring accurate data retrieval for player salary and ID mappings during the export process.

app.py

@@ -368,7 +368,7 @@ def convert_pm_df(array):
             array[col] = array[col].apply(normalize_special_characters)
     return array.to_csv(index=False).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('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('WNBA')
 dk_salary_dict = dict(zip(dk_raw.Player, dk_raw.Salary))
 fd_salary_dict = dict(zip(fd_raw.Player, fd_raw.Salary))
 dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
@@ -401,7 +401,7 @@ with reset_col:
     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('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('WNBA')
             dk_salary_dict = dict(zip(dk_raw.Player, dk_raw.Salary))
             fd_salary_dict = dict(zip(fd_raw.Player, fd_raw.Salary))
             dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))
@@ -532,7 +532,7 @@ 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('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('WNBA')
             dk_salary_dict = dict(zip(dk_raw.Player, dk_raw.Salary))
             fd_salary_dict = dict(zip(fd_raw.Player, fd_raw.Salary))
             dk_id_dict = dict(zip(dk_raw.Player, dk_raw.player_ID))