Update app.py

app.py

@@ -837,6 +837,26 @@ with tab1:
 with tab2:
     col1, col2 = st.columns([1, 7])
     with col1:
+        if 'Sim_Winner_Display' not in st.session_state:
+            st.session_state.Sim_Winner_Display = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
+        if 'Sim_Winner_Frame' not in st.session_state:
+            st.session_state.Sim_Winner_Frame = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
+        if 'Sim_Winner_Export' not in st.session_state:
+            st.session_state.Sim_Winner_Export = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
+        if 'player_freq' not in st.session_state: 
+            st.session_state.player_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'qb_freq' not in st.session_state: 
+            st.session_state.qb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'rb_freq' not in st.session_state: 
+            st.session_state.rb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'wr_freq' not in st.session_state: 
+            st.session_state.wr_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'te_freq' not in st.session_state: 
+            st.session_state.te_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'flex_freq' not in st.session_state: 
+            st.session_state.flex_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+        if 'dst_freq' not in st.session_state: 
+            st.session_state.dst_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
         st.info(t_stamp)
         if st.button("Load/Reset Data", key='reset1'):
               st.cache_data.clear()
@@ -889,28 +909,29 @@ with tab2:
             sharp_split = .75
             Strength_var = .01
             scaling_var = 15
-        if 'Sim_Winner_Display' not in st.session_state:
-            st.session_state.Sim_Winner_Display = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-        if 'Sim_Winner_Frame' not in st.session_state:
-            st.session_state.Sim_Winner_Frame = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-        if 'Sim_Winner_Export' not in st.session_state:
-            st.session_state.Sim_Winner_Export = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
-        if 'player_freq' not in st.session_state: 
-            st.session_state.player_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'qb_freq' not in st.session_state: 
-            st.session_state.qb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'rb_freq' not in st.session_state: 
-            st.session_state.rb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'wr_freq' not in st.session_state: 
-            st.session_state.wr_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'te_freq' not in st.session_state: 
-            st.session_state.te_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'flex_freq' not in st.session_state: 
-            st.session_state.flex_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
-        if 'dst_freq' not in st.session_state: 
-            st.session_state.dst_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+
     with col2:
         with st.container():
+            if 'Sim_Winner_Display' not in st.session_state:
+                st.session_state.Sim_Winner_Display = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
+            if 'Sim_Winner_Frame' not in st.session_state:
+                st.session_state.Sim_Winner_Frame = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
+            if 'Sim_Winner_Export' not in st.session_state:
+                st.session_state.Sim_Winner_Export = pd.DataFrame(columns=['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'User/Field', 'Salary', 'Projection', 'Own', 'Fantasy', 'GPP_Proj'])
+            if 'player_freq' not in st.session_state: 
+                st.session_state.player_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+            if 'qb_freq' not in st.session_state: 
+                st.session_state.qb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+            if 'rb_freq' not in st.session_state: 
+                st.session_state.rb_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+            if 'wr_freq' not in st.session_state: 
+                st.session_state.wr_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+            if 'te_freq' not in st.session_state: 
+                st.session_state.te_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+            if 'flex_freq' not in st.session_state: 
+                st.session_state.flex_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
+            if 'dst_freq' not in st.session_state: 
+                st.session_state.dst_freq = pd.DataFrame(columns=['Player', 'Position', 'Team', 'Salary', 'Proj Own', 'Exposure', 'Edge'])
             if st.button("Simulate Contest"):
                 try:
                     del dst_freq
@@ -1357,81 +1378,90 @@ with tab2:
                 
         with st.container():
             simulate_container = st.empty()
-            player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
-            if player_split_var2 == 'Specific Players':
-                      find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
-            elif player_split_var2 == 'Full Players':
-                      find_var2 = st.session_state.player_freq.Player.values.tolist()
-
-            if player_split_var2 == 'Specific Players':
-                      st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
-            if player_split_var2 == 'Full Players':
-                      st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
-            st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
-        
-            st.download_button(
-                label="Export Tables",
-                data=convert_df_to_csv(st.session_state.Sim_Winner_Export),
-                file_name='NFL_consim_export.csv',
-                mime='text/csv',
-            )
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+    
+                if player_split_var2 == 'Specific Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(copy=False),  find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').background_gradient(cmap='RdYlGn_r', subset=['Own']).format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
+                st.download_button(
+                    label="Export Tables",
+                    data=convert_df_to_csv(st.session_state.Sim_Winner_Export),
+                    file_name='NFL_consim_export.csv',
+                    mime='text/csv',
+                )
         
         with st.container():
             freq_container = st.empty()
             tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(['Overall Exposures', 'QB Exposures', 'RB Exposures', 'WR Exposures', 'TE Exposures', 'FLEX Exposures', 'DST Exposures'])
             with tab1:
-                st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.player_freq),
-                    file_name='player_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'player_freq' in st.session_state:
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.player_freq),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                    )
             with tab2:
-                st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.qb_freq),
-                    file_name='qb_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'qb_freq' in st.session_state:
+                    st.dataframe(st.session_state.qb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.qb_freq),
+                        file_name='qb_freq_export.csv',
+                        mime='text/csv',
+                    )
             with tab3:
-                st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.rb_freq),
-                    file_name='rb_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'rb_freq' in st.session_state:
+                    st.dataframe(st.session_state.rb_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.rb_freq),
+                        file_name='rb_freq_export.csv',
+                        mime='text/csv',
+                    )
             with tab4:
-                st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.wr_freq),
-                    file_name='wr_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'wr_freq' in st.session_state:
+                    st.dataframe(st.session_state.wr_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.wr_freq),
+                        file_name='wr_freq_export.csv',
+                        mime='text/csv',
+                    )
             with tab5:
-                st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.te_freq),
-                    file_name='te_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'te_freq' in st.session_state:
+                    st.dataframe(st.session_state.te_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.te_freq),
+                        file_name='te_freq_export.csv',
+                        mime='text/csv',
+                    )
             with tab6:
-                st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.flex_freq),
-                    file_name='flex_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'flex_freq' in st.session_state:
+                    st.dataframe(st.session_state.flex_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.flex_freq),
+                        file_name='flex_freq_export.csv',
+                        mime='text/csv',
+                    )
             with tab7:
-                st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
-                st.download_button(
-                    label="Export Exposures",
-                    data=convert_df_to_csv(st.session_state.dst_freq),
-                    file_name='dst_freq_export.csv',
-                    mime='text/csv',
-                )
+                if 'dst_freq' in st.session_state:
+                    st.dataframe(st.session_state.dst_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=convert_df_to_csv(st.session_state.dst_freq),
+                        file_name='dst_freq_export.csv',
+                        mime='text/csv',
+                    )