Update app.py

app.py

@@ -203,137 +203,141 @@ with tab1:
                             gc.collect() 
                             
 with tab2:
-    col1, col2, col3 = st.columns([1, 8, 1])
-    with col1:
+    with st.container():
         hold_container = st.empty()
-        if st.button("Load/Reset Data", key='reset1'):
-            for key in st.session_state.keys():
-                  del st.session_state[key]
-            display_portfolio = hold_portfolio
-            st.session_state.display_portfolio = display_portfolio
-            st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:9].values, return_counts=True)),
-                                        columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
-            st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
-        if st.button("Trim Lineups", key='trim1'):
-            max_proj = 10000
-            max_own = display_portfolio['Ownership'].iloc[0]
-            x = 0
-            for index, row in display_portfolio.iterrows():
-                if row['Ownership'] > max_own:
-                    display_portfolio.drop(index, inplace=True)
-                elif row['Ownership'] <= max_own:
-                    max_own = row['Ownership']
-            st.session_state.display_portfolio = display_portfolio
-        player_check = st.selectbox('Select player to create comps', options = proj_dataframe['Player'].unique(), key='dk_player')
-        if st.button('Simulate appropriate pivots'):
-            with hold_container:
-                
-                working_roo = proj_dataframe
-                working_roo.rename(columns={"Minutes Proj": "Minutes_Proj"}, inplace = True)
-                own_dict = dict(zip(working_roo.Player, working_roo.Own))
-                min_dict = dict(zip(working_roo.Player, working_roo.Minutes_Proj))
-                team_dict = dict(zip(working_roo.Player, working_roo.Team))
-                total_sims = 1000
-                
-                player_var = working_roo.loc[working_roo['Player'] == player_check]
-                player_var = player_var.reset_index()
-                
-                working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - 300) & (working_roo['Salary'] <= player_var['Salary'][0] + 300)]
-                working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - 3) & (working_roo['Median'] <= player_var['Median'][0] + 3)]
-                
-                flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes_Proj']]
-                flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes_Proj'] * .25)
-                flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes_Proj'] * .25)
-                flex_file['STD'] = (flex_file['Median']/4)
-                flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
-                hold_file = flex_file
-                overall_file = flex_file
-                salary_file = flex_file
-  
-                overall_players = overall_file[['Player']]
-  
-                for x in range(0,total_sims):    
-                    salary_file[x] = salary_file['Salary']
-  
-                salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                salary_file.astype('int').dtypes
-  
-                salary_file = salary_file.div(1000)
-  
-                for x in range(0,total_sims):    
-                    overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
-  
-                overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
-                overall_file.astype('int').dtypes
-  
-                players_only = hold_file[['Player']]
-                raw_lineups_file = players_only
-  
-                for x in range(0,total_sims):
-                    maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
-                    raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
-                    players_only[x] = raw_lineups_file[x].rank(ascending=False)
-  
-                players_only=players_only.drop(['Player'], axis=1)
-                players_only.astype('int').dtypes
-
-                salary_2x_check = (overall_file - (salary_file*4))
-                salary_3x_check = (overall_file - (salary_file*5))
-                salary_4x_check = (overall_file - (salary_file*6))
-                gpp_check = (overall_file - ((salary_file*5)+10))
-
-                players_only['Average_Rank'] = players_only.mean(axis=1)
-                players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
-                players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
-                players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
-                players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
-                players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
-                players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
-
-                players_only['Player'] = hold_file[['Player']]
-
-                final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
-  
-                final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
-                final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
-
-                final_Proj['Own'] = final_Proj['Player'].map(own_dict)
-                final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
-                final_Proj['Team'] = final_Proj['Player'].map(team_dict)
-                final_Proj['Own'] = final_Proj['Own'].astype('float')
-                final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
-                final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
-                final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
-                final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
-                final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
-                final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
-
-                final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
-                final_Proj = final_Proj.sort_values(by='Median', ascending=False)
-                final_Proj['Player_swap'] = player_check
-                st.session_state.final_Proj = final_Proj
-                
-                hold_container = st.empty()
+        col1, col2, col3 = st.columns([3, 3, 3])
+        with col1:
+            if st.button("Load/Reset Data", key='reset1'):
+                for key in st.session_state.keys():
+                      del st.session_state[key]
+                display_portfolio = hold_portfolio
+                st.session_state.display_portfolio = display_portfolio
+                st.session_state.player_freq = pd.DataFrame(np.column_stack(np.unique(st.session_state.display_portfolio.iloc[:,0:9].values, return_counts=True)),
+                                            columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+                st.session_state.player_freq['Freq'] = st.session_state.player_freq['Freq'].astype(int)
+        with col2:
+            if st.button("Trim Lineups", key='trim1'):
+                max_proj = 10000
+                max_own = display_portfolio['Ownership'].iloc[0]
+                x = 0
+                for index, row in display_portfolio.iterrows():
+                    if row['Ownership'] > max_own:
+                        display_portfolio.drop(index, inplace=True)
+                    elif row['Ownership'] <= max_own:
+                        max_own = row['Ownership']
+                st.session_state.display_portfolio = display_portfolio
+        with col3:
+            player_check = st.selectbox('Select player to create comps', options = proj_dataframe['Player'].unique(), key='dk_player')
+            if st.button('Simulate appropriate pivots'):
+                with hold_container:
+                    
+                    working_roo = proj_dataframe
+                    working_roo.rename(columns={"Minutes Proj": "Minutes_Proj"}, inplace = True)
+                    own_dict = dict(zip(working_roo.Player, working_roo.Own))
+                    min_dict = dict(zip(working_roo.Player, working_roo.Minutes_Proj))
+                    team_dict = dict(zip(working_roo.Player, working_roo.Team))
+                    total_sims = 1000
+                    
+                    player_var = working_roo.loc[working_roo['Player'] == player_check]
+                    player_var = player_var.reset_index()
+                    
+                    working_roo = working_roo.loc[(working_roo['Salary'] >= player_var['Salary'][0] - 300) & (working_roo['Salary'] <= player_var['Salary'][0] + 300)]
+                    working_roo = working_roo.loc[(working_roo['Median'] >= player_var['Median'][0] - 3) & (working_roo['Median'] <= player_var['Median'][0] + 3)]
+                    
+                    flex_file = working_roo[['Player', 'Position', 'Salary', 'Median', 'Minutes_Proj']]
+                    flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes_Proj'] * .25)
+                    flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes_Proj'] * .25)
+                    flex_file['STD'] = (flex_file['Median']/4)
+                    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+                    hold_file = flex_file
+                    overall_file = flex_file
+                    salary_file = flex_file
+      
+                    overall_players = overall_file[['Player']]
+      
+                    for x in range(0,total_sims):    
+                        salary_file[x] = salary_file['Salary']
+      
+                    salary_file=salary_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    salary_file.astype('int').dtypes
+      
+                    salary_file = salary_file.div(1000)
+      
+                    for x in range(0,total_sims):    
+                        overall_file[x] = np.random.normal(overall_file['Median'],overall_file['STD'])
+      
+                    overall_file=overall_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+                    overall_file.astype('int').dtypes
+      
+                    players_only = hold_file[['Player']]
+                    raw_lineups_file = players_only
+      
+                    for x in range(0,total_sims):
+                        maps_dict = {'proj_map':dict(zip(hold_file.Player,hold_file[x]))}
+                        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+                        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+      
+                    players_only=players_only.drop(['Player'], axis=1)
+                    players_only.astype('int').dtypes
+    
+                    salary_2x_check = (overall_file - (salary_file*4))
+                    salary_3x_check = (overall_file - (salary_file*5))
+                    salary_4x_check = (overall_file - (salary_file*6))
+                    gpp_check = (overall_file - ((salary_file*5)+10))
+    
+                    players_only['Average_Rank'] = players_only.mean(axis=1)
+                    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+                    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+                    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+                    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+                    players_only['3x%'] = salary_2x_check[salary_2x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['4x%'] = salary_3x_check[salary_3x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['5x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+                    players_only['GPP%'] = salary_4x_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+    
+                    players_only['Player'] = hold_file[['Player']]
+    
+                    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
+      
+                    final_Proj = pd.merge(hold_file, final_outcomes, on="Player")
+                    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%']]
+    
+                    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+                    final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+                    final_Proj['Team'] = final_Proj['Player'].map(team_dict)
+                    final_Proj['Own'] = final_Proj['Own'].astype('float')
+                    final_Proj['Projection Rank'] = final_Proj.Top_finish.rank(pct = True)
+                    final_Proj['Own Rank'] = final_Proj.Own.rank(pct = True)
+                    final_Proj['LevX'] = (final_Proj['Projection Rank'] - final_Proj['Own Rank']) * 100
+                    final_Proj['ValX'] = ((final_Proj[['4x%', '5x%']].mean(axis=1))*100) + final_Proj['LevX']
+                    final_Proj['ValX'] = np.where(final_Proj['ValX'] > 100, 100, final_Proj['ValX'])
+                    final_Proj['ValX'] = np.where(final_Proj['ValX'] < 0, 0, final_Proj['ValX'])
+    
+                    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '3x%', '4x%', '5x%', 'GPP%', 'Own', 'LevX', 'ValX']]
+                    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+                    final_Proj['Player_swap'] = player_check
+                    st.session_state.final_Proj = final_Proj
                     
-    with col2:
+                    hold_container = st.empty()
+    with st.container():
         proj_container = st.empty()
         display_container = st.empty()
         display_dl_container = st.empty()
-        with proj_container:
-             proj_container = st.empty()
-             if 'display_portfolio' in st.session_state:
-                 st.dataframe(st.session_state.display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-        
-        with display_container:
-             display_container = st.empty()
-             if 'final_Proj' in st.session_state:
-                 st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
-    with col3:
-        freq_container = st.empty()
-        with freq_container:
-             freq_container = st.empty()
-             if 'player_freq' in st.session_state:
-                 st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        col1, col2 = st.columns([7, 3])         
+        with col1:
+            with proj_container:
+                 proj_container = st.empty()
+                 if 'display_portfolio' in st.session_state:
+                     st.dataframe(st.session_state.display_portfolio.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            
+            with display_container:
+                 display_container = st.empty()
+                 if 'final_Proj' in st.session_state:
+                     st.dataframe(st.session_state.final_Proj.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+        with col2:
+            freq_container = st.empty()
+            with freq_container:
+                 freq_container = st.empty()
+                 if 'player_freq' in st.session_state:
+                     st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)