Refactor Sim_Winner_Frame processing in Streamlit app to drop 'unique_id' and 'win_count' columns, and enhance frequency calculations for player data, improving data handling and clarity.

src/streamlit_app.py

@@ -624,7 +624,7 @@ if selected_tab == "Showdown Contest Sims":
             
             # Sorting
             st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+            st.session_state.Sim_Winner_Frame = st.session_state.Sim_Winner_Frame.drop(columns=['unique_id', 'win_count'])
             
             # Data Copying
             st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
@@ -663,58 +663,59 @@ if selected_tab == "Showdown Contest Sims":
             
             # Sorting
             st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
-            st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+            st.session_state.Sim_Winner_Frame = st.session_state.Sim_Winner_Frame.drop(columns=['unique_id', 'win_count'])
             
             # Data Copying
             st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+            st.session_state.Sim_Winner_Export.iloc[:, 0:6] = st.session_state.Sim_Winner_Export.iloc[:, 0:6].apply(lambda x: x.map(export_id_dict))
             
             # Data Copying
             st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
             freq_copy = st.session_state.Sim_Winner_Display
             
-            if sim_site_var2 == 'Draftkings':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            elif sim_site_var2 == 'Fanduel':
-                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            freq_working['Freq'] = freq_working['Freq'].astype(int)
-            freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
-            freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
-            freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
-            freq_working['Exposure'] = freq_working['Freq']/(1000)
-            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
-            freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
-            st.session_state.player_freq = freq_working.copy()
+        if sim_site_var2 == 'Draftkings':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        elif sim_site_var2 == 'Fanduel':
+            freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:5].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        freq_working['Freq'] = freq_working['Freq'].astype(int)
+        freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
+        freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map']) / 1.5
+        freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
+        freq_working['Exposure'] = freq_working['Freq']/(1000)
+        freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
+        freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
+        st.session_state.player_freq = freq_working.copy()
 
-            cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
-            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
-            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
-            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
-            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
-            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
-            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
-            st.session_state.sp_freq = cpt_working.copy()
-            
-            flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            cpt_own_div = 600
-            flex_working['Freq'] = flex_working['Freq'].astype(int)
-            flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
-            flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
-            flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100)
-            flex_working['Exposure'] = flex_working['Freq']/(1000)
-            flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
-            flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])
-            st.session_state.flex_freq = flex_working.copy()
+        cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        cpt_working['Freq'] = cpt_working['Freq'].astype(int)
+        cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
+        cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+        cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['cpt_Own_map']) / 100
+        cpt_working['Exposure'] = cpt_working['Freq']/(1000)
+        cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
+        cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
+        st.session_state.sp_freq = cpt_working.copy()
+        
+        flex_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:6].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        cpt_own_div = 600
+        flex_working['Freq'] = flex_working['Freq'].astype(int)
+        flex_working['Position'] = flex_working['Player'].map(maps_dict['Pos_map'])
+        flex_working['Salary'] = flex_working['Player'].map(maps_dict['Salary_map']) / 1.5
+        flex_working['Proj Own'] = (flex_working['Player'].map(maps_dict['Own_map']) / 100) - (flex_working['Player'].map(maps_dict['cpt_Own_map']) / 100)
+        flex_working['Exposure'] = flex_working['Freq']/(1000)
+        flex_working['Edge'] = flex_working['Exposure'] - flex_working['Proj Own']
+        flex_working['Team'] = flex_working['Player'].map(maps_dict['Team_map'])
+        st.session_state.flex_freq = flex_working.copy()
 
-            team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)),
-                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            team_working['Freq'] = team_working['Freq'].astype(int)
-            team_working['Exposure'] = team_working['Freq']/(1000)
-            st.session_state.team_freq = team_working.copy()
+        team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,8:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+        team_working['Freq'] = team_working['Freq'].astype(int)
+        team_working['Exposure'] = team_working['Freq']/(1000)
+        st.session_state.team_freq = team_working.copy()
             
         with st.container():
             if st.button("Reset Sim", key='reset_sim'):